Datasets:
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import struct
import numpy as np
import datasets
from datasets.tasks import ImageClassification
_CITATION = R"""
@article{DBLP:journals/corr/abs-1812-01718,
author = {Tarin Clanuwat and
Mikel Bober{-}Irizar and
Asanobu Kitamoto and
Alex Lamb and
Kazuaki Yamamoto and
David Ha},
title = {Deep Learning for Classical Japanese Literature},
journal = {CoRR},
volume = {abs/1812.01718},
year = {2018},
url = {http://arxiv.org/abs/1812.01718},
eprinttype = {arXiv},
eprint = {1812.01718},
timestamp = {Thu, 14 Oct 2021 09:15:14 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1812-01718.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
"""
_URL = "./raw/"
_URLS = {
"train_images": "train-images-idx3-ubyte.gz",
"train_labels": "train-labels-idx1-ubyte.gz",
"test_images": "t10k-images-idx3-ubyte.gz",
"test_labels": "t10k-labels-idx1-ubyte.gz",
}
class KMNIST(datasets.GeneratorBasedBuilder):
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="kmnist",
version=datasets.Version("1.0.0"),
)
]
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.features.ClassLabel(
names=[
"お",
"き",
"す",
"つ",
"な",
"は",
"ま",
"や",
"れ",
"を",
]
),
}
),
supervised_keys=("image", "label"),
homepage="https://github.com/rois-codh/kmnist",
citation=_CITATION,
task_templates=[
ImageClassification(
image_column="image",
label_column="label",
)
],
)
def _split_generators(self, dl_manager):
urls_to_download = {key: _URL + fname for key, fname in _URLS.items()}
downloaded_files = dl_manager.download_and_extract(urls_to_download)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": (
downloaded_files["train_images"],
downloaded_files["train_labels"],
),
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": (
downloaded_files["test_images"],
downloaded_files["test_labels"],
),
"split": "test",
},
),
]
def _generate_examples(self, filepath, split):
"""This function returns the examples in the raw form."""
# Images
with open(filepath[0], "rb") as f:
# First 16 bytes contain some metadata
_ = f.read(4)
size = struct.unpack(">I", f.read(4))[0]
_ = f.read(8)
images = np.frombuffer(f.read(), dtype=np.uint8).reshape(size, 28, 28)
# Labels
with open(filepath[1], "rb") as f:
# First 8 bytes contain some metadata
_ = f.read(8)
labels = np.frombuffer(f.read(), dtype=np.uint8)
for idx in range(size):
yield idx, {"image": images[idx], "label": str(labels[idx])}
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