Create load_cifar.py

load_cifar.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
+"""CIFAR-10 Data Set"""
+
+
+import pickle
+
+import numpy as np
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+_CITATION = """\
+@TECHREPORT{Krizhevsky09learningmultiple,
+    author = {Alex Krizhevsky},
+    title = {Learning multiple layers of features from tiny images},
+    institution = {},
+    year = {2009}
+}
+"""
+
+_DESCRIPTION = """\
+The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
+per class. There are 50000 training images and 10000 test images.
+"""
+
+_DATA_URL = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+
+_NAMES = [
+    "airplane",
+    "automobile",
+    "bird",
+    "cat",
+    "deer",
+    "dog",
+    "frog",
+    "horse",
+    "ship",
+    "truck",
+]
+
+
+class Cifar10(datasets.GeneratorBasedBuilder):
+    """CIFAR-10 Data Set"""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text import of CIFAR-10 Data Set",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "img": datasets.Image(),
+                    "label": datasets.features.ClassLabel(names=_NAMES),
+                }
+            ),
+            supervised_keys=("img", "label"),
+            homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
+            citation=_CITATION,
+            task_templates=ImageClassification(image_column="img", label_column="label"),
+        )
+
+    def _split_generators(self, dl_manager):
+        archive = dl_manager.download(_DATA_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "train"}
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
+            ),
+        ]
+
+    def _generate_examples(self, files, split):
+        """This function returns the examples in the raw (text) form."""
+
+        if split == "train":
+            batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]
+
+        if split == "test":
+            batches = ["test_batch"]
+        batches = [f"cifar-10-batches-py/{filename}" for filename in batches]
+
+        for path, fo in files:
+
+            if path in batches:
+                dict = pickle.load(fo, encoding="bytes")
+
+                labels = dict[b"labels"]
+                images = dict[b"data"]
+
+                for idx, _ in enumerate(images):
+
+                    img_reshaped = np.transpose(np.reshape(images[idx], (3, 32, 32)), (1, 2, 0))
+
+                    yield f"{path}_{idx}", {
+                        "img": img_reshaped,
+                        "label": labels[idx],
+                    }