remove

src/lerobot/policies/smolvla/smolvlm_with_expert.py

@@ -78,7 +78,6 @@ class SmolVLMWithExpertModel(nn.Module):
                 model_id,
                 device_map="auto",
                 torch_dtype="bfloat16",
-                # torch_dtype=torch.float16,
                 low_cpu_mem_usage=True,
             )
             config = self.vlm.config

src/lerobot/scripts/eval.py

@@ -465,48 +465,6 @@ def _compile_episode_data(
 
     return data_dict
 
-
-from lerobot.datasets.lerobot_dataset import LeRobotDatasetMetadata
-from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
-
-
-def _inject_normalization_stats(policy: SmolVLAPolicy, dataset_meta: LeRobotDatasetMetadata):
-    """Recreate normalization layers with proper stats from the dataset."""
-    from lerobot.policies.normalize import Normalize, Unnormalize
-
-    # Convert numpy stats to the format expected by normalization layers
-    stats = {}
-    for key, stat_dict in dataset_meta.stats.items():
-        stats[key] = {
-            stat_type: torch.from_numpy(stat_array) if isinstance(stat_array, np.ndarray) else stat_array
-            for stat_type, stat_array in stat_dict.items()
-        }
-
-    # Recreate normalization layers with proper stats
-    normalize_inputs = Normalize(policy.config.input_features, policy.config.normalization_mapping, stats)
-
-    normalize_targets = Normalize(policy.config.output_features, policy.config.normalization_mapping, stats)
-
-    unnormalize_outputs = Unnormalize(
-        policy.config.output_features, policy.config.normalization_mapping, stats
-    )
-
-    # Replace the normalization layers on the policy
-    policy.normalize_inputs = normalize_inputs
-    policy.normalize_targets = normalize_targets
-    policy.unnormalize_outputs = unnormalize_outputs
-
-    print("Normalization layers recreated with dataset stats.")
-
-
-def load_smolvla(cfg, dataset_repo: str, policy):
-    from lerobot.datasets.lerobot_dataset import LeRobotDataset
-
-    dataset = LeRobotDataset(dataset_repo, root="/raid/jade/.cache/huggingface/datasets/")
-    _inject_normalization_stats(policy=policy, dataset_meta=dataset.meta)  # only needed if stats are missing
-    return policy.to("cuda"), dataset
-
-
 @parser.wrap()
 def eval_main(cfg: EvalPipelineConfig):
     logging.info(pformat(asdict(cfg)))

src/lerobot/scripts/train.py

@@ -105,35 +105,6 @@ def update_policy(
     train_metrics.update_s = time.perf_counter() - start_time
     return train_metrics, output_dict
 
-
-# def _inject_normalization_stats(policy: SmolVLAPolicy, dataset_meta: LeRobotDatasetMetadata):
-#     """Recreate normalization layers with dataset stats if missing (Adil's workaround)."""
-#     from lerobot.policies.normalize import Normalize, Unnormalize
-
-#     if not hasattr(dataset_meta, "stats") or not dataset_meta.stats:
-#         print("⚠️ Dataset has no stats, skipping normalization injection.")
-#         return
-
-#     stats = {}
-#     for key, stat_dict in dataset_meta.stats.items():
-#         stats[key] = {
-#             stat_type: torch.as_tensor(stat_array)
-#             if isinstance(stat_array, np.ndarray)
-#             else stat_array
-#             for stat_type, stat_array in stat_dict.items()
-#         }
-
-#     normalize_inputs = Normalize(policy.config.input_features, policy.config.normalization_mapping, stats)
-#     normalize_targets = Normalize(policy.config.output_features, policy.config.normalization_mapping, stats)
-#     unnormalize_outputs = Unnormalize(policy.config.output_features, policy.config.normalization_mapping, stats)
-
-#     policy.normalize_inputs = normalize_inputs
-#     policy.normalize_targets = normalize_targets
-#     policy.unnormalize_outputs = unnormalize_outputs
-
-#     print("✅ Normalization layers injected with dataset stats.")
-
-
 @parser.wrap()
 def train(cfg: TrainPipelineConfig):
     cfg.validate()