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cartoonset / cartoonset.py

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100k

f105ee9 over 2 years ago

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	"""Cartoonset-10k Data Set"""


	import pickle

	import numpy as np
	import PIL.Image


	import datasets
	from datasets.tasks import ImageClassification


	_CITATION = """\
	@TECHREPORT{Krizhevsky09learningmultiple,
	author = {Alex Krizhevsky},
	title = {Learning multiple layers of features from tiny images},
	institution = {},
	year = {2009}
	}
	"""

	_DESCRIPTION = """\
	The Cartoonset-10k dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images
	per class. There are 50000 training images and 10000 test images.
	"""

	_DATA_URLS = {
	"10k": "https://storage.cloud.google.com/cartoonset_public_files/cartoonset10k.tgz",
	"100k": "https://storage.cloud.google.com/cartoonset_public_files/cartoonset100k.tgz",
	}

	_NAMES = []


	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-10k Data Set",
	),
	]

	DEFAULT_CONFIG_NAME = "10k"

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"img": datasets.Image(),
	# "label": datasets.features.ClassLabel(names=_NAMES),
	}
	),
	supervised_keys=("img",),
	homepage="https://www.cs.toronto.edu/~kriz/cifar.html",
	citation=_CITATION,
	# task_templates=ImageClassification(
	# image_column="img", label_column="label"
	# ),
	)

	def _split_generators(self, dl_manager: datasets.DownloadManager):
	url = _DATA_URLS[self.config.name]
	print("URL:", url)
	exit()
	archive = dl_manager.download(url)

	print(archive)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"files": dl_manager.iter_archive(archive),
	"split": "train",
	},
	),
	# datasets.SplitGenerator(
	# name=datasets.Split.TEST, gen_kwargs={"files": dl_manager.iter_archive(archive), "split": "test"}
	# ),
	]

	def _generate_examples(self, files, split):
	"""This function returns the examples in the raw (text) form."""

	# if split == "train":
	# batches = ["data_batch_1", "data_batch_2", "data_batch_3", "data_batch_4", "data_batch_5"]

	# if split == "test":
	# batches = ["test_batch"]
	# batches = [f"Cartoonset-10k-batches-py/{filename}" for filename in batches]
	print("FILES", files)

	path: str
	for path, file_obj in files:

	if path.endswith(".png"):
	image = PIL.Image.open(path)

	yield path, {
	"img": np.asarray(image),
	}