Delete loading script

MyoQuant-SDH-Data.py

@@ -1,130 +0,0 @@
-# Copyright 2020 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.
-"""MyoQuant-SDH-Data: The MyoQuant SDH Model Data."""
-
-
-import csv
-import json
-import os
-
-import datasets
-
-
-_CITATION = """\
-@InProceedings{Meyer,
-title = {MyoQuant SDH Data},
-author={Corentin Meyer},
-year={2022}
-}
-"""
-_NAMES = ["control", "sick"]
-
-_DESCRIPTION = """\
-This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders.
-"""
-
-_HOMEPAGE = "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data"
-
-_LICENSE = "agpl-3.0"
-
-_URLS = {
-    "SDH_16k": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/SDH_16k.zip"
-}
-_METADATA_URL = {
-    "SDH_16k_metadata": "https://huggingface.co/datasets/corentinm7/MyoQuant-SDH-Data/resolve/main/SDH_16k/metadata.jsonl"
-}
-
-
-class SDH_16k(datasets.GeneratorBasedBuilder):
-    """This dataset is used to train the SDH model of MyoQuant to detect and quantify anomaly in the mitochondria repartition in SDH stained muscle fiber with myopathy disorders."""
-
-    VERSION = datasets.Version("1.0.0")
-
-    # This is an example of a dataset with multiple configurations.
-    # If you don't want/need to define several sub-sets in your dataset,
-    # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
-
-    # If you need to make complex sub-parts in the datasets with configurable options
-    # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
-    # BUILDER_CONFIG_CLASS = MyBuilderConfig
-
-    # You will be able to load one or the other configurations in the following list with
-    # data = datasets.load_dataset('my_dataset', 'first_domain')
-    # data = datasets.load_dataset('my_dataset', 'second_domain')
-
-    DEFAULT_CONFIG_NAME = "SDH_16k"  # It's not mandatory to have a default configuration. Just use one if it make sense.
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.ClassLabel(num_classes=2, names=_NAMES),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            homepage=_HOMEPAGE,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        archive_path = dl_manager.download(_URLS)
-        split_metadata_path = dl_manager.download(_METADATA_URL)
-        files_metadata = {}
-        with open(split_metadata_path["SDH_16k_metadata"], encoding="utf-8") as f:
-            for lines in f.read().splitlines():
-                file_json_metdata = json.loads(lines)
-                files_metadata.setdefault(file_json_metdata["split"], []).append(
-                    file_json_metdata
-                )
-        downloaded_files = dl_manager.download_and_extract(archive_path)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "download_path": downloaded_files["SDH_16k"],
-                    "metadata": files_metadata["train"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "download_path": downloaded_files["SDH_16k"],
-                    "metadata": files_metadata["validation"],
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "download_path": downloaded_files["SDH_16k"],
-                    "metadata": files_metadata["test"],
-                },
-            ),
-        ]
-
-    def _generate_examples(self, download_path, metadata):
-        """Generate images and labels for splits."""
-        for i, single_metdata in enumerate(metadata):
-            img_path = os.path.join(
-                download_path,
-                single_metdata["split"],
-                single_metdata["label"],
-                single_metdata["file_name"],
-            )
-            yield i, {
-                "image": img_path,
-                "label": single_metdata["label"],
-            }