fix renaming issues with cams

src/lerobot/scripts/eval.py

@@ -146,7 +146,8 @@ def rollout(
     check_env_attributes_and_types(env)
     while not np.all(done) and step < max_steps:
         # Numpy array to tensor and changing dictionary keys to LeRobot policy format.
-        observation = preprocess_observation(observation)
+        # observation = preprocess_observation(observation)
+        observation = preprocess_observation(observation, cfg=policy.config)
         if return_observations:
             all_observations.append(deepcopy(observation))
 
@@ -159,7 +160,6 @@ def rollout(
         observation = add_envs_task(env, observation)
         with torch.inference_mode():
             action = policy.select_action(observation)
-        observation["observation.images.image"]
         # Convert to CPU / numpy.
         action = action.to("cpu").numpy()
         assert action.ndim == 2, "Action dimensions should be (batch, action_dim)"
@@ -198,7 +198,7 @@ def rollout(
 
     # Track the final observation.
     if return_observations:
-        observation = preprocess_observation(observation)
+        observation = preprocess_observation(observation, cfg=policy.config)
         all_observations.append(deepcopy(observation))
 
     # Stack the sequence along the first dimension so that we have (batch, sequence, *) tensors.

src/lerobot/scripts/train.py

@@ -269,6 +269,7 @@ def train(cfg: TrainPipelineConfig):
                             continue  # Skip the overall stats since we already printed it
                         print(f"\nAggregated Metrics for {task_group}:")
                         print(task_group_info["aggregated"])
+                        breakpoint()
                 else:
                     print("START EVAL")
                     eval_info = eval_policy(
@@ -279,6 +280,7 @@ def train(cfg: TrainPipelineConfig):
                         max_episodes_rendered=4,
                         start_seed=cfg.seed,
                     )
+                    breakpoint()
                     aggregated = eval_info["aggregated"]
                     print("END EVAL")