cartoonset.py

"""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):
        self.config.name
        print("URL:", _DATA_URL)
        archive = dl_manager.download(_DATA_URL)

        print(archive)
        exit()

        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),
                }