| # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| """Animals with Attributes v2 (AwA2)""" | |
| import csv | |
| import json | |
| import os | |
| import datasets | |
| _CITATION = """\ | |
| @article{xian2018zero, | |
| title={Zero-shot learning—a comprehensive evaluation of the good, the bad and the ugly}, | |
| author={Xian, Yongqin and Lampert, Christoph H and Schiele, Bernt and Akata, Zeynep}, | |
| journal={IEEE transactions on pattern analysis and machine intelligence}, | |
| volume={41}, | |
| number={9}, | |
| pages={2251--2265}, | |
| year={2018}, | |
| publisher={IEEE} | |
| } | |
| """ | |
| # TODO: Add description of the dataset here | |
| # You can copy an official description | |
| _DESCRIPTION = """\ | |
| **Homepage:** https://cvml.ista.ac.at/AwA2/ | |
| **IMPORTANT NOTES** | |
| - This HF dataset loads the instances with class-level annotations. | |
| - Images and License can be downloaded from: https://cvml.ista.ac.at/AwA2/AwA2-data.zip | |
| """ | |
| # TODO: Add a link to an official homepage for the dataset here | |
| _HOMEPAGE = "https://cvml.ista.ac.at/AwA2/" | |
| # TODO: Add the licence for the dataset here if you can find it | |
| _LICENSE = "" | |
| # TODO: Add link to the official dataset URLs here | |
| # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
| # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
| # _URLS = { | |
| # # "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", | |
| # # "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", | |
| # } | |
| _URLS = { | |
| "data": "https://cvml.ista.ac.at/AwA2/AwA2-data.zip", # including images | |
| # "annotation": "https://cvml.ista.ac.at/AwA2/AwA2-base.zip", | |
| # "features": "http://cvml.ist.ac.at/AwA2/AwA2-features.zip", | |
| } | |
| def _load_AwA2_dataset(datadir): | |
| image_dir = os.path.join(datadir, "JPEGImages") | |
| classes_path = os.path.join(datadir, "classes.txt") | |
| predicates_path = os.path.join(datadir, "predicates.txt") | |
| annotation_binary = os.path.join(datadir, "predicate-matrix-binary.txt") | |
| annotation_continuous = os.path.join(datadir, "predicate-matrix-continuous.txt") | |
| # load classes | |
| classes = [] | |
| with open(classes_path, "r") as f: | |
| for line in f: | |
| classes.append(line.split("\t")[1].strip()) | |
| # load predicates | |
| predicates = [] | |
| with open(predicates_path, "r") as f: | |
| for line in f: | |
| predicates.append(line.split("\t")[1].strip()) | |
| # class to annotation binary | |
| annotation_binary_list = [] | |
| with open(annotation_binary, "r") as f: | |
| for line in f: | |
| ann = [int(x) for x in line.strip().split(" ")] | |
| assert len(ann) == len(predicates) | |
| annotation_binary_list.append(ann) | |
| class_to_annotation_binary = dict(zip(classes, annotation_binary_list)) | |
| # class to annotation continuous | |
| annotation_continuous_list = [] | |
| with open(annotation_continuous, "r") as f: | |
| for line in f: | |
| ann = [float(x) for x in line.strip().split(" ") if x != ""] | |
| assert len(ann) == len(predicates) | |
| annotation_continuous_list.append(ann) | |
| class_to_annotation_continuous = dict(zip(classes, annotation_continuous_list)) | |
| print("classes:", len(classes), classes) | |
| print("attribute types:", len(predicates), predicates) | |
| data = [] | |
| # list all images in image dir | |
| for class_type in os.listdir(image_dir): | |
| image_class_dir = os.path.join(image_dir, class_type) | |
| for img_name in os.listdir(image_class_dir): | |
| data.append( | |
| { | |
| "image_id": img_name, | |
| "image_path": os.path.join(image_class_dir, img_name), | |
| "class": class_type, | |
| "attributes_binary": class_to_annotation_binary[class_type], | |
| "attributes_continuous": class_to_annotation_continuous[class_type], | |
| "attribute_types": predicates | |
| } | |
| ) | |
| return data | |
| # TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case | |
| class AwA2(datasets.GeneratorBasedBuilder): | |
| """TODO: Short description of my dataset.""" | |
| VERSION = datasets.Version("1.0.0") | |
| # This is an example of a dataset with multiple configurations. | |
| # If you don't want/need to define several sub-sets in your dataset, | |
| # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. | |
| # If you need to make complex sub-parts in the datasets with configurable options | |
| # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig | |
| # BUILDER_CONFIG_CLASS = MyBuilderConfig | |
| # You will be able to load one or the other configurations in the following list with | |
| # data = datasets.load_dataset('my_dataset', 'first_domain') | |
| # data = datasets.load_dataset('my_dataset', 'second_domain') | |
| # BUILDER_CONFIGS = [ | |
| # datasets.BuilderConfig(name="first_domain", version=VERSION, description="This part of my dataset covers a first domain"), | |
| # datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"), | |
| # ] | |
| # DEFAULT_CONFIG_NAME = "first_domain" # It's not mandatory to have a default configuration. Just use one if it make sense. | |
| def _info(self): | |
| # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
| # if self.config.name == "first_domain": # This is the name of the configuration selected in BUILDER_CONFIGS above | |
| # features = datasets.Features( | |
| # { | |
| # "sentence": datasets.Value("string"), | |
| # "option1": datasets.Value("string"), | |
| # "answer": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| # else: # This is an example to show how to have different features for "first_domain" and "second_domain" | |
| # features = datasets.Features( | |
| # { | |
| # "sentence": datasets.Value("string"), | |
| # "option2": datasets.Value("string"), | |
| # "second_domain_answer": datasets.Value("string") | |
| # # These are the features of your dataset like images, labels ... | |
| # } | |
| # ) | |
| features = datasets.Features( | |
| { | |
| "image_id": datasets.Value("string"), | |
| "image_path": datasets.Value("string"), | |
| "class": datasets.Value("string"), | |
| "attributes_binary": datasets.features.Sequence(datasets.Value("int32")), | |
| "attributes_continuous": datasets.features.Sequence(datasets.Value("float32")), | |
| "attribute_types": datasets.features.Sequence(datasets.Value("string")), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| # This is the description that will appear on the datasets page. | |
| description=_DESCRIPTION, | |
| # This defines the different columns of the dataset and their types | |
| features=features, # Here we define them above because they are different between the two configurations | |
| # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and | |
| # specify them. They'll be used if as_supervised=True in builder.as_dataset. | |
| # supervised_keys=("sentence", "label"), | |
| # Homepage of the dataset for documentation | |
| homepage=_HOMEPAGE, | |
| # License for the dataset if available | |
| license=_LICENSE, | |
| # Citation for the dataset | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration | |
| # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name | |
| # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS | |
| # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. | |
| # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive | |
| downloaded_files = dl_manager.download_and_extract(_URLS) | |
| # downloaded_files = { | |
| # "data": "/shared/nas/data/m1/shared-resource/vision-language/data/raw/AwA2/Animals_with_Attributes2", | |
| # } | |
| print("downloaded_files: ", downloaded_files) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| # These kwargs will be passed to _generate_examples | |
| gen_kwargs={ | |
| "filepath": downloaded_files["data"], | |
| "split": "train", | |
| }, | |
| ) | |
| ] | |
| # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
| def _generate_examples(self, filepath, split): | |
| # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. | |
| # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. | |
| data = _load_AwA2_dataset(filepath) | |
| for key, row in enumerate(data): | |
| yield key, { | |
| "image_id": row["image_id"], | |
| "image_path": row["image_path"], | |
| "class": row["class"], | |
| "attributes_binary": row["attributes_binary"], | |
| "attributes_continuous": row["attributes_continuous"], | |
| "attribute_types": row["attribute_types"], | |
| } | |