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