fix dinov3

src/lerobot/policies/rlearn/modeling_rlearn.py

@@ -393,8 +393,24 @@ class RLearNPolicy(PreTrainedPolicy):
         else:
             raise RuntimeError("DINOv3 outputs do not contain last_hidden_state or pooler_output")
         
-        # Reshape to (B, T, D) for analysis
-        vision_features = rearrange(vision_features_flat, '(b t) d -> b t d', b=B, t=T)
+        # Robustly reshape to (B, T, D): detect correct flatten order by maximizing temporal variance
+        try:
+            cand1 = rearrange(vision_features_flat, '(b t) d -> b t d', b=B, t=T)
+            cand2 = rearrange(vision_features_flat, '(t b) d -> b t d', t=T, b=B)
+            # Compute mean temporal difference per sample
+            def mean_time_diff(x):
+                if T <= 1:
+                    return torch.tensor(0.0, device=x.device)
+                diffs = (x[:, 1:, :] - x[:, :-1, :]).pow(2).sum(dim=-1).sqrt()
+                return diffs.mean()
+            diff1 = mean_time_diff(cand1)
+            diff2 = mean_time_diff(cand2)
+            vision_features = cand1 if diff1 >= diff2 else cand2
+            if self.training and torch.rand(1).item() < 0.05:
+                print(f"DINO reshape choice: {'(b t)->b t' if diff1 >= diff2 else '(t b)->b t'} | diff1={diff1.item():.6f}, diff2={diff2.item():.6f}")
+        except Exception:
+            # Fallback to default
+            vision_features = rearrange(vision_features_flat, '(b t) d -> b t d', b=B, t=T)
         
         # DEBUG: Analyze vision feature variability
         if self.training and torch.rand(1).item() < 0.1:  # 10% of training steps for more frequent debugging