Delete loading script

mnist.py

@@ -1,120 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
-#
-# 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.
-
-# Lint as: python3
-"""MNIST Data Set"""
-
-
-import struct
-
-import numpy as np
-
-import datasets
-from datasets.tasks import ImageClassification
-
-
-_CITATION = """\
-@article{lecun2010mnist,
-  title={MNIST handwritten digit database},
-  author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
-  journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
-  volume={2},
-  year={2010}
-}
-"""
-
-_DESCRIPTION = """\
-The MNIST dataset consists of 70,000 28x28 black-and-white images in 10 classes (one for each digits), with 7,000
-images per class. There are 60,000 training images and 10,000 test images.
-"""
-
-_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/"
-_URLS = {
-    "train_images": "train-images-idx3-ubyte.gz",
-    "train_labels": "train-labels-idx1-ubyte.gz",
-    "test_images": "t10k-images-idx3-ubyte.gz",
-    "test_labels": "t10k-labels-idx1-ubyte.gz",
-}
-
-
-class MNIST(datasets.GeneratorBasedBuilder):
-    """MNIST Data Set"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name="mnist",
-            version=datasets.Version("1.0.0"),
-            description=_DESCRIPTION,
-        )
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "image": datasets.Image(),
-                    "label": datasets.features.ClassLabel(names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]),
-                }
-            ),
-            supervised_keys=("image", "label"),
-            homepage="http://yann.lecun.com/exdb/mnist/",
-            citation=_CITATION,
-            task_templates=[
-                ImageClassification(
-                    image_column="image",
-                    label_column="label",
-                )
-            ],
-        )
-
-    def _split_generators(self, dl_manager):
-        urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
-        downloaded_files = dl_manager.download_and_extract(urls_to_download)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "filepath": (downloaded_files["train_images"], downloaded_files["train_labels"]),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": (downloaded_files["test_images"], downloaded_files["test_labels"]),
-                    "split": "test",
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath, split):
-        """This function returns the examples in the raw form."""
-        # Images
-        with open(filepath[0], "rb") as f:
-            # First 16 bytes contain some metadata
-            _ = f.read(4)
-            size = struct.unpack(">I", f.read(4))[0]
-            _ = f.read(8)
-            images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
-
-        # Labels
-        with open(filepath[1], "rb") as f:
-            # First 8 bytes contain some metadata
-            _ = f.read(8)
-            labels = np.frombuffer(f.read(), dtype=np.uint8)
-
-        for idx in range(size):
-            yield idx, {"image": images[idx], "label": str(labels[idx])}