Add dataset loading script for revised FUNSD dataset

funsd.py

@@ -0,0 +1,157 @@
+# coding=utf-8
+import json
+import os
+
+import datasets
+
+from PIL import Image
+import numpy as np
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{vu2020revising,
+        title={Revising FUNSD dataset for key-value detection in document images},
+        author={Vu, Hieu M and Nguyen, Diep Thi-Ngoc},
+        journal={arXiv preprint arXiv:2010.05322},
+        year={2020}
+}
+"""
+_DESCRIPTION = """\
+FUNSD is one of the limited publicly available datasets for information extraction from document images.
+The information in the FUNSD dataset is defined by text areas of four categories ("key", "value", "header", "other", and "background")
+and connectivity between areas as key-value relations. Inspecting FUNSD, we found several inconsistency in labeling, which impeded its
+applicability to the key-value extraction problem. In this report, we described some labeling issues in FUNSD and the revision we made
+to the dataset.
+"""
+
+_URL = """\
+https://drive.google.com/drive/folders/1HjJyoKqAh-pvtg3eQAmrbfzPccQZ48rz
+"""
+
+
+def load_image(image_path):
+    image = Image.open(image_path).convert("RGB")
+    w, h = image.size
+    return image, (w, h)
+
+
+def normalize_bbox(bbox, size):
+    return [
+        int(1000 * bbox[0] / size[0]),
+        int(1000 * bbox[1] / size[1]),
+        int(1000 * bbox[2] / size[0]),
+        int(1000 * bbox[3] / size[1]),
+    ]
+
+
+class FunsdConfig(datasets.BuilderConfig):
+    """BuilderConfig for FUNSD"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for FUNSD.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(FunsdConfig, self).__init__(**kwargs)
+
+
+class Funsd(datasets.GeneratorBasedBuilder):
+    """FUNSD: Form Understanding in Noisy Scanned Documents."""
+
+    BUILDER_CONFIGS = [
+        FunsdConfig(
+            name="funsd_vu2020revising",
+            version=datasets.Version("1.0.0"),
+            description="Revised FUNSD dataset",
+        ),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "words": datasets.Sequence(datasets.Value("string")),
+                    "bboxes": datasets.Sequence(
+                        datasets.Sequence(datasets.Value("int64"))
+                    ),
+                    "ner_tags": datasets.Sequence(
+                        datasets.features.ClassLabel(
+                            names=[
+                                "O",
+                                "B-HEADER",
+                                "I-HEADER",
+                                "B-QUESTION",
+                                "I-QUESTION",
+                                "B-ANSWER",
+                                "I-ANSWER",
+                            ]
+                        )
+                    ),
+                    "image_path": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+            homepage="https://guillaumejaume.github.io/FUNSD/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract(
+            "https://drive.google.com/uc?export=download&id=1wdJJQgRIb1c404SJnX1dyBSi7U2mVduI"
+        )
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"filepath": f"{downloaded_file}/FUNSD/training_data/"},
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={"filepath": f"{downloaded_file}/FUNSD/testing_data/"},
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        logger.info("⏳ Generating examples from = %s", filepath)
+        ann_dir = os.path.join(filepath, "adjusted_annotations")
+        img_dir = os.path.join(filepath, "images")
+        for guid, file in enumerate(sorted(os.listdir(ann_dir))):
+            words = []
+            bboxes = []
+            ner_tags = []
+            file_path = os.path.join(ann_dir, file)
+            with open(file_path, "r", encoding="utf8") as f:
+                data = json.load(f)
+            image_path = os.path.join(img_dir, file)
+            image_path = image_path.replace("json", "png")
+            _, size = load_image(image_path)
+            for item in data["form"]:
+                words_example, label = item["words"], item["label"]
+                words_example = [w for w in words_example if w["text"].strip() != ""]
+                if len(words_example) == 0:
+                    continue
+                if label == "other":
+                    for w in words_example:
+                        words.append(w["text"])
+                        ner_tags.append("O")
+                        bboxes.append(normalize_bbox(w["box"], size))
+                else:
+                    words.append(words_example[0]["text"])
+                    ner_tags.append("B-" + label.upper())
+                    bboxes.append(normalize_bbox(words_example[0]["box"], size))
+                    for w in words_example[1:]:
+                        words.append(w["text"])
+                        ner_tags.append("I-" + label.upper())
+                        bboxes.append(normalize_bbox(w["box"], size))
+            yield guid, {
+                "id": str(guid),
+                "words": words,
+                "bboxes": bboxes,
+                "ner_tags": ner_tags,
+                "image_path": image_path,
+            }