use dummy stats

test_pi0_hub.py

@@ -7,6 +7,29 @@ import torch
 from lerobot.policies.pi0_openpi import PI0OpenPIPolicy
 
 
+def create_dummy_stats(config):
+    """Create dummy dataset statistics for testing."""
+    dummy_stats = {
+        "observation.state": {
+            "mean": torch.zeros(config.state_dim),
+            "std": torch.ones(config.state_dim),
+        },
+        "action": {
+            "mean": torch.zeros(config.action_dim),
+            "std": torch.ones(config.action_dim),
+        },
+    }
+
+    # Add stats for image keys if they exist
+    for key in config.image_keys:
+        dummy_stats[key] = {
+            "mean": torch.zeros(3, config.image_resolution[0], config.image_resolution[1]),
+            "std": torch.ones(3, config.image_resolution[0], config.image_resolution[1]),
+        }
+
+    return dummy_stats
+
+
 def test_hub_loading():
     """Test loading model from HuggingFace hub."""
     print("=" * 60)
@@ -27,6 +50,21 @@ def test_hub_loading():
         )
         print("✓ Model loaded successfully from HuggingFace hub")
 
+        # Inject dummy stats since they aren't loaded from the hub
+        print("Creating dummy dataset stats for testing...")
+        device = next(policy.parameters()).device
+        dummy_stats = create_dummy_stats(policy.config)
+
+        # Move dummy stats to device
+        for key, stats in dummy_stats.items():
+            dummy_stats[key] = {
+                "mean": stats["mean"].to(device),
+                "std": stats["std"].to(device),
+            }
+
+        # Initialize normalization layers with dummy stats if they have NaN/inf values
+        print("✓ Dummy stats created and moved to device")
+
         # Get model info
         print("\nModel configuration:")
         print(f"  - PaliGemma variant: {policy.config.paligemma_variant}")
@@ -34,7 +72,7 @@ def test_hub_loading():
         print(f"  - Action dimension: {policy.config.action_dim}")
         print(f"  - State dimension: {policy.config.state_dim}")
         print(f"  - Action horizon: {policy.config.action_horizon}")
-        print(f"  - Device: {next(policy.parameters()).device}")
+        print(f"  - Device: {device}")
         print(f"  - Dtype: {next(policy.parameters()).dtype}")
 
     except Exception as e:
@@ -46,30 +84,43 @@ def test_hub_loading():
 
     # Create dummy batch for testing
     batch_size = 1
-    device = next(policy.parameters()).device
 
-    # Create dummy dataset stats if not loaded with the model
+    # Check if normalization layers have invalid stats and replace with dummy stats if needed
-    if not hasattr(policy, "normalize_inputs") or policy.normalize_inputs is None:
+    try:
+        # Check if the normalize_inputs has valid stats
+        if hasattr(policy.normalize_inputs, "stats"):
+            obs_state_mean = policy.normalize_inputs.stats.get("observation.state", {}).get("mean")
+            if obs_state_mean is not None and (
+                torch.isinf(obs_state_mean).any() or torch.isnan(obs_state_mean).any()
+            ):
+                print("⚠️  Found invalid normalization stats, replacing with dummy stats...")
+
+                # Replace with dummy stats
+                from lerobot.policies.normalize import Normalize, Unnormalize
+
+                policy.normalize_inputs = Normalize(
+                    policy.config.input_features, policy.config.normalization_mapping, dummy_stats
+                )
+                policy.normalize_targets = Normalize(
+                    policy.config.output_features, policy.config.normalization_mapping, dummy_stats
+                )
+                policy.unnormalize_outputs = Unnormalize(
+                    policy.config.output_features, policy.config.normalization_mapping, dummy_stats
+                )
+                print("✓ Normalization layers updated with dummy stats")
+    except Exception as e:
+        print(f"⚠️  Error checking normalization stats, creating new ones: {e}")
+        # Fallback: create new normalization layers
         from lerobot.policies.normalize import Normalize, Unnormalize
 
-        dataset_stats = {
-            "observation.state": {
-                "mean": torch.zeros(policy.config.state_dim, device=device),
-                "std": torch.ones(policy.config.state_dim, device=device),
-            },
-            "action": {
-                "mean": torch.zeros(policy.config.action_dim, device=device),
-                "std": torch.ones(policy.config.action_dim, device=device),
-            },
-        }
         policy.normalize_inputs = Normalize(
-            policy.config.input_features, policy.config.normalization_mapping, dataset_stats
+            policy.config.input_features, policy.config.normalization_mapping, dummy_stats
         )
         policy.normalize_targets = Normalize(
-            policy.config.output_features, policy.config.normalization_mapping, dataset_stats
+            policy.config.output_features, policy.config.normalization_mapping, dummy_stats
         )
         policy.unnormalize_outputs = Unnormalize(
-            policy.config.output_features, policy.config.normalization_mapping, dataset_stats
+            policy.config.output_features, policy.config.normalization_mapping, dummy_stats
         )
 
     # Create test batch