sample 100k

src/lerobot/scripts/lerobot_train.py

@@ -246,31 +246,36 @@ def train(cfg: TrainPipelineConfig, accelerator: Accelerator | None = None):
     # Recompute action stats as delta if use_delta_actions is enabled.
     # Must iterate the actual dataset (which returns action chunks via delta_timestamps)
     # so stats capture the full range of chunk-level deltas, not just per-frame deltas.
+    # We sample a subset for speed — 100K frames is sufficient for accurate stats.
     if getattr(cfg.policy, "use_delta_actions", False) and is_main_process:
-        logging.info("use_delta_actions is enabled — computing delta action stats from dataset chunks")
+        import numpy as np
+
         from lerobot.datasets.compute_stats import get_feature_stats
         from lerobot.processor.delta_action_processor import to_delta_actions
 
+        max_samples = min(100_000, len(dataset))
+        indices = np.random.choice(len(dataset), max_samples, replace=False)
+        logging.info(
+            f"use_delta_actions is enabled — computing delta action stats from {max_samples} dataset chunks"
+        )
+
         all_delta_actions = []
-        for i in range(len(dataset)):
+        for i in indices:
-            item = dataset[i]
+            item = dataset[int(i)]
             action = item["action"]
             state = item["observation.state"]
-            # action may be (chunk_size, action_dim) or (action_dim,)
             if action.ndim == 1:
                 action = action.unsqueeze(0)
             mask = [True] * action.shape[-1]
             delta = to_delta_actions(action.unsqueeze(0), state.unsqueeze(0), mask).squeeze(0)
             all_delta_actions.append(delta.numpy())
 
-        import numpy as np
-
         all_delta = np.concatenate(all_delta_actions, axis=0)
         delta_stats = get_feature_stats(all_delta, axis=0, keepdims=all_delta.ndim == 1)
         dataset.meta.stats["action"] = delta_stats
         logging.info(
-            f"Delta action stats computed from {len(dataset)} samples: "
+            f"Delta action stats: mean={np.abs(delta_stats['mean']).mean():.4f}, "
-            f"mean={np.abs(delta_stats['mean']).mean():.4f}, std={delta_stats['std'].mean():.4f}"
+            f"std={delta_stats['std'].mean():.4f}"
         )
 
     # Wait for all processes to finish policy creation before continuing