Create cats_vs_dogs_sample.py

cats_vs_dogs_sample.py

@@ -0,0 +1,78 @@
+# 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.
+"""Sample of the Microsoft Cats vs. Dogs dataset"""
+
+from pathlib import Path
+from typing import List
+
+import datasets
+from datasets.tasks import ImageClassification
+
+
+logger = datasets.logging.get_logger(__name__)
+
+_URL = "https://huggingface.co/datasets/hf-internal-testing/cats_vs_dogs_sample/raw/main/cats_and_dogs_sample.zip"
+
+_HOMEPAGE = "https://www.microsoft.com/en-us/download/details.aspx?id=54765"
+
+_DESCRIPTION = "A 50 image sample of microsoft's cats vs. dogs dataset for unit testing."
+
+_CITATION = """\
+@Inproceedings (Conference){asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization,
+    author = {Elson, Jeremy and Douceur, John (JD) and Howell, Jon and Saul, Jared},
+    title = {Asirra: A CAPTCHA that Exploits Interest-Aligned Manual Image Categorization},
+    booktitle = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
+    year = {2007},
+    month = {October},
+    publisher = {Association for Computing Machinery, Inc.},
+    url = {https://www.microsoft.com/en-us/research/publication/asirra-a-captcha-that-exploits-interest-aligned-manual-image-categorization/},
+    edition = {Proceedings of 14th ACM Conference on Computer and Communications Security (CCS)},
+}
+"""
+
+
+class CatsVsDogs(datasets.GeneratorBasedBuilder):
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "image_file_path": datasets.Value("string"),
+                    "labels": datasets.features.ClassLabel(names=["cat", "dog"]),
+                }
+            ),
+            supervised_keys=("image_file_path", "labels"),
+            task_templates=[
+                ImageClassification(
+                    image_file_path_column="image_file_path", label_column="labels", labels=["cat", "dog"]
+                )
+            ],
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        images_path = Path(dl_manager.download_and_extract(_URL)) / "PetImagesSample"
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images_path": images_path}),
+        ]
+
+    def _generate_examples(self, images_path):
+        logger.info("generating examples from = %s", images_path)
+        for i, filepath in enumerate(images_path.glob("**/*.jpg")):
+            yield str(i), {
+                "image_file_path": str(filepath),
+                "labels": filepath.parent.name.lower(),
+            }