clean cartoonset
Browse files- cartoonset.py +25 -30
cartoonset.py
CHANGED
@@ -11,18 +11,32 @@ import datasets
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from datasets.tasks import ImageClassification
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_CITATION = """
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@
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}
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"""
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_DESCRIPTION = """\
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"""
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_DATA_URLS = {
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@@ -45,7 +59,7 @@ class Cartoonset(datasets.GeneratorBasedBuilder):
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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-
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),
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]
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@@ -57,24 +71,16 @@ class Cartoonset(datasets.GeneratorBasedBuilder):
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features=datasets.Features(
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{
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"img": datasets.Image(),
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# "label": datasets.features.ClassLabel(names=_NAMES),
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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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# task_templates=ImageClassification(
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# image_column="img", label_column="label"
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# ),
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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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print("URL:", url)
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exit()
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archive = dl_manager.download(url)
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print(archive)
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return [
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datasets.SplitGenerator(
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@@ -84,28 +90,17 @@ class Cartoonset(datasets.GeneratorBasedBuilder):
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"split": "train",
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},
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),
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# datasets.SplitGenerator(
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# name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
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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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# if split == "train":
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# batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]
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# if split == "test":
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# batches = ["test_batch"]
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# batches = [f"Cartoonset-10k-batches-py/{filename}" for filename in batches]
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print("FILES", files)
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path: str
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for path, file_obj in files:
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if path.endswith(".png"):
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image = PIL.Image.open(
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yield path, {
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"img":
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}
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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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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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features=datasets.Features(
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{
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"img": datasets.Image(),
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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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print("URL:", url)
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return [
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datasets.SplitGenerator(
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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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for path, file_obj in files:
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if path.endswith(".png"):
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image = PIL.Image.open(file_obj)
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yield path, {
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"img": image,
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}
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