renamed and quention's iteration suggestion

DUDE_loader.py

@@ -0,0 +1,191 @@
+# 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 os
+import copy
+import json
+from pathlib import Path
+from typing import List
+import pdf2image
+from tqdm import tqdm
+
+
+import datasets
+
+
+_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 = ["sample"]  # ["train", "val", "test"]
+
+_URLS = {}
+for split in _SPLITS:
+    _URLS[
+        f"{split}_annotations"
+    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_dataset.json"
+    _URLS[
+        f"{split}_pdfs"
+    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_pdfs.tar.gz"
+    _URLS[
+        f"{split}_OCR"
+    ] = f"https://huggingface.co/datasets/jordyvl/DUDE_loader/resolve/main/data/DUDE_{split}_OCR.tar.gz"
+
+
+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 DUDE(datasets.GeneratorBasedBuilder):
+    """DUDE dataset."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="DUDE",
+            version=datasets.Version("0.0.1"),
+            description=_DESCRIPTION,
+        )
+    ]
+
+    DEFAULT_CONFIG_NAME = "DUDE"
+
+    def _info(self):
+
+        features = datasets.Features(
+            {
+                "docId": datasets.Value("string"),
+                "questionId": datasets.Value("string"),
+                "question": datasets.Value("string"),
+                "answers": datasets.Sequence(datasets.Value("string")),
+                # ''' OPTIONAL?
+                # "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"),
+                "OCR": datasets.Value("binary"),
+            }
+        )
+
+        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]:
+
+        splits = []
+        for split in _SPLITS:
+            annotations = {}
+            if f"{split}_annotations" in _URLS:  # blind test set
+                annotations = json.load(open(_URLS[f"{split}_annotations"], "r"))
+            pdfs_archive_path = dl_manager.download(_URLS[f"{split}_pdfs"])
+            pdfs_archive = dl_manager.iter_archive(pdfs_archive_path)
+            OCR_archive_path = dl_manager.download(_URLS[f"{split}_OCR"])
+            OCR_archive = dl_manager.iter_archive(OCR_archive_path)
+            splits.append(
+                datasets.SplitGenerator(
+                    name=split,
+                    gen_kwargs={
+                        "pdfs_archive": pdfs_archive,
+                        "OCR_archive": OCR_archive,
+                        "annotations": annotations,
+                        "split": split,
+                    },
+                )
+            )
+        return splits
+
+    def _generate_examples(self, pdfs_archive, OCR_archive, annotations, split):
+        def retrieve_doc(pdfs_archive, docid):
+            for file_path, file_obj in pdfs_archive:
+                path, ext = file_path.split(".")
+                md5 = path.split("/")[-1]
+
+                if md5 == docid:
+                    # images = pdf2image.convert_from_bytes(file_obj.read())
+                    return file_obj.read()  # binary
+
+        def retrieve_OCR(OCR_archive, docid):
+            for file_path, file_obj in OCR_archive:
+                # /DUDE_sample_OCR/OCR/Amazon Textract/md5_{original,due}.json
+                path, ext = file_path.split(".")
+                filename = path.split("/")[-1]
+                md5 = filename.split("_")[0]
+
+                if md5 == docid and "original" in filename:
+                    return json.loads(file_obj.read())  # binary
+
+        question = self.info.features["question"]
+        answers = self.info.features["answers"]
+
+        extensions = {"pdf", "PDF"}
+
+        for i, a in enumerate(annotations):
+            a["data_split"] = split
+            a["document"] = retrieve_doc(pdfs_archive, a["docId"])
+            a["OCR"] = retrieve_OCR(OCR_archive, a["docId"])
+            a.pop("answers_page_bounding_boxes")  # fix later
+
+            yield i, a