feat(dataset): registering torchvision transforms (#3153)

* add: a flexible transformation registry

* fix: image transforms can be set both at init and after

* add: tests

* fix: take in review

* feat(datasets): add image transform setters

* fix: pre-commit

* fix: CI

---------

Signed-off-by: Francesco Capuano <74058581+fracapuano@users.noreply.github.com>

src/lerobot/datasets/lerobot_dataset.py

@@ -151,9 +151,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
                 ``$HF_LEROBOT_HOME/hub``.
             episodes (list[int] | None, optional): If specified, this will only load episodes specified by
                 their episode_index in this list. Defaults to None.
-            image_transforms (Callable | None, optional): You can pass standard v2 image transforms from
-                torchvision.transforms.v2 here which will be applied to visual modalities (whether they come
-                from videos or images). Defaults to None.
+            image_transforms (Callable | None, optional):
+                Transform applied to visual modalities inside `__getitem__` after image decoding / tensor
+                conversion. This works for both image-backed and video-backed observations and can later be
+                updated with `set_image_transforms()` or cleared with `clear_image_transforms()`.
+                Defaults to None.
             delta_timestamps (dict[list[float]] | None, optional): _description_. Defaults to None.
             tolerance_s (float, optional): Tolerance in seconds used to ensure data timestamps are actually in
                 sync with the fps value. It is used at the init of the dataset to make sure that each
@@ -192,7 +194,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         super().__init__()
         self.repo_id = repo_id
         self._requested_root = Path(root) if root else None
-        self.image_transforms = image_transforms
+        self.reader = None
+        self.set_image_transforms(image_transforms)
         self.delta_timestamps = delta_timestamps
         self.episodes = episodes
         self.tolerance_s = tolerance_s
@@ -475,6 +478,18 @@ class LeRobotDataset(torch.utils.data.Dataset):
             f"}})"
         )
 
+    def set_image_transforms(self, image_transforms: Callable | None) -> None:
+        """Replace the transform applied to visual observations."""
+        if image_transforms is not None and not callable(image_transforms):
+            raise TypeError("image_transforms must be callable or None.")
+        self.image_transforms = image_transforms
+        if self.reader is not None:
+            self.reader._image_transforms = image_transforms
+
+    def clear_image_transforms(self) -> None:
+        """Remove the transform applied to visual observations."""
+        self.set_image_transforms(None)
+
     # ── Hub methods (stay on facade) ──────────────────────────────────
 
     def push_to_hub(

src/lerobot/datasets/multi_dataset.py

@@ -89,12 +89,24 @@ class MultiLeRobotDataset(torch.utils.data.Dataset):
                 )
                 self.disabled_features.update(extra_keys)
 
-        self.image_transforms = image_transforms
         self.delta_timestamps = delta_timestamps
         # TODO(rcadene, aliberts): We should not perform this aggregation for datasets
         # with multiple robots of different ranges. Instead we should have one normalization
         # per robot.
         self.stats = aggregate_stats([dataset.meta.stats for dataset in self._datasets])
+        self.set_image_transforms(image_transforms)
+
+    def set_image_transforms(self, image_transforms: Callable | None) -> None:
+        """Replace the transform for this dataset and its children."""
+        if image_transforms is not None and not callable(image_transforms):
+            raise TypeError("image_transforms must be callable or None.")
+        self.image_transforms = image_transforms
+        for dataset in getattr(self, "_datasets", []):
+            dataset.set_image_transforms(self.image_transforms)
+
+    def clear_image_transforms(self) -> None:
+        """Remove the transform from this dataset and its children."""
+        self.set_image_transforms(None)
 
     @property
     def repo_id_to_index(self):

tests/datasets/test_datasets.py

@@ -24,6 +24,7 @@ import torch
 from huggingface_hub import HfApi
 from PIL import Image
 from safetensors.torch import load_file
+from torchvision.transforms import v2
 
 import lerobot
 from lerobot.configs.default import DatasetConfig
@@ -34,6 +35,7 @@ from lerobot.datasets.image_writer import image_array_to_pil_image
 from lerobot.datasets.io_utils import hf_transform_to_torch
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.datasets.multi_dataset import MultiLeRobotDataset
+from lerobot.datasets.transforms import ImageTransforms, ImageTransformsConfig
 from lerobot.datasets.utils import (
     DEFAULT_CHUNK_SIZE,
     DEFAULT_DATA_FILE_SIZE_IN_MB,
@@ -355,6 +357,62 @@ def test_add_frame_image_pil(image_dataset):
     assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
 
 
+def test_set_image_transforms_applies_transparently(image_dataset):
+    dataset = image_dataset
+    dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
+    dataset.save_episode()
+    dataset.finalize()
+
+    dataset.set_image_transforms(v2.Resize((224, 224)))
+    assert dataset[0]["image"].shape == torch.Size((3, 224, 224))
+
+    dataset.set_image_transforms(v2.Resize((128, 128)))
+    assert dataset[0]["image"].shape == torch.Size((3, 128, 128))
+
+    dataset.clear_image_transforms()
+    assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
+
+
+def test_set_image_transforms_supports_lerobot_image_transforms(image_dataset):
+    dataset = image_dataset
+    dataset.add_frame({"image": np.random.rand(*DUMMY_CHW), "task": "Dummy task"})
+    dataset.save_episode()
+    dataset.finalize()
+
+    image_transforms = ImageTransforms(ImageTransformsConfig(enable=False))
+    dataset.set_image_transforms(image_transforms)
+
+    assert dataset.image_transforms is image_transforms
+    assert dataset[0]["image"].shape == torch.Size(DUMMY_CHW)
+
+
+def test_set_image_transforms_supports_loaded_dataset(tmp_path, lerobot_dataset_factory):
+    dataset = lerobot_dataset_factory(root=tmp_path / "test", use_videos=False)
+    dataset.set_image_transforms(v2.Compose([v2.Resize((224, 224)), v2.Resize((112, 112))]))
+
+    camera_key = dataset.meta.camera_keys[0]
+    assert dataset[0][camera_key].shape == torch.Size((3, 112, 112))
+
+
+def test_multilerobot_dataset_set_image_transforms_propagates(tmp_path, lerobot_dataset_factory):
+    root = tmp_path / "multi"
+    repo_ids = ["lerobot/test_multi_a", "lerobot/test_multi_b"]
+
+    for repo_id in repo_ids:
+        lerobot_dataset_factory(root=root / repo_id, repo_id=repo_id, use_videos=False)
+
+    dataset = MultiLeRobotDataset(repo_ids, root=root, download_videos=False)
+    dataset.set_image_transforms(v2.Resize((96, 96)))
+
+    camera_key = dataset.camera_keys[0]
+    assert dataset[0][camera_key].shape == torch.Size((3, 96, 96))
+    assert all(child.image_transforms is dataset.image_transforms for child in dataset._datasets)
+
+    dataset.clear_image_transforms()
+    assert dataset.image_transforms is None
+    assert all(child.image_transforms is None for child in dataset._datasets)
+
+
 def test_image_array_to_pil_image_wrong_range_float_0_255():
     image = np.random.rand(*DUMMY_HWC) * 255
     with pytest.raises(ValueError):