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cats_vs_dogs_sample

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cats_vs_dogs_sample / cats_vs_dogs_sample.py

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	# 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/resolve/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 = """\\n@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": datasets.Image(),
	"labels": datasets.features.ClassLabel(names=["cat", "dog"]),
	}
	),
	supervised_keys=("image", "labels"),
	task_templates=[
	ImageClassification(
	image_column="image", label_column="labels"
	)
	],
	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(sorted(images_path.glob("*/.jpg"))):
	yield str(i), {
	"image": str(filepath),
	"labels": filepath.parent.name.lower(),
	}