remove unces

src/lerobot/datasets/lerobot_dataset.py

@@ -471,7 +471,8 @@ class LeRobotDataset(torch.utils.data.Dataset):
         if self.episodes is not None and self.meta._version >= packaging.version.parse("v2.1"):
             episodes_stats = [self.meta.episodes_stats[ep_idx] for ep_idx in self.episodes]
             self.stats = aggregate_stats(episodes_stats)
-
+        
+        # Load actual data
         try:
             if force_cache_sync:
                 raise FileNotFoundError

src/lerobot/envs/utils.py

@@ -80,59 +80,6 @@ def preprocess_observation(observations: dict[str, np.ndarray]) -> dict[str, Ten
 
     return return_observations
 
-
-def preprocess_observation1(
-    observations: dict[str, np.ndarray], cfg: dict[str, Any] = None
-) -> dict[str, Tensor]:
-    # TODO(aliberts, rcadene): refactor this to use features from the environment (no hardcoding)
-    """Convert environment observation to LeRobot format observation.
-    Args:
-        observation: Dictionary of observation batches from a Gym vector environment.
-    Returns:
-        Dictionary of observation batches with keys renamed to LeRobot format and values as tensors.
-    """
-    # map to expected inputs for the policy
-    return_observations = {}
-    image_key = list(cfg.image_features.keys())[0] if cfg else "observation.image"
-    state_key = cfg.robot_state_feature_key if cfg else "observation.state"
-    if "pixels" in observations:
-        if isinstance(observations["pixels"], dict):
-            # imgs = {f"{image_key}.{key}": img for key, img in observations["pixels"].items()}
-            imgs = observations["pixels"]  # keys should be OBS_IMAGE, OBS_IMAGE_2, OBS_IMAGE_3
-        else:
-            imgs = {f"{image_key}": observations["pixels"]}
-
-        for imgkey, img in imgs.items():
-            # TODO(aliberts, rcadene): use transforms.ToTensor()?
-            img = torch.from_numpy(img)
-
-            # sanity check that images are channel last
-            _, h, w, c = img.shape
-            assert c < h and c < w, f"expect channel last images, but instead got {img.shape=}"
-
-            # sanity check that images are uint8
-            assert img.dtype == torch.uint8, f"expect torch.uint8, but instead {img.dtype=}"
-
-            # convert to channel first of type float32 in range [0,1]
-            img = einops.rearrange(img, "b h w c -> b c h w").contiguous()
-            img = img.type(torch.float32)
-            img /= 255
-
-            return_observations[imgkey] = img
-
-    if "environment_state" in observations:
-        return_observations["observation.environment_state"] = torch.from_numpy(
-            observations["environment_state"]
-        ).float()
-
-    # TODO(rcadene): enable pixels only baseline with `obs_type="pixels"` in environment by removing
-    # requirement for "agent_pos"
-    return_observations[state_key] = torch.from_numpy(observations["agent_pos"]).float()
-    if "task" in observations:
-        return_observations["task"] = observations["task"]
-    return return_observations
-
-
 def env_to_policy_features(env_cfg: EnvConfig) -> dict[str, PolicyFeature]:
     # TODO(aliberts, rcadene): remove this hardcoding of keys and just use the nested keys as is
     # (need to also refactor preprocess_observation and externalize normalization from policies)

src/lerobot/scripts/eval.py

@@ -155,7 +155,6 @@ def rollout(
     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_observation1(observation)
         if return_observations:
             all_observations.append(deepcopy(observation))