Upload mpi3d-toy.py

mpi3d-toy.py

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+import datasets
+import numpy as np
+import os
+from PIL import Image
+
+_MPI3D_URL = "https://huggingface.co/datasets/waleedgondal/mpi3d/resolve/main/mpi3d_toy.npz"
+
+class MPI3DToy(datasets.GeneratorBasedBuilder):
+    """MPI3D Toy dataset: 6x6x2x3x3x40x40 factor combinations, 64x64 RGB images."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=(
+                "MPI3D Toy dataset: synthetic images of physical 3D objects with "
+                "7 known factors of variation. "
+                "Images are 64x64 RGB. "
+                "Factors: object color (6), object shape (6), object size (2), camera height (3), "
+                "background color (3), robotic arm DOF1 (40), robotic arm DOF2 (40). "
+                "The images are ordered as the Cartesian product of the factors in row-major order."
+            ),
+            features=datasets.Features(
+                {
+                    "image": datasets.Image(),    # (64, 64, 3)
+                    "index": datasets.Value("int32"),  # index of the image
+                    "label": datasets.Sequence(datasets.Value("int32")),  # 7 factor indices
+                    "color": datasets.Value("int32"),  # object color index (0-5)
+                    "shape": datasets.Value("int32"),  # object shape index (0-5)
+                    "size": datasets.Value("int32"),  # object size index (0-1)
+                    "height": datasets.Value("int32"),  # camera height index (0-2)
+                    "background": datasets.Value("int32"),  # background color index (0-2)
+                    "dof1": datasets.Value("int32"),  # robotic arm DOF1 index (0-39)
+                    "dof2": datasets.Value("int32"),  # robotic arm DOF2 index (0-39)
+                }
+            ),
+            supervised_keys=("image", "label"),
+            homepage="https://github.com/rr-learning/disentanglement_dataset",
+            license="Creative Commons Attribution 4.0 International",
+            citation="""@article{gondal2019transfer,
+  title={On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset},
+  author={Gondal, Muhammad Waleed and Wuthrich, Manuel and Miladinovic, Djordje and Locatello, Francesco and Breidt, Martin and Volchkov, Valentin and Akpo, Joel and Bachem, Olivier and Sch{\"o}lkopf, Bernhard and Bauer, Stefan},
+  journal={Advances in Neural Information Processing Systems},
+  volume={32},
+  year={2019}
+}""",
+        )
+
+    def _split_generators(self, dl_manager):
+        npz_path = dl_manager.download(_MPI3D_URL)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={"npz_path": npz_path},
+            ),
+        ]
+
+    def _generate_examples(self, npz_path):
+        # Load npz
+        data = np.load(npz_path)
+        images = data["images"]  # shape: (1036800, 64, 64, 3)
+
+        factor_sizes = np.array([6, 6, 2, 3, 3, 40, 40])
+        factor_bases = np.cumprod([1] + list(factor_sizes[::-1]))[::-1][1:]
+
+        def index_to_factors(index):
+            factors = []
+            for base, size in zip(factor_bases, factor_sizes):
+                factor = (index // base) % size
+                factors.append(int(factor))
+            return factors
+
+        # Iterate over images
+        for idx in range(len(images)):
+            img = images[idx]
+            img_pil = Image.fromarray(img)
+
+            factors = index_to_factors(idx)
+
+            yield idx, {
+                "image": img_pil,
+                "index": idx,
+                "label": factors,
+                "color": factors[0],
+                "shape": factors[1],
+                "size": factors[2],
+                "height": factors[3],
+                "background": factors[4],
+                "dof1": factors[5],
+                "dof2": factors[6],
+            }