refactor dataset loading script (#1)

- refactor dataset loading script (beddd3c05f28fa231aba7cd154f5b366be0d5b05)


Co-authored-by: Daniel van Strien <davanstrien@users.noreply.huggingface.co>

DocLayNet.py

@@ -6,10 +6,10 @@ https://huggingface.co/datasets/ydshieh/coco_dataset_script/blob/main/coco_datas
 import json
 import os
 import datasets
+import collections
 
 
 class COCOBuilderConfig(datasets.BuilderConfig):
-
     def __init__(self, name, splits, **kwargs):
         super().__init__(name, **kwargs)
         self.splits = splits
@@ -43,12 +43,10 @@ _LICENSE = "CDLA-Permissive-1.0"
 # The HuggingFace dataset library don't host the datasets but only point to the original files
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 
-# This script is supposed to work with local (downloaded) COCO dataset.
 _URLs = {
     "core": "https://codait-cos-dax.s3.us.cloud-object-storage.appdomain.cloud/dax-doclaynet/1.0.0/DocLayNet_core.zip",
 }
 
-
 # Name of the dataset usually match the script name with CamelCase instead of snake_case
 class COCODataset(datasets.GeneratorBasedBuilder):
     """An example dataset script to work with the local (downloaded) COCO dataset"""
@@ -57,28 +55,51 @@ class COCODataset(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIG_CLASS = COCOBuilderConfig
     BUILDER_CONFIGS = [
-        COCOBuilderConfig(name='2022.08', splits=['train', 'val', 'test']),
+        COCOBuilderConfig(name="2022.08", splits=["train", "val", "test"]),
     ]
     DEFAULT_CONFIG_NAME = "2022.08"
 
     def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-
-        feature_dict = {
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                # Custom fields
+                "doc_category": datasets.Value(
+                    "string"
+                ),  # high-level document category
+                "collection": datasets.Value("string"),  # sub-collection name
+                "doc_name": datasets.Value("string"),  # original document filename
+                "page_no": datasets.Value("int64"),  # page number in original document
+            }
+        )
+        object_dict = {
+            "category_id": datasets.ClassLabel(
+                names=[
+                    "Caption",
+                    "Footnote",
+                    "Formula",
+                    "List-item",
+                    "Page-footer",
+                    "Page-header",
+                    "Picture",
+                    "Section-header",
+                    "Table",
+                    "Text",
+                    "Title",
+                ]
+            ),
+            "image_id": datasets.Value("string"),
             "id": datasets.Value("int64"),
-            "height": datasets.Value("int64"),
-            "width": datasets.Value("int64"),
-            "file_name": datasets.Value("string"),
-
-            # Custom fields
-            "doc_category": datasets.Value("string"),  # high-level document category
-            "collection": datasets.Value("string"),  # sub-collection name
-            "doc_name": datasets.Value("string"),  # original document filename
-            "page_no": datasets.Value("int64"),  # page number in original document
-            # "precedence": datasets.Value("int64"),  # annotation order, non-zero in case of redundant double- or triple-annotation
+            "area": datasets.Value("int64"),
+            "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+            "segmentation": [[datasets.Value("float32")]],
+            "iscrowd": datasets.Value("bool"),
+            "precedence": datasets.Value("int32"),
         }
-
-        features = datasets.Features(feature_dict)
+        features["objects"] = [object_dict]
 
         return datasets.DatasetInfo(
             # This is the description that will appear on the datasets page.
@@ -99,53 +120,41 @@ class COCODataset(datasets.GeneratorBasedBuilder):
 
