Upload TTFormLM.py

TTFormLM.py

@@ -0,0 +1,106 @@
+# coding=utf-8
+import json
+import os
+
+import datasets
+
+from PIL import Image
+import numpy as np
+
+logger = datasets.logging.get_logger(__name__)
+
+
+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 TTFormLMConfig(datasets.BuilderConfig):
+    """BuilderConfig for TTForm"""
+
+    def __init__(self, **kwargs):
+        """BuilderConfig for TTForm.
+
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(TTFormLMConfig, self).__init__(**kwargs)
+
+class TTFormLM(datasets.GeneratorBasedBuilder):
+    """TTForm dataset."""
+
+    BUILDER_CONFIGS = [
+        TTFormLMConfig(name="ttform", version=datasets.Version("1.0.0"), description="TTFormLM dataset"),
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            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
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        downloaded_file = dl_manager.download_and_extract("https://drive.google.com/uc?export=download&id=18ytJQIAE4wFtE5fDhnlFW5zcRWI_tJjR")
+        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}