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examples/1_load_lerobot_dataset.py

@@ -43,10 +43,7 @@ pprint(lerobot.available_datasets)
 
 # You can also browse through the datasets created/ported by the community on the hub using the hub api:
 hub_api = HfApi()
-repo_ids = [
-    info.id
-    for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])
-]
+repo_ids = [info.id for info in hub_api.list_datasets(task_categories="robotics", tags=["LeRobot"])]
 pprint(repo_ids)
 
 # Or simply explore them in your web browser directly at:
@@ -61,9 +58,7 @@ ds_meta = LeRobotDatasetMetadata(repo_id)
 # structure of the dataset without downloading the actual data yet (only metadata files — which are
 # lightweight).
 print(f"Total number of episodes: {ds_meta.total_episodes}")
-print(
-    f"Average number of frames per episode: {ds_meta.total_frames / ds_meta.total_episodes:.3f}"
-)
+print(f"Average number of frames per episode: {ds_meta.total_frames / ds_meta.total_episodes:.3f}")
 print(f"Frames per second used during data collection: {ds_meta.fps}")
 print(f"Robot type: {ds_meta.robot_type}")
 print(f"keys to access images from cameras: {ds_meta.camera_keys=}\n")

examples/3_train_policy.py

@@ -51,18 +51,12 @@ def main():
     #   - dataset stats: for normalization and denormalization of input/outputs
     dataset_metadata = LeRobotDatasetMetadata("lerobot/pusht")
     features = dataset_to_policy_features(dataset_metadata.features)
-    output_features = {
-        key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION
-    }
-    input_features = {
-        key: ft for key, ft in features.items() if key not in output_features
-    }
+    output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}
+    input_features = {key: ft for key, ft in features.items() if key not in output_features}
 
     # Policies are initialized with a configuration class, in this case `DiffusionConfig`. For this example,
     # we'll just use the defaults and so no arguments other than input/output features need to be passed.
-    cfg = DiffusionConfig(
-        input_features=input_features, output_features=output_features
-    )
+    cfg = DiffusionConfig(input_features=input_features, output_features=output_features)
 
     # We can now instantiate our policy with this config and the dataset stats.
     policy = DiffusionPolicy(cfg, dataset_stats=dataset_metadata.stats)
@@ -72,12 +66,8 @@ def main():
     # Another policy-dataset interaction is with the delta_timestamps. Each policy expects a given number frames
     # which can differ for inputs, outputs and rewards (if there are some).
     delta_timestamps = {
-        "observation.image": [
-            i / dataset_metadata.fps for i in cfg.observation_delta_indices
-        ],
-        "observation.state": [
-            i / dataset_metadata.fps for i in cfg.observation_delta_indices
-        ],
+        "observation.image": [i / dataset_metadata.fps for i in cfg.observation_delta_indices],
+        "observation.state": [i / dataset_metadata.fps for i in cfg.observation_delta_indices],
         "action": [i / dataset_metadata.fps for i in cfg.action_delta_indices],
     }
 
@@ -129,10 +119,7 @@ def main():
     done = False
     while not done:
         for batch in dataloader:
-            batch = {
-                k: (v.to(device) if isinstance(v, torch.Tensor) else v)
-                for k, v in batch.items()
-            }
+            batch = {k: (v.to(device) if isinstance(v, torch.Tensor) else v) for k, v in batch.items()}
             loss, _ = policy.forward(batch)
             loss.backward()
             optimizer.step()

examples/advanced/1_add_image_transforms.py

@@ -48,14 +48,10 @@ transforms = v2.Compose(
 )
 
 # Create another LeRobotDataset with the defined transformations
-transformed_dataset = LeRobotDataset(
-    dataset_repo_id, episodes=[0], image_transforms=transforms
-)
+transformed_dataset = LeRobotDataset(dataset_repo_id, episodes=[0], image_transforms=transforms)
 
 # Get a frame from the transformed dataset
-transformed_frame = transformed_dataset[first_idx][
-    transformed_dataset.meta.camera_keys[0]
-]
+transformed_frame = transformed_dataset[first_idx][transformed_dataset.meta.camera_keys[0]]
 
 # Create a directory to store output images
 output_dir = Path("outputs/image_transforms")

examples/advanced/2_calculate_validation_loss.py

@@ -90,9 +90,7 @@ def main():
     train_dataset = LeRobotDataset(
         "lerobot/pusht", episodes=train_episodes, delta_timestamps=delta_timestamps
     )
-    val_dataset = LeRobotDataset(
-        "lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps
-    )
+    val_dataset = LeRobotDataset("lerobot/pusht", episodes=val_episodes, delta_timestamps=delta_timestamps)
     print(f"Number of frames in training dataset (90% subset): {len(train_dataset)}")
     print(f"Number of frames in validation dataset (10% subset): {len(val_dataset)}")