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import datasets
import numpy as np
from PIL import Image
_DSPRITES_URL = "https://github.com/google-deepmind/dsprites-dataset/raw/refs/heads/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz"
_SCREAM_URL = "https://huggingface.co/datasets/randall-lab/dsprites-scream/resolve/main/scream.jpg"
class DSprites(datasets.GeneratorBasedBuilder):
"""Scream dSprites dataset: 3x6x40x32x32 factor combinations, 64x64 RGB images with Scream background."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=(
"Scream dSprites dataset: procedurally generated 2D shapes dataset with known ground-truth factors, "
"commonly used for disentangled representation learning. "
"This is the ScreamDSprites variant, where each object is embedded onto a random Scream image patch "
"with the object pixels inverted. "
"Factors: color (1), shape (3), scale (6), orientation (40), position X (32), position Y (32). "
"Images are 64x64 RGB."
),
features=datasets.Features(
{
"image": datasets.Image(), # (64, 64), RGB
"index": datasets.Value("int32"), # index of the image
"label": datasets.Sequence(datasets.Value("int32")), # 6 factor indices (classes)
"label_values": datasets.Sequence(datasets.Value("float32")), # 6 factor continuous values
"color": datasets.Value("int32"), # color index (always 0)
"shape": datasets.Value("int32"), # shape index (0-2)
"scale": datasets.Value("int32"), # scale index (0-5)
"orientation": datasets.Value("int32"), # orientation index (0-39)
"posX": datasets.Value("int32"), # posX index (0-31)
"posY": datasets.Value("int32"), # posY index (0-31)
"colorValue": datasets.Value("float64"), # color index (always 0)
"shapeValue": datasets.Value("float64"), # shape index (0-2)
"scaleValue": datasets.Value("float64"), # scale index (0-5)
"orientationValue": datasets.Value("float64"), # orientation index (0-39)
"posXValue": datasets.Value("float64"), # posX index (0-31)
"posYValue": datasets.Value("float64"), # posY index (0-31)
}
),
supervised_keys=("image", "label"),
homepage="https://github.com/google-research/disentanglement_lib/tree/master",
license="apache-2.0",
citation="""@inproceedings{locatello2019challenging,
title={Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations},
author={Locatello, Francesco and Bauer, Stefan and Lucic, Mario and Raetsch, Gunnar and Gelly, Sylvain and Sch{\"o}lkopf, Bernhard and Bachem, Olivier},
booktitle={International Conference on Machine Learning},
pages={4114--4124},
year={2019}
}""",
)
def _split_generators(self, dl_manager):
npz_path = dl_manager.download(_DSPRITES_URL)
scream_path = dl_manager.download_and_extract(_SCREAM_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"npz_path": npz_path, "scream_path": scream_path},
),
]
def _generate_examples(self, npz_path, scream_path):
# Load dSprites data
data = np.load(npz_path, allow_pickle=True)
images = data["imgs"] # shape: (737280, 64, 64), uint8
latents_classes = data["latents_classes"] # shape: (737280, 6), int64
latents_values = data["latents_values"] # shape: (737280, 6), float64
# Load Scream image once
scream_img = Image.open(scream_path).convert("RGB")
scream_img = scream_img.resize((350, 274))
scream = np.array(scream_img).astype(np.float32) / 255.0 # (H, W, 3)
# Iterate over images
for idx in range(len(images)):
img = images[idx].astype(np.float32) # (64, 64), float32
# Random scream patch
x_crop = np.random.randint(0, scream.shape[0] - 64)
y_crop = np.random.randint(0, scream.shape[1] - 64)
background_patch = scream[x_crop:x_crop + 64, y_crop:y_crop + 64] # (64, 64, 3)
# Create mask
mask = img == 1
mask = mask[..., None] # (64, 64, 1)
# Invert object region
output_img = np.copy(background_patch)
output_img[mask.squeeze()] = 1.0 - background_patch[mask.squeeze()]
# Convert to RGB PIL
img_pil = Image.fromarray((output_img * 255).astype(np.uint8), mode="RGB")
# Labels
factors_classes = latents_classes[idx].tolist()
factors_values = latents_values[idx].tolist()
yield idx, {
"image": img_pil,
"index": idx,
"label": factors_classes,
"label_values": factors_values,
"color": factors_classes[0],
"shape": factors_classes[1],
"scale": factors_classes[2],
"orientation": factors_classes[3],
"posX": factors_classes[4],
"posY": factors_classes[5],
"colorValue": factors_values[0],
"shapeValue": factors_values[1],
"scaleValue": factors_values[2],
"orientationValue": factors_values[3],
"posXValue": factors_values[4],
"posYValue": factors_values[5],
}
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