rvl_cdip.py

# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
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"""RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset"""


import os

import datasets
from datasets.tasks import ImageClassification


_CITATION = """\
@inproceedings{harley2015icdar,
    title = {Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval},
    author = {Adam W Harley and Alex Ufkes and Konstantinos G Derpanis},
    booktitle = {International Conference on Document Analysis and Recognition ({ICDAR})}},
    year = {2015}
}
"""


_DESCRIPTION = """\
The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of 400,000 grayscale images in 16 classes, with 25,000 images per class. There are 320,000 training images, 40,000 validation images, and 40,000 test images.
"""


_HOMEPAGE = "https://www.cs.cmu.edu/~aharley/rvl-cdip/"


_LICENSE = "https://www.industrydocuments.ucsf.edu/help/copyright/"


_URLS = {
    "rvl-cdip": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/rvl-cdip.tar.gz",
}

_METADATA_URLS = {
    "train": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/train.txt",
    "test": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/test.txt",
    "val": "https://huggingface.co/datasets/rvl_cdip/resolve/main/data/val.txt",
}

_CLASSES = [
    "letter",
    "form",
    "email",
    "handwritten",
    "advertisement",
    "scientific report",
    "scientific publication",
    "specification",
    "file folder",
    "news article",
    "budget",
    "invoice",
    "presentation",
    "questionnaire",
    "resume",
    "memo",
]

_IMAGES_DIR = "images/"


class RvlCdip(datasets.GeneratorBasedBuilder):
    """Ryerson Vision Lab Complex Document Information Processing dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "image": datasets.Image(),
                    "label": datasets.ClassLabel(names=_CLASSES),
                }
            ),
            supervised_keys=("image", "label"),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            task_templates=[ImageClassification(image_column="image", label_column="label")],
        )

    def _split_generators(self, dl_manager):
        archive_path = dl_manager.download(_URLS["rvl-cdip"])
        labels_path = dl_manager.download(_METADATA_URLS)

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    "labels_filepath": labels_path["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    "labels_filepath": labels_path["test"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    "labels_filepath": labels_path["val"],
                },
            ),
        ]

    @staticmethod
    def _get_image_to_class_map(data):
        image_to_class_id = {}
        for item in data:
            image_path, class_id = item.split(" ")
            image_path = os.path.join(_IMAGES_DIR, image_path)
            image_to_class_id[image_path] = int(class_id)

        return image_to_class_id

    def _generate_examples(self, archive_iterator, labels_filepath):

        with open(labels_filepath, encoding="utf-8") as f:
            data = f.read().splitlines()

        image_to_class_id = self._get_image_to_class_map(data)

        for file_path, file_obj in archive_iterator:
            if file_path.startswith(_IMAGES_DIR):
                if file_path in image_to_class_id:
                    class_id = image_to_class_id[file_path]
                    label = _CLASSES[class_id]
                    yield file_path, {"image": {"path": file_path, "bytes": file_obj.read()}, "label": label}