chore(format): formatting code

src/lerobot/datasets/compute_stats.py

@@ -531,9 +531,12 @@ def compute_episode_stats(
         )
 
         if features[key]["dtype"] in ["image", "video"]:
-            normalization_factor = 255.0 if not (features[key].get("info") or {}).get("is_depth_map", False) else 1.0
+            normalization_factor = (
+                255.0 if not (features[key].get("info") or {}).get("is_depth_map", False) else 1.0
+            )
             ep_stats[key] = {
-                k: v if k == "count" else np.squeeze(v / normalization_factor, axis=0) for k, v in ep_stats[key].items()
+                k: v if k == "count" else np.squeeze(v / normalization_factor, axis=0)
+                for k, v in ep_stats[key].items()
             }
 
     return ep_stats

src/lerobot/datasets/dataset_tools.py

@@ -37,7 +37,13 @@ import pyarrow.parquet as pq
 import torch
 from tqdm import tqdm
 
-from lerobot.configs import VideoEncoderConfig, camera_encoder_defaults, DepthEncoderConfig, encoder_config_from_video_info, depth_encoder_defaults
+from lerobot.configs import (
+    DepthEncoderConfig,
+    VideoEncoderConfig,
+    camera_encoder_defaults,
+    depth_encoder_defaults,
+    encoder_config_from_video_info,
+)
 from lerobot.configs.video import DEPTH_ENCODER_INFO_FIELD_NAMES
 from lerobot.utils.constants import ACTION, HF_LEROBOT_HOME, OBS_IMAGE, OBS_STATE
 from lerobot.utils.utils import flatten_dict
@@ -1617,7 +1623,7 @@ def recompute_stats(
         raise ValueError(f"No parquet files found in {data_dir}")
 
     all_episode_stats = []
-    #TODO: enable image and video stats re-computation
+    # TODO: enable image and video stats re-computation
     numeric_keys = [k for k, v in features_to_compute.items() if v["dtype"] not in ["image", "video"]]
 
     for parquet_path in tqdm(parquet_files, desc="Computing stats from data files"):
@@ -1717,9 +1723,7 @@ def convert_image_to_video_dataset(
     logging.info(
         f"Converting {len(episode_indices)} episodes with {len(img_keys)} cameras from {dataset.repo_id}"
     )
-    logging.info(
+    logging.info(f"RGB video encoder: {camera_encoder}, depth video encoder: {depth_encoder}")
-        f"RGB video encoder: {camera_encoder}, depth video encoder: {depth_encoder}"
-    )
 
     # Create new features dict, converting image features to video features
     new_features = {}
@@ -1973,7 +1977,11 @@ def reencode_dataset(
         return dataset
     logging.info(f"Re-encoding {sum(len(paths) for paths in video_keys_paths_dict.values())} video file(s).")
 
-    worker_args = [(path, encoder, encoder_threads) for video_key, encoder in video_keys_encoders_dict.items() for path in video_keys_paths_dict[video_key]]
+    worker_args = [
+        (path, encoder, encoder_threads)
+        for video_key, encoder in video_keys_encoders_dict.items()
+        for path in video_keys_paths_dict[video_key]
+    ]
     if num_workers and num_workers > 1:
         with ProcessPoolExecutor(max_workers=num_workers) as pool:
             futures = [pool.submit(_reencode_video_worker, args) for args in worker_args]
@@ -1992,9 +2000,7 @@ def reencode_dataset(
     depth_preserve_keys = {"is_depth_map", *(f"video.{n}" for n in DEPTH_ENCODER_INFO_FIELD_NAMES)}
     for video_key, encoder in video_keys_encoders_dict.items():
         preserve_keys = depth_preserve_keys if video_key in meta.depth_keys else None
-        meta.update_video_info(
+        meta.update_video_info(video_key=video_key, video_encoder=encoder, preserve_keys=preserve_keys)
-            video_key=video_key, video_encoder=encoder, preserve_keys=preserve_keys
-        )
 
     write_info(meta.info, meta.root)
     logging.info("Dataset metadata updated.")

src/lerobot/datasets/dataset_writer.py

@@ -303,7 +303,9 @@ class DatasetWriter:
                 temp_path, video_stats = streaming_results[video_key]
                 if video_stats is not None:
                     ep_stats[video_key] = {
-                        k: v if k == "count" else np.squeeze(v.reshape(1, -1, 1, 1) / normalization_factor, axis=0)
+                        k: v
+                        if k == "count"
+                        else np.squeeze(v.reshape(1, -1, 1, 1) / normalization_factor, axis=0)
                         for k, v in video_stats.items()
                     }
                 ep_metadata.update(self._save_episode_video(video_key, episode_index, temp_path=temp_path))

src/lerobot/datasets/depth_utils.py

@@ -207,7 +207,6 @@ def dequantize_depth(
 
     # ── Torch path: stay on the input device, single fp32 allocation. ────────
     if isinstance(quantized, torch.Tensor):
-
         if quantized.ndim >= 3:
             # Drop the single-channel dimension so the math runs on (..., H, W).
             quantized = quantized.squeeze(-3) if quantized.shape[-3] == 1 else quantized.squeeze(-1)

