try fix 5

src/lerobot/datasets/lerobot_dataset.py

@@ -80,30 +80,24 @@ from lerobot.utils.constants import HF_LEROBOT_HOME
 CODEBASE_VERSION = "v3.0"
 
 
-def hf_transform_to_torch(items_dict: dict[str, list[Any]]) -> dict[str, list[torch.Tensor | str]]:
-    """Convert a batch from a Hugging Face dataset to torch tensors.
-
-    This transform function converts items from Hugging Face dataset format (pyarrow)
-    to torch tensors. Importantly, images are converted from PIL objects (H, W, C, uint8)
-    to a torch image representation (C, H, W, float32) in the range [0, 1]. Other
-    types are converted to torch.tensor.
-
-    Args:
-        items_dict (dict): A dictionary representing a batch of data from a
-            Hugging Face dataset.
-
-    Returns:
-        dict: The batch with items converted to torch tensors.
+def hf_transform_to_torch(items_dict: dict[str, Any]) -> dict[str, torch.Tensor | str]:
+    """Get a transform function that convert items from Hugging Face dataset (pyarrow)
+    to torch tensors. ...
+    [This is the v2.1 item-level transform]
     """
     for key in items_dict:
-        first_item = items_dict[key][0]
-        if isinstance(first_item, PILImage.Image):
+        if items_dict[key] is None:
+            continue
+        if isinstance(items_dict[key], PILImage.Image):
+            # PIL image (h w c) (uint8)
             to_tensor = transforms.ToTensor()
-            items_dict[key] = [to_tensor(img) for img in items_dict[key]]
-        elif first_item is None:
+            items_dict[key] = to_tensor(items_dict[key])
+        elif isinstance(items_dict[key], str):
+            # keep as is
             pass
         else:
-            items_dict[key] = [x if isinstance(x, str) else torch.tensor(x) for x in items_dict[key]]
+            # This handles tensors, ints, floats, etc.
+            items_dict[key] = torch.tensor(items_dict[key])
     return items_dict