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"""Cartoonset-10k Data Set""" |
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import pickle |
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import numpy as np |
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import PIL.Image |
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import tarfile |
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import datasets |
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from datasets.tasks import ImageClassification |
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_CITATION = r""" |
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@article{DBLP:journals/corr/abs-1711-05139, |
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author = {Amelie Royer and |
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Konstantinos Bousmalis and |
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Stephan Gouws and |
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Fred Bertsch and |
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Inbar Mosseri and |
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Forrester Cole and |
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Kevin Murphy}, |
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title = {{XGAN:} Unsupervised Image-to-Image Translation for many-to-many Mappings}, |
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journal = {CoRR}, |
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volume = {abs/1711.05139}, |
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year = {2017}, |
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url = {http://arxiv.org/abs/1711.05139}, |
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eprinttype = {arXiv}, |
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eprint = {1711.05139}, |
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timestamp = {Mon, 13 Aug 2018 16:47:38 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-1711-05139.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Cartoon Set is a collection of random, 2D cartoon avatar images. The cartoons vary in 10 artwork |
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categories, 4 color categories, and 4 proportion categories, with a total of ~1013 possible |
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combinations. We provide sets of 10k and 100k randomly chosen cartoons and labeled attributes. |
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""" |
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_DATA_URLS = { |
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"10k": "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset10k.tgz", |
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"100k": "https://huggingface.co/datasets/cgarciae/cartoonset/resolve/1.0.0/data/cartoonset100k.tgz", |
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} |
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class Cartoonset(datasets.GeneratorBasedBuilder): |
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"""Cartoonset-10k Data Set""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="10k", |
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version=datasets.Version("1.0.0", ""), |
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description="Loads the Cartoonset-10k Data Set", |
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), |
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datasets.BuilderConfig( |
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name="100k", |
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version=datasets.Version("1.0.0", ""), |
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description="Loads the Cartoonset-100k Data Set", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "10k" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"img_bytes": datasets.Value("binary"), |
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} |
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), |
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supervised_keys=("img",), |
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homepage="https://www.cs.toronto.edu/~kriz/cifar.html", |
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citation=_CITATION, |
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) |
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def _split_generators(self, dl_manager: datasets.DownloadManager): |
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url = _DATA_URLS[self.config.name] |
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archive = dl_manager.download(url) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"files": dl_manager.iter_archive(archive), |
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"split": "train", |
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}, |
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), |
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] |
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def _generate_examples(self, files, split): |
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"""This function returns the examples in the raw (text) form.""" |
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path: str |
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file_obj: tarfile.ExFileObject |
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for path, file_obj in files: |
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if path.endswith(".png"): |
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image = file_obj.read() |
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yield path, { |
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"img_bytes": image, |
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} |
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