rvl_cdip_n_mp.py

# Copyright 2023 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.
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#     http://www.apache.org/licenses/LICENSE-2.0
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"""RVL-CDIP-N_mp (Ryerson Vision Lab Complex Document Information Processing) -New -Multipage dataset"""


import os
import datasets
from pathlib import Path
from tqdm import tqdm
import pdf2image

datasets.logging.set_verbosity_info()
logger = datasets.logging.get_logger(__name__)

_MODE = "binary"

_CITATION = """\
@inproceedings{larson2022evaluating,
	title={Evaluating Out-of-Distribution Performance on Document Image Classifiers},
	author={Larson, Stefan and Lim, Gordon and Ai, Yutong and Kuang, David and Leach, Kevin},
	booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
	year={2022}
}
    
@inproceedings{bdpc,
    title = {Beyond Document Page Classification},
    author = {Anonymous},
    booktitle = {Under Review},
    year = {2023}
}
"""

_DESCRIPTION = """\
The RVL-CDIP-N (Ryerson Vision Lab Complex Document Information Processing) dataset consists of newly gathered documents in 16 classes 
There are 991 documents for testing purposes. There were 10 documents from the original dataset that could not be retrieved based on the metadata or were out-of-scope (language). 
"""


_HOMEPAGE = "https://www.cs.cmu.edu/~aharley/rvl-cdip/"
_LICENSE = "https://www.industrydocuments.ucsf.edu/help/copyright/"


SOURCE = "jordyvl/rvl_cdip_mp"
_URL = f"https://huggingface.co/datasets/{SOURCE}/resolve/main/data.gz"
_BACKOFF_folder = "/mnt/lerna/data/RVL-CDIP-NO/RVL-CDIP-N_pdf/data"

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


def batched_conversion(pdf_file):
    info = pdf2image.pdfinfo_from_path(pdf_file, userpw=None, poppler_path=None)
    maxPages = info["Pages"]

    logger.info(f"{pdf_file} has {str(maxPages)} pages")

    images = []

    for page in range(1, maxPages + 1, 10):
        images.extend(
            pdf2image.convert_from_path(pdf_file, dpi=200, first_page=page, last_page=min(page + 10 - 1, maxPages))
        )
    return images


def open_pdf_binary(pdf_file):
    with open(pdf_file, "rb") as f:
        return f.read()


class RvlCdipNMp(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    DEFAULT_CONFIG_NAME = "default"

    def _info(self):
        if isinstance(self.config.data_dir, str):
            folder = self.config.data_dir  # contains the folder structure at someone local disk
        else:
            if not os.path.exists(_BACKOFF_folder):
                raise ValueError("No data folder found. Please set data_dir or data_files.")
            folder = _BACKOFF_folder  # my local path, others should set data_dir or data_files
        self.config.data_dir = folder

        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "file": datasets.Value("binary"),
                    "labels": datasets.features.ClassLabel(names=_CLASSES),
                }
            ),
            homepage=_HOMEPAGE,
            citation=_CITATION,
            license=_LICENSE,
            task_templates=None,
        )

    def _split_generators(self, dl_manager):
        if self.config.data_dir.endswith(".tar.gz"):
            archive_path = dl_manager.download(self.config.data_dir)
            data_files = dl_manager.iter_archive(archive_path)
        else:
            data_files = self.config.data_dir

        return [datasets.SplitGenerator(name="test", gen_kwargs={"archive_path": data_files})]

    def _generate_examples(self, archive_path):
        labels = self.info.features["labels"]

        extensions = {".pdf", ".PDF"}

        for i, path in tqdm(enumerate(Path(archive_path).glob("**/*")), desc=f"{archive_path}"):
            if path.suffix in extensions:
                try:
                    if _MODE == "binary":
                        images = open_pdf_binary(path)
                        # batched_conversion(path)
                    else:
                        images = path

                    a = dict(
                        id=path.name,
                        file=images,
                        labels=labels.encode_example(path.parent.name.lower()),
                    )
                    yield path.name, a

                except Exception as e:
                    logger.warning(f"{e} failed to parse {i}")