refactor: several fixes

src/lerobot/utils/import_utils.py

@@ -70,10 +70,11 @@ def is_package_available(
 
 
 def get_safe_default_codec():
+    logger = logging.getLogger(__name__)
     if importlib.util.find_spec("torchcodec"):
         return "torchcodec"
     else:
-        logging.warning(
+        logger.warning(
             "'torchcodec' is not available in your platform, falling back to 'pyav' as a default decoder"
         )
         return "pyav"

src/lerobot/utils/io_utils.py

@@ -14,9 +14,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import json
+import logging
 from pathlib import Path
 from typing import Any
 
+logger = logging.getLogger(__name__)
+
 JsonLike = str | int | float | bool | None | list["JsonLike"] | dict[str, "JsonLike"] | tuple["JsonLike", ...]
 
 
@@ -58,10 +61,18 @@ def write_video(video_path: str | Path, stacked_frames: list, fps: int) -> None:
     import av
 
     with av.open(str(video_path), mode="w") as container:
-        height, width = stacked_frames[0].shape[:2]
-        # Ensure dimensions are even for yuv420p compatibility
-        height = height if height % 2 == 0 else height - 1
-        width = width if width % 2 == 0 else width - 1
+        orig_height, orig_width = stacked_frames[0].shape[:2]
+        # yuv420p requires even dimensions; crop by one pixel if needed
+        height = orig_height if orig_height % 2 == 0 else orig_height - 1
+        width = orig_width if orig_width % 2 == 0 else orig_width - 1
+        if height != orig_height or width != orig_width:
+            logger.warning(
+                "Frame dimensions %dx%d are not even; cropping to %dx%d for yuv420p compatibility.",
+                orig_width,
+                orig_height,
+                width,
+                height,
+            )
         stream = container.add_stream("libx264", rate=fps)
         stream.width = width
         stream.height = height

src/lerobot/utils/utils.py

@@ -22,11 +22,12 @@ import select
 import subprocess
 import sys
 import time
+from collections.abc import Iterator
 from copy import copy, deepcopy
 from datetime import datetime
 from pathlib import Path
 from statistics import mean
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 
@@ -252,6 +253,27 @@ def unflatten_dict(d: dict, sep: str = "/") -> dict:
     return outdict
 
 
+def cycle(iterable: Any) -> Iterator[Any]:
+    """Create a dataloader-safe cyclical iterator.
+
+    This is an equivalent of `itertools.cycle` but is safe for use with
+    PyTorch DataLoaders with multiple workers.
+    See https://github.com/pytorch/pytorch/issues/23900 for details.
+
+    Args:
+        iterable: The iterable to cycle over.
+
+    Yields:
+        Items from the iterable, restarting from the beginning when exhausted.
+    """
+    iterator = iter(iterable)
+    while True:
+        try:
+            yield next(iterator)
+        except StopIteration:
+            iterator = iter(iterable)
+
+
 class SuppressProgressBars:
     """
     Context manager to suppress progress bars.