new_dataset.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""

import json
import os

import datasets
import numpy as np

for _ in range(10):
    print("LOADING SCRIPT")

# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""

# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
This new dataset is designed to solve this great NLP task and is crafted with a lot of care.
"""

# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = ""

# 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 = {
    "8x8": [
        "https://huggingface.co/datasets/Prisma-Multimodal/segmented-imagenet1k-subset/resolve/main/images.tar.gz?download=true",
        "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/annotations/{split}_annotations/mask.tar.gz?download=true",
        "https://huggingface.co/datasets/manuel-delverme/test_repo/resolve/main/{split}.jsonl?download=true"
    ]
}


# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class PatchyImagenet(datasets.GeneratorBasedBuilder):
    """TODO: Short description of my dataset."""

    VERSION = datasets.Version("0.0.1")

    # 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.

    BUILDER_CONFIGS = [
        # datasets.BuilderConfig(name="1x1", version=VERSION, description="Patchy Imagenet with 1x1 resolution (this is the original resolution)"),
        datasets.BuilderConfig(name="8x8", version=VERSION, description="Patchy Imagenet with 8x8 resolution"),
        # datasets.BuilderConfig(name="16x16", version=VERSION, description="Patchy Imagenet with 16x16 resolution"),
        # datasets.BuilderConfig(name="32x32", version=VERSION, description="Patchy Imagenet with 32x32 resolution"),
        # datasets.BuilderConfig(name="64x64", version=VERSION, description="Patchy Imagenet with 64x64 resolution"),
    ]
    DEFAULT_CONFIG_NAME = "8x8"

    def _info(self):
        features = datasets.Features(
            {
                "image": datasets.Image(),
                "patches": datasets.Features(
                    {
                        # "categories": datasets.Sequence(datasets.ClassLabel(names=_IMAGENET_CLASSES)),
                        "categories": datasets.Value("string"),
                        "scores": datasets.Sequence(datasets.Value("float32")),
                        # "mask": datasets.Array2D(shape=(None, None), dtype="bool"),
                        "mask": datasets.Sequence(datasets.Image()),
                    }
                ),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            # This defines the different columns of the dataset and their types
            features=features,
            # 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
        url_templates = _URLS[self.config.name]

        split_kwargs = {}
        for split in ["train", "test", "val"]:
            urls = [url.format(split=split) for url in url_templates]
            image_dir, mask_dir, metadata_file = dl_manager.download_and_extract(urls)
            split_kwargs[split] = {
                "meta_path": metadata_file,
                "image_dir": image_dir, "mask_dir": mask_dir,
                "split": split
            }

        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs=split_kwargs["train"]),
            datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs=split_kwargs["val"]),
            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs=split_kwargs["test"]),
        ]

    # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
    def _generate_examples(self, meta_path, image_dir, mask_dir, 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.
        # breakpoint()
        with open(meta_path, encoding="utf-8") as f:
            for key, row in enumerate(f):
                data = json.loads(row)
                image_path = os.path.join(image_dir, data["file_name"])
                sample_name, _extension = os.path.splitext(data["file_name"])
                mask_file = os.path.join(mask_dir, "masks", sample_name + ".npy")
                mask = np.load(mask_file).astype(np.uint8)
                yield key, {
                    "image_path": image_path,
                    "patches": {
                        "categories": data["patches"]["categories"],
                        "scores": data["patches"]["scores"],
                        "mask": list(mask),
                    }
                }