src/lerobot/datasets/video_utils.py

@@ -82,17 +82,23 @@ def decode_video_frames(
     if backend != "pyav" and is_depth:
         logger.warning("Decoding depth maps is only supported with the 'pyav' backend.")
         # We do not actually return uint8 here, but we avoid the 255 normalization step.
-        return decode_video_frames_pyav(video_path, timestamps, tolerance_s, return_uint8=False, is_depth=True)
+        return decode_video_frames_pyav(
+            video_path, timestamps, tolerance_s, return_uint8=False, is_depth=True
+        )
 
     if backend is None:
         backend = get_safe_default_video_backend()
     if backend == "torchcodec":
         return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s, return_uint8=return_uint8)
     elif backend == "pyav":
-        return decode_video_frames_pyav(video_path, timestamps, tolerance_s, return_uint8=return_uint8, is_depth=is_depth)
+        return decode_video_frames_pyav(
+            video_path, timestamps, tolerance_s, return_uint8=return_uint8, is_depth=is_depth
+        )
     elif backend == "video_reader":
         logger.warning("backend='video_reader' is deprecated and now aliases to 'pyav'.")
-        return decode_video_frames_pyav(video_path, timestamps, tolerance_s, return_uint8=return_uint8, is_depth=is_depth)
+        return decode_video_frames_pyav(
+            video_path, timestamps, tolerance_s, return_uint8=return_uint8, is_depth=is_depth
+        )
     else:
         raise ValueError(f"Unsupported video backend: {backend}")
 
@@ -483,7 +489,7 @@ def encode_video_frames(
                         shift=video_encoder.shift,
                         use_log=video_encoder.use_log,
                         pix_fmt=video_encoder.pix_fmt,
-                        video_backend="pyav"
+                        video_backend="pyav",
                     )
                 else:
                     input_image = input_image.convert("RGB")

tests/datasets/test_aggregate.py

@@ -30,10 +30,8 @@ from lerobot.datasets.aggregate import aggregate_datasets
 from lerobot.datasets.feature_utils import features_equal_for_merge
 from lerobot.datasets.lerobot_dataset import LeRobotDataset
 from tests.fixtures.constants import (
-    DUMMY_CAMERA_FEATURES,
-    DUMMY_DEPTH_CAMERA_FEATURES,
-    DUMMY_REPO_ID,
     DUMMY_CAMERA_FEATURES_WITH_DEPTH,
+    DUMMY_REPO_ID,
 )
 
 CAMERA_FEATURES_WITH_DEPTH = {**DUMMY_CAMERA_FEATURES, **DUMMY_DEPTH_CAMERA_FEATURES}
@@ -235,9 +233,7 @@ def assert_depth_keys_preserved(aggr_ds, ds_0, ds_1):
     )
     for key in expected_depth_keys:
         info = aggr_ds.meta.info.features[key].get("info") or {}
-        assert info.get("is_depth_map") is True, (
+        assert info.get("is_depth_map") is True, f"Depth marker lost on feature {key!r} after aggregation"
-            f"Depth marker lost on feature {key!r} after aggregation"
-        )
 
 
 def assert_video_timestamps_within_bounds(aggr_ds):

tests/datasets/test_depth.py

@@ -26,15 +26,16 @@ from lerobot.datasets.image_writer import image_array_to_pil_image, write_image
 from tests.fixtures.constants import (
     DEFAULT_FPS,
     DUMMY_CAMERA_FEATURES,
+    DUMMY_CAMERA_FEATURES_WITH_DEPTH,
+    DUMMY_CHW,
     DUMMY_DEPTH_CAMERA_FEATURES,
     DUMMY_REPO_ID,
-    DUMMY_CAMERA_FEATURES_WITH_DEPTH,
-    DUMMY_CHW
 )
 from tests.fixtures.dataset_factories import add_frames
 
 _, H, W = DUMMY_CHW
 
+
 def _depth_metres_ramp() -> np.ndarray:
     """Linearly-spaced float32 depth in metres covering the default range."""
     return np.linspace(DEFAULT_DEPTH_MIN, DEFAULT_DEPTH_MAX, H * W, dtype=np.float32).reshape(H, W)
@@ -98,9 +99,7 @@ class TestQuantizeDequantize:
         # ``m``: float32 noise (~10 µm in log mode, after ``exp``) — still 200× below the ~2 mm quant step.
         # ``mm`` + tensor stays in float32 (no uint16 round-trip), so allow 1 mm slop.
         atol = 1e-5 if output_unit == "m" else 1.0
-        np.testing.assert_allclose(
+        np.testing.assert_allclose(out.cpu().numpy().astype(np.float64), ref.astype(np.float64), atol=atol)
-            out.cpu().numpy().astype(np.float64), ref.astype(np.float64), atol=atol
-        )
 
     @pytest.mark.parametrize(
         "input_shape,output_shape",
@@ -131,9 +130,7 @@ class TestQuantizeDequantize:
 
     def test_invalid_log_params_raises(self):
         with pytest.raises(ValueError, match=r"depth_min \+ shift must be positive"):
-            quantize_depth(
+            quantize_depth(_depth_metres_ramp(), depth_min=1.0, shift=-2.0, use_log=True, video_backend=None)
-                _depth_metres_ramp(), depth_min=1.0, shift=-2.0, use_log=True, video_backend=None
-            )
 
 
 # ── 2. Image writer depth support ─────────────────────────────────────