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import numpy as np
import datasets
from sklearn.model_selection import train_test_split
class DSprites(datasets.GeneratorBasedBuilder):
"""TODO: Short description of my dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
features = datasets.Features(
{
"image": datasets.Image(),
"orientation": datasets.Value("float"),
"shape": datasets.ClassLabel(names=["square", "ellipse", "heart"]),
"scale": datasets.Value("float"),
"color": datasets.ClassLabel(names=["white"]),
"position_x": datasets.Value("float"),
"position_y": datasets.Value("float"),
}
)
homepage = "https://github.com/deepmind/dsprites-dataset"
license = "zlib/libpng"
return datasets.DatasetInfo(
description="""dSprites is a dataset of 2D shapes procedurally generated from 6 ground truth independent latent factors. These factors are color, shape, scale, rotation, x and y positions of a sprite.
All possible combinations of these latents are present exactly once, generating N = 737280 total images.""",
features=features,
supervised_keys=("image", "shape"),
homepage=homepage,
license=license,
citation="""@misc{dsprites17,
author = {Loic Matthey and Irina Higgins and Demis Hassabis and Alexander Lerchner},
title = {dSprites: Disentanglement testing Sprites dataset},
howpublished= {https://github.com/deepmind/dsprites-dataset/},
year = "2017"}""",
)
def _split_generators(self, dl_manager):
archive = dl_manager.download(
"https://github.com/google-deepmind/dsprites-dataset/raw/refs/heads/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz"
)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"archive": archive, "split": "train"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"archive": archive, "split": "test"},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, archive, split):
dataset_zip = np.load(archive, allow_pickle=True)
images = dataset_zip["imgs"]
latents_values = dataset_zip["latents_values"]
# Split the indices for train and test
indices = np.arange(len(images))
train_indices, test_indices = train_test_split(indices, test_size=0.3, random_state=42)
if split == "train":
selected_indices = train_indices
elif split == "test":
selected_indices = test_indices
for key in selected_indices:
yield int(key), { # Ensure the key is a Python native int
"image": images[key],
"color": int(latents_values[key, 0]) - 1,
"shape": int(latents_values[key, 1]) - 1,
"scale": latents_values[key, 2],
"orientation": latents_values[key, 3],
"position_x": latents_values[key, 4],
"position_y": latents_values[key, 5],
}
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