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# coding=utf-8
"""MNIST Point Cloud Data Set"""
import struct
import numpy as np
import datasets
_VERSION = "0.0.3"
# _CITATION = """\
# @article{lecun2010mnist,
# title={MNIST handwritten digit database},
# author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
# journal={ATT Labs [Online]. Available: http://yann.lecun.com/exdb/mnist},
# volume={2},
# year={2010}
# }
# """
_DESCRIPTION = """\
The MNIST dataset consists of 70,000 28x28 black-and-white points in 10 classes (one for each digits), with 7,000
points per class. There are 60,000 training points and 10,000 test points.
"""
_URL = f"https://huggingface.co/datasets/cgarciae/point-cloud-mnist/resolve/{_VERSION}/data/"
_URLS = {
"train_points": "X_train.npy",
"train_labels": "y_train.npy",
"test_points": "X_test.npy",
"test_labels": "y_test.npy",
}
class MNIST(datasets.GeneratorBasedBuilder):
"""MNIST Data Set"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="point-cloud-mnist",
version=datasets.Version(_VERSION),
description=_DESCRIPTION,
)
]
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"points": datasets.Array2D(shape=(351, 3), dtype="uint8"),
"label": datasets.features.ClassLabel(
names=["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
),
}
),
supervised_keys=("points", "label"),
# homepage="http://yann.lecun.com/exdb/mnist/",
# citation=_CITATION,
)
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_points"],
downloaded_files["train_labels"],
],
"split": "train",
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"filepath": [
downloaded_files["test_points"],
downloaded_files["test_labels"],
],
"split": "test",
},
),
]
def _generate_examples(self, filepath, split):
"""This function returns the examples in the raw form."""
# points
with open(filepath[0], "rb") as f:
points = np.load(f)
# Labels
with open(filepath[1], "rb") as f:
labels = np.load(f)
for idx in range(len(points)):
yield (
idx,
{
"points": points[idx],
"label": str(labels[idx]),
},
)
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