Delete loading script

funsd.py

@@ -1,123 +0,0 @@
-# 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{Jaume2019FUNSDAD,
-  title={FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents},
-  author={Guillaume Jaume and H. K. Ekenel and J. Thiran},
-  journal={2019 International Conference on Document Analysis and Recognition Workshops (ICDARW)},
-  year={2019},
-  volume={2},
-  pages={1-6}
-}
-"""
-_DESCRIPTION = """\
-https://guillaumejaume.github.io/FUNSD/
-"""
-
-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 dataset."""
-
-    BUILDER_CONFIGS = [
-        FunsdConfig(name="funsd", version=datasets.Version("1.0.0"), description="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://guillaumejaume.github.io/FUNSD/dataset.zip")
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": f"{downloaded_file}/dataset/training_data/"}
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": f"{downloaded_file}/dataset/testing_data/"}
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        logger.info("⏳ Generating examples from = %s", filepath)
-        ann_dir = os.path.join(filepath, "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")
-            image, 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}