Upload mnist.py

mnist.py

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+# 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])}