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rvl_cdip_mp / rvl_cdip_mp.py

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	# 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.
	# 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.

	"""RVL-CDIP_mp (Ryerson Vision Lab Complex Document Information Processing) -Extended -Multipage dataset"""


	import os
	import datasets
	from pathlib import Path
	from typing import List
	from tqdm import tqdm

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

	MODE = "binary"

	_CITATION = """

	@inproceedings{bdpc,
	title = {Beyond Document Page Classification},
	author = {Anonymous},
	booktitle = {Under Review},
	year = {2023}
	}
	"""

	_DESCRIPTION = """\
	The RVL-CDIP (Ryerson Vision Lab Complex Document Information Processing) dataset consists of originally retrieved documents in 16 classes.
	There were +-500 documents from the original dataset that could not be retrieved based on the metadata or were corrupt in IDL.
	"""


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


	SOURCE = "bdpc/rvl_cdip_mp"

	_BACKOFF_folder = "/mnt/lerna/data/RVL-CDIP_pdf"

	_CLASSES = [
	"letter",
	"form",
	"email",
	"handwritten",
	"advertisement",
	"scientific_report",
	"scientific_publication",
	"specification",
	"file_folder",
	"news_article",
	"budget",
	"invoice",
	"presentation",
	"questionnaire",
	"resume",
	"memo",
	]


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


	class RvlCdipMp(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	datasets.BuilderConfig(
	name="default",
	version=datasets.Version("1.0.0", ""),
	description="",
	)
	]

	def __init__(self, args, examples_per_class=None, *kwargs):
	super().__init__(args, *kwargs)
	# examples per class to stop generating
	self.examples_per_class = examples_per_class

	@property
	def manual_download_instructions(self):
	return (
	"To use RVL-CDIP_multi you have to download it manually. Please extract all files in one folder and load the dataset with: "
	"`datasets.load_dataset('bdpc/rvl_cdip_mp', data_dir='path/to/folder/folder_name')`"
	)

	def _info(self):
	# DEFAULT_WRITER_BATCH_SIZE

	folder = None
	if isinstance(self.config.data_files, str):
	folder = self.config.data_files # needs to be extracted cuz zip/tar
	else:
	if isinstance(self.config.data_dir, str):
	folder = self.config.data_dir # contains the folder structure at someone local disk
	else:
	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(
	{
	"file": datasets.Value("binary"), # datasets.Sequence(datasets.Image()),
	"labels": datasets.features.ClassLabel(names=_CLASSES),
	}
	),
	task_templates=None,
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
	if os.path.isdir(self.config.data_dir):
	data_files = {
	labelset: os.path.join(self.config.data_dir, labelset)
	for labelset in sorted(os.listdir(self.config.data_dir), reverse=True)
	if not "csv" in labelset
	}

	elif self.config.data_dir.endswith(".tar.gz"):
	archive_path = dl_manager.download(self.config.data_dir)
	data_files = dl_manager.iter_archive(archive_path)
	raise NotImplementedError()
	elif self.config.data_dir.endswith(".zip"):
	archive_path = dl_manager.download_and_extract(self.config.data_dir)
	data_files = dl_manager.iter_archive(archive_path)
	raise NotImplementedError()

	splits = []
	for split_name, folder in data_files.items():
	print(folder)
	splits.append(datasets.SplitGenerator(name=split_name, gen_kwargs={"archive_path": folder}))
	return splits

	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:
	images = open_pdf_binary(path)
	yield path.name, {
	"file": images,
	"labels": labels.encode_example(path.parent.name.lower()),
	}
	except Exception as e:
	logger.warning(f"{e} failed to parse {i}")