     def _split_generators(self, dl_manager):
         """Returns SplitGenerators."""
-        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-
-        # data_dir = self.config.data_dir
-        # if not data_dir:
-        #     raise ValueError(
-        #         "This script is supposed to work with local (downloaded) COCO dataset. The argument `data_dir` in `load_dataset()` is required."
-        #     )
-
-        # _DL_URLS = {
-        #     "train": os.path.join(data_dir, "train2017.zip"),
-        #     "val": os.path.join(data_dir, "val2017.zip"),
-        #     "test": os.path.join(data_dir, "test2017.zip"),
-        #     "annotations_trainval": os.path.join(data_dir, "annotations_trainval2017.zip"),
-        #     "image_info_test": os.path.join(data_dir, "image_info_test2017.zip"),
-        # }
         archive_path = dl_manager.download_and_extract(_URLs)
-        print("archive_path: ", archive_path)
-
         splits = []
         for split in self.config.splits:
-            if split == 'train':
+            if split == "train":
                 dataset = datasets.SplitGenerator(
                     name=datasets.Split.TRAIN,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(archive_path["core"], "COCO", "train.json"),
+                        "json_path": os.path.join(
+                            archive_path["core"], "COCO", "train.json"
+                        ),
                         "image_dir": os.path.join(archive_path["core"], "PNG"),
                         "split": "train",
-                    }
+                    },
                 )
-            elif split in ['val', 'valid', 'validation', 'dev']:
+            elif split in ["val", "valid", "validation", "dev"]:
                 dataset = datasets.SplitGenerator(
                     name=datasets.Split.VALIDATION,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(archive_path["core"], "COCO", "val.json"),
+                        "json_path": os.path.join(
+                            archive_path["core"], "COCO", "val.json"
+                        ),
                         "image_dir": os.path.join(archive_path["core"], "PNG"),
                         "split": "val",
                     },
                 )
-            elif split == 'test':
+            elif split == "test":
                 dataset = datasets.SplitGenerator(
                     name=datasets.Split.TEST,
                     # These kwargs will be passed to _generate_examples
                     gen_kwargs={
-                        "json_path": os.path.join(archive_path["core"], "COCO", "test.json"),
+                        "json_path": os.path.join(
+                            archive_path["core"], "COCO", "test.json"
+                        ),
                         "image_dir": os.path.join(archive_path["core"], "PNG"),
                         "split": "test",
                     },
@@ -154,53 +163,48 @@ class COCODataset(datasets.GeneratorBasedBuilder):
                 continue
 
             splits.append(dataset)
-
         return splits
 
     def _generate_examples(
         # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-        self, json_path, image_dir, split
+        self,
+        json_path,
+        image_dir,
+        split,
     ):
-        """ Yields examples as (key, example) tuples. """
+        """Yields examples as (key, example) tuples."""
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
-
-        _features = ["image_id", "image_path", "doc_category", "collection", "height", "width", "file_name", "doc_name", "page_no", "id"]
-        features = list(_features)
-
-        with open(json_path, 'r', encoding='UTF-8') as fp:
-            data = json.load(fp)
-
-        # list of dict
-        images = data["images"]
-        entries = images
-
-        # build a dict of image_id -> image info dict
-        d = {image["id"]: image for image in images}
-
-        # list of dict
-        if split in ["train", "val"]:
-            annotations = data["annotations"]
-
-            # build a dict of image_id ->
+        def _image_info_to_example(image_info, image_dir):
+            image = image_info["file_name"]
+            return {
+                "image_id": image_info["id"],
+                "image": os.path.join(image_dir, image),
+                "width": image_info["width"],
+                "height": image_info["height"],
+                "doc_category": image_info["doc_category"],
+                "collection": image_info["collection"],
+                "doc_name": image_info["doc_name"],
+                "page_no": image_info["page_no"],
+            }
+
+        with open(json_path, encoding="utf8") as f:
+            annotation_data = json.load(f)
+            images = annotation_data["images"]
+            annotations = annotation_data["annotations"]
+            image_id_to_annotations = collections.defaultdict(list)
             for annotation in annotations:
-                _id = annotation["id"]
-                image_info = d[annotation["image_id"]]
-                annotation.update(image_info)
-                annotation["id"] = _id
-
-            entries = annotations
-
-        for id_, entry in enumerate(entries):
+                image_id_to_annotations[annotation["image_id"]].append(annotation)
 
-            entry = {k: v for k, v in entry.items() if k in features}
-
-            if split == "test":
-                entry["image_id"] = entry["id"]
-                entry["id"] = -1
-
-            entry["image_path"] = os.path.join(image_dir, entry["file_name"])
-
-            entry = {k: entry[k] for k in _features if k in entry}
-
-            yield str((entry["image_id"], entry["id"])), entry
+        for idx, image_info in enumerate(images):
+            example = _image_info_to_example(image_info, image_dir)
+            annotations = image_id_to_annotations[image_info["id"]]
+            objects = []
+            for annotation in annotations:
+                category_id = annotation["category_id"]  # Zero based counting
+                if category_id != -1:
+                    category_id = category_id - 1
+                annotation["category_id"] = category_id
+                objects.append(annotation)
+            example["objects"] = objects
+            yield idx, example