Create dsprites.py

dsprites.py

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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],
+            }