# coding=utf-8 # 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. """DUDE dataset loader""" import copy import json import os from typing import List, Literal import datasets import pdf2image from tqdm import tqdm _CITATION = """ @inproceedings{dude2023icdar, title={ICDAR 2023 Challenge on Document UnderstanDing of Everything (DUDE)}, author={Van Landeghem, Jordy et . al.}, booktitle={Proceedings of the ICDAR}, year={2023} } """ _DESCRIPTION = """\ DUDE requires models to reason and understand about document layouts in multi-page images/PDFs to answer questions about them. Specifically, models need to incorporate a new modality of layout present in the images/PDFs and reason over it to answer DUDE questions. """ # DUDE Contains X questions and Y and ... _HOMEPAGE = "https://rrc.cvc.uab.es/?ch=23" _LICENSE = "CC BY 4.0" _SPLITS = ["train", "val", "test"] _URLS = { "binaries": "https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_train-val-test_binaries.tar.gz", "annotations": "https://zenodo.org/record/7763635/files/2023-03-23_DUDE_gt_test_PUBLIC.json?download=1" # "blind": "/home/jordy/code/DUchallenge/DUDEeval/gt/2023-03-07_DUDE_gt_release-candidate_NOTSHARABLE.json", } SKIP_DOC_IDS = [ "nan", "ef03364aa27a0987c9870472e312aceb", "5c5a5880e6a73b4be2315d506ab0b15b", ] def parse_bbox(bbox): if bbox in [[], [[]]]: return None answers_page_bounding_boxes = [] if isinstance(bbox[0], list): bbox = bbox[0] keys = ["left", "top", "width", "height", "page"] for page_bb in bbox: if len(page_bb) == 0: continue page_bb = {key: page_bb[key] for key in keys} answers_page_bounding_boxes.append(page_bb) return answers_page_bounding_boxes def batched_conversion(pdf_file): info = pdf2image.pdfinfo_from_path(pdf_file, userpw=None, poppler_path=None) maxPages = info["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 DUDEConfig(datasets.BuilderConfig): """BuilderConfig for DUDE.""" def __init__( self, binary_mode: bool = False, ocr_engine: Literal["Azure", "Amazon", "Tesseract"] = "Amazon", format: Literal["original", "due"] = "original", **kwargs, ): """BuilderConfig for DUDE. Args: binary_mode: `boolean`, load binary PDFs/OCR or pass along paths on local file system **kwargs: keyword arguments forwarded to super. """ super(DUDEConfig, self).__init__(description=_DESCRIPTION, **kwargs) self.binary_mode = binary_mode self.ocr_engine = ocr_engine self.format = format def builder_configs(version): configurations = [] for binary_mode in [True, False]: for ocr_engine in ["Azure", "Amazon", "Tesseract"]: for format in ["original", "due"]: binary_name = "bin_" if binary_mode else "" configurations.append( DUDEConfig( name=f"{binary_name}{ocr_engine}_{format}", version=version, binary_mode=binary_mode, ocr_engine=ocr_engine, format=format, ) ) return configurations class DUDE(datasets.GeneratorBasedBuilder): """DUDE dataset.""" VERSION = datasets.Version("1.0.7") BUILDER_CONFIGS = builder_configs(VERSION) DEFAULT_CONFIG_NAME = ( "Amazon_original" # for some reason not working, need to pass a config anyway ) def _info(self): features = datasets.Features( { "docId": datasets.Value("string"), "questionId": datasets.Value("string"), "question": datasets.Value("string"), "answers": datasets.Sequence(datasets.Value("string")), "answers_page_bounding_boxes": datasets.Sequence( { "left": datasets.Value("int32"), "top": datasets.Value("int32"), "width": datasets.Value("int32"), "height": datasets.Value("int32"), "page": datasets.Value("int32"), } ), "answers_variants": datasets.Sequence(datasets.Value("string")), "answer_type": datasets.Value("string"), "data_split": datasets.Value("string"), "document": datasets.Value("binary") if self.config.binary_mode else datasets.Value("string"), "OCR": datasets.Value("binary") if self.config.binary_mode else datasets.Value("string"), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, supervised_keys=None, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators( self, dl_manager: datasets.DownloadManager ) -> List[datasets.SplitGenerator]: if "blind" in _URLS and os.path.exists(_URLS[f"blind"]): annotations = json.load(open(_URLS[f"blind"], "r")) else: annotations = json.load(open(_URLS[f"annotations"], "r")) if self.config.data_dir: # when unpacked to a custom directory binary_extraction_path = self.config.data_dir else: binaries_path = dl_manager.download(_URLS["binaries"]) binary_extraction_path = dl_manager.extract(binaries_path) # binaries_archive = dl_manager.iter_archive(binaries_path) splits = [] for split in _SPLITS: splits.append( datasets.SplitGenerator( name=split, gen_kwargs={ "binary_extraction_path": binary_extraction_path, "annotations": annotations, "split": split, }, ) ) return splits def _generate_examples(self, binary_extraction_path, annotations, split): def retrieve_doc(docid): extracted_path = os.path.join( binary_extraction_path, "PDF", split, docid + ".pdf" ) return extracted_path def retrieve_OCR(docid, ocr_engine="Amazon", format="original"): extracted_path = os.path.join( binary_extraction_path, "OCR", ocr_engine, docid + f"_{format}.json" ) return extracted_path annotations = annotations['data'] if isinstance(annotations, dict) else annotations split_condition = ( lambda x, split: bool(x["data_split"] == split) if split in ["train", "val"] else bool(split in x["data_split"]) ) # test, test2; only relevant for blind set annotations = [x for x in annotations if split_condition(x, split)] for i, a in enumerate(annotations): if a["docId"] in SKIP_DOC_IDS: continue a = dict(a) a["data_split"] = split if not "answers" in a.keys(): # test set has no ground truth provided a["answers"] = None a["answers_variants"] = None a["answer_type"] = None a["answers_page_bounding_boxes"] = None else: a["answers_page_bounding_boxes"] = parse_bbox( a.get("answers_page_bounding_boxes", []) ) docpath = retrieve_doc(a["docId"]) ocrpath = retrieve_OCR(a["docId"]) if self.config.binary_mode: with open(docpath, "rb") as f, open(ocrpath, "rb") as g: a["document"] = f.read() a["OCR"] = g.read() else: a["document"] = docpath a["OCR"] = ocrpath yield i, a