remove important sampling

lerobot/common/datasets/sampler.py

@@ -184,12 +184,3 @@ class PrioritizedSampler(Sampler[int]):
 
     def __len__(self) -> int:
         return self.num_samples_per_epoch
-
-    def compute_is_weights(self, indices: List[int]) -> torch.Tensor:
-        w = []
-        total_p = self.sumtree.total_priority()
-        for idx in indices:
-            p = self.priorities[idx] / total_p
-            w.append((p * self.data_len) ** (-self.beta))
-        w = torch.tensor(w, dtype=torch.float32)
-        return w / w.max()

lerobot/scripts/train.py

@@ -71,16 +71,6 @@ def update_policy(
         loss, output_dict = policy.forward(batch)
         # TODO(rcadene): policy.unnormalize_outputs(out_dict)
 
-        # Apply importance-sampling if available
-        if "is_weights" in batch and "per_sample_l1" in output_dict:
-            per_sample_l1 = output_dict["per_sample_l1"]
-            l1_per_item = per_sample_l1.mean(dim=-1)
-            w = batch["is_weights"].to(device)
-            weighted_loss = (l1_per_item * w).mean()
-            if policy.config.use_vae and "kld_loss" in output_dict:
-                weighted_loss += output_dict["kld_loss"] * policy.config.kl_weight
-            loss = weighted_loss
-
     grad_scaler.scale(loss).backward()
 
     # Unscale the gradient of the optimizer's assigned params in-place **prior to gradient clipping**.
@@ -231,10 +221,6 @@ def train(cfg: TrainPipelineConfig):
             if isinstance(batch[key], torch.Tensor):
                 batch[key] = batch[key].to(device, non_blocking=True)
 
-        if "indices" in batch:
-            is_weights = sampler.compute_is_weights(batch["indices"].cpu().tolist())
-            batch["is_weights"] = is_weights
-
         train_tracker, output_dict = update_policy(
             train_tracker,
             policy,