Delete loading script

cppe-5.py

@@ -1,135 +0,0 @@
-# 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
-
-import datasets
-
-
-_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.
-"""
-
-_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."""
-
-    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(
-                    {
-                        "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):
-        archive = dl_manager.download(_URL)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "annotation_file_path": "annotations/train.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "annotation_file_path": "annotations/test.json",
-                    "files": dl_manager.iter_archive(archive),
-                },
-            ),
-        ]
-
-    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
-        # 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": image["id"],
-                    "image": {"path": path, "bytes": f.read()},
-                    "width": image["width"],
-                    "height": image["height"],
-                    "objects": objects,
-                }
-                idx += 1