[update]add dataset.tar.gz to speed up download

cppe-5.py

@@ -1,20 +1,26 @@
-#!/usr/bin/python3
-# -*- coding: utf-8 -*-
-from glob import glob
+# coding=utf-8
+# Copyright 2021 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.
+"""CPPE-5 dataset."""
+
+
+import collections
 import json
 import os
-from pathlib import Path
 
 import datasets
-from PIL import Image
 
-# _URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
-
-_HOMEPAGE = "https://sites.google.com/view/cppe5"
-
-_LICENSE = "Unknown"
-
-_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
 
 _CITATION = """\
 @misc{dagli2021cppe5,
@@ -33,6 +39,15 @@ to allow the study of subordinate categorization of medical personal protective
 which is not possible with other popular data sets that focus on broad level categories.
 """
 
+_HOMEPAGE = "https://sites.google.com/view/cppe5"
+
+_LICENSE = "Unknown"
+
+# _URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
+_URL = "data/dataset.tar.gz"
+
+_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
+
 
 class CPPE5(datasets.GeneratorBasedBuilder):
     """CPPE - 5 dataset."""
@@ -47,12 +62,12 @@ class CPPE5(datasets.GeneratorBasedBuilder):
                 "width": datasets.Value("int32"),
                 "height": datasets.Value("int32"),
                 "objects": datasets.Sequence(
-                    feature=datasets.Features({
+                    {
                         "id": datasets.Value("int64"),
                         "area": datasets.Value("int64"),
                         "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                         "category": datasets.ClassLabel(names=_CATEGORIES),
-                    })
+                    }
                 ),
             }
         )
@@ -65,54 +80,56 @@ class CPPE5(datasets.GeneratorBasedBuilder):
         )
 
     def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        train_json = dl_manager.download("data/annotations/train.jsonl")
-        test_json = dl_manager.download("data/annotations/test.jsonl")
-
+        archive = dl_manager.download(_URL)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 gen_kwargs={
-                    "archive_path": train_json,
-                    "dl_manager": dl_manager,
+                    "annotation_file_path": "annotations/train.json",
+                    "files": dl_manager.iter_archive(archive),
                 },
             ),
             datasets.SplitGenerator(
                 name=datasets.Split.TEST,
                 gen_kwargs={
-                    "archive_path": test_json,
-                    "dl_manager": dl_manager,
+                    "annotation_file_path": "annotations/test.json",
+                    "files": dl_manager.iter_archive(archive),
                 },
             ),
         ]
 
-    def _generate_examples(self, archive_path, dl_manager):
-        """Yields examples."""
-        archive_path = Path(archive_path)
+    def _generate_examples(self, annotation_file_path, files):
+        def process_annot(annot, category_id_to_category):
+            return {
+                "id": annot["id"],
+                "area": annot["area"],
+                "bbox": annot["bbox"],
+                "category": category_id_to_category[annot["category_id"]],
+            }
 
+        image_id_to_image = {}
         idx = 0
-
-        with open(archive_path, "r", encoding="utf-8") as f:
-            for row in f:
-                sample = json.loads(row)
-
-                file_path = sample["image"]
-                file_path = dl_manager.download(file_path)
-
-                with open(file_path, "rb") as image_f:
-                    image_bytes = image_f.read()
-                    # image = Image.open(image_f)
-
+        # This loop relies on the ordering of the files in the archive:
+        # Annotation files come first, then the images.
+        for path, f in files:
+            file_name = os.path.basename(path)
+            if path == annotation_file_path:
+                annotations = json.load(f)
+                category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
+                image_id_to_annotations = collections.defaultdict(list)
+                for annot in annotations["annotations"]:
+                    image_id_to_annotations[annot["image_id"]].append(annot)
+                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
+            elif file_name in image_id_to_image:
+                image = image_id_to_image[file_name]
+                objects = [
+                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
+                ]
                 yield idx, {
-                    "image_id": sample["image_id"],
-                    "image": {"path": file_path, "bytes": image_bytes},
-                    # "image": image,
-                    "width": sample["width"],
-                    "height": sample["height"],
-                    "objects": sample["objects"],
+                    "image_id": image["id"],
+                    "image": {"path": path, "bytes": f.read()},
+                    "width": image["width"],
+                    "height": image["height"],
+                    "objects": objects,
                 }
                 idx += 1
-
-
-if __name__ == '__main__':
-    pass

cppe_5_backup.py

@@ -0,0 +1,118 @@
+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+from glob import glob
+import json
+import os
+from pathlib import Path
+
+import datasets
+from PIL import Image
+
+# _URL = "https://drive.google.com/uc?id=1MGnaAfbckUmigGUvihz7uiHGC6rBIbvr"
+
+_HOMEPAGE = "https://sites.google.com/view/cppe5"
+
+_LICENSE = "Unknown"
+
+_CATEGORIES = ["Coverall", "Face_Shield", "Gloves", "Goggles", "Mask"]
+
+_CITATION = """\
+@misc{dagli2021cppe5,
+      title={CPPE-5: Medical Personal Protective Equipment Dataset},
+      author={Rishit Dagli and Ali Mustufa Shaikh},
+      year={2021},
+      eprint={2112.09569},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+"""
+
+_DESCRIPTION = """\
+CPPE - 5 (Medical Personal Protective Equipment) is a new challenging dataset with the goal
+to allow the study of subordinate categorization of medical personal protective equipments,
+which is not possible with other popular data sets that focus on broad level categories.
+"""
+
+
+class CPPE5(datasets.GeneratorBasedBuilder):
+    """CPPE - 5 dataset."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = datasets.Features(
+            {
+                "image_id": datasets.Value("int64"),
+                "image": datasets.Image(),
+                "width": datasets.Value("int32"),
+                "height": datasets.Value("int32"),
+                "objects": datasets.Sequence(
+                    feature=datasets.Features({
+                        "id": datasets.Value("int64"),
+                        "area": datasets.Value("int64"),
+                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
+                        "category": datasets.ClassLabel(names=_CATEGORIES),
+                    })
+                ),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            license=_LICENSE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        train_json = dl_manager.download("data/annotations/train.jsonl")
+        test_json = dl_manager.download("data/annotations/test.jsonl")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "archive_path": train_json,
+                    "dl_manager": dl_manager,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "archive_path": test_json,
+                    "dl_manager": dl_manager,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, archive_path, dl_manager):
+        """Yields examples."""
+        archive_path = Path(archive_path)
+
+        idx = 0
+
+        with open(archive_path, "r", encoding="utf-8") as f:
+            for row in f:
+                sample = json.loads(row)
+
+                file_path = sample["image"]
+                file_path = dl_manager.download(file_path)
+
+                with open(file_path, "rb") as image_f:
+                    image_bytes = image_f.read()
+                    # image = Image.open(image_f)
+
+                yield idx, {
+                    "image_id": sample["image_id"],
+                    "image": {"path": file_path, "bytes": image_bytes},
+                    # "image": image,
+                    "width": sample["width"],
+                    "height": sample["height"],
+                    "objects": sample["objects"],
+                }
+                idx += 1
+
+
+if __name__ == '__main__':
+    pass