| """Cartoonset-10k Data Set""" | |
| from io import BytesIO | |
| from typing import Optional | |
| import tarfile | |
| import pandas as pd | |
| import datasets | |
| _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 (images only).", | |
| ), | |
| datasets.BuilderConfig( | |
| name="10k+features", | |
| version=datasets.Version("1.0.0", ""), | |
| description="Loads the Cartoonset-10k Data Set (images and attributes).", | |
| ), | |
| datasets.BuilderConfig( | |
| name="100k", | |
| version=datasets.Version("1.0.0", ""), | |
| description="Loads the Cartoonset-100k Data Set (images only).", | |
| ), | |
| datasets.BuilderConfig( | |
| name="100k+features", | |
| version=datasets.Version("1.0.0", ""), | |
| description="Loads the Cartoonset-100k Data Set (images and attributes).", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "10k" | |
| def _info(self): | |
| features = {"img_bytes": datasets.Value("binary")} | |
| if self.config.name.endswith("+features"): | |
| features.update( | |
| { | |
| "eye_angle": datasets.Value("int32"), | |
| "eye_angle_num_categories": datasets.Value("int32"), | |
| "eye_lashes": datasets.Value("int32"), | |
| "eye_lashes_num_categories": datasets.Value("int32"), | |
| "eye_lid": datasets.Value("int32"), | |
| "eye_lid_num_categories": datasets.Value("int32"), | |
| "chin_length": datasets.Value("int32"), | |
| "chin_length_num_categories": datasets.Value("int32"), | |
| "eyebrow_weight": datasets.Value("int32"), | |
| "eyebrow_weight_num_categories": datasets.Value("int32"), | |
| "eyebrow_shape": datasets.Value("int32"), | |
| "eyebrow_shape_num_categories": datasets.Value("int32"), | |
| "eyebrow_thickness": datasets.Value("int32"), | |
| "eyebrow_thickness_num_categories": datasets.Value("int32"), | |
| "face_shape": datasets.Value("int32"), | |
| "face_shape_num_categories": datasets.Value("int32"), | |
| "facial_hair": datasets.Value("int32"), | |
| "facial_hair_num_categories": datasets.Value("int32"), | |
| "hair": datasets.Value("int32"), | |
| "hair_num_categories": datasets.Value("int32"), | |
| "eye_color": datasets.Value("int32"), | |
| "eye_color_num_categories": datasets.Value("int32"), | |
| "face_color": datasets.Value("int32"), | |
| "face_color_num_categories": datasets.Value("int32"), | |
| "hair_color": datasets.Value("int32"), | |
| "hair_color_num_categories": datasets.Value("int32"), | |
| "glasses": datasets.Value("int32"), | |
| "glasses_num_categories": datasets.Value("int32"), | |
| "glasses_color": datasets.Value("int32"), | |
| "glasses_color_num_categories": datasets.Value("int32"), | |
| "eye_slant": datasets.Value("int32"), | |
| "eye_slant_num_categories": datasets.Value("int32"), | |
| "eyebrow_width": datasets.Value("int32"), | |
| "eyebrow_width_num_categories": datasets.Value("int32"), | |
| "eye_eyebrow_distance": datasets.Value("int32"), | |
| "eye_eyebrow_distance_num_categories": datasets.Value("int32"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features(features), | |
| supervised_keys=("img_bytes",), | |
| 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.replace("+features", "")] | |
| 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.""" | |
| if self.config.name.endswith("+features"): | |
| return self._generate_examples_with_features(files, split) | |
| else: | |
| return self._generate_examples_without_features(files, split) | |
| def _generate_examples_without_features(self, files, split): | |
| path: str | |
| file_obj: tarfile.ExFileObject | |
| root: str | |
| for path, file_obj in files: | |
| root = path[:-4] | |
| if path.endswith(".png"): | |
| image = file_obj.read() | |
| yield root, {"img_bytes": image} | |
| def _generate_examples_with_features(self, files, split): | |
| path: str | |
| file_obj: tarfile.ExFileObject | |
| outputs = {} | |
| root: Optional[str] = None | |
| for path, file_obj in files: | |
| root = path[:-4] | |
| if root not in outputs: | |
| outputs[root] = {} | |
| current_output = outputs[root] | |
| if path.endswith(".png"): | |
| image = file_obj.read() | |
| current_output["img_bytes"] = image | |
| else: | |
| df = pd.read_csv( | |
| BytesIO(file_obj.read()), | |
| header=None, | |
| names=["feature", "value", "num_categories"], | |
| ) | |
| for index, row in df.iterrows(): | |
| current_output[row.feature] = row.value | |
| current_output[f"{row.feature}_num_categories"] = row.num_categories | |
| if "img_bytes" in current_output and len(current_output) > 1: | |
| yield root, current_output | |
| del outputs[root] | |
| root = None | |
| if len(outputs) > 0: | |
| raise ValueError( | |
| f"Unable to extract the following samples: {list(outputs)}" | |
| ) | |