fix tests

tests/policies/test_pi05_openpi.py

@@ -23,6 +23,27 @@ def test_pi05_model_architecture():
         dtype="float32",
     )
 
+    # Set up input_features and output_features in the config
+    from lerobot.configs.types import FeatureType, PolicyFeature
+
+    config.input_features = {
+        "observation.state": PolicyFeature(
+            type=FeatureType.STATE,
+            shape=(14,),
+        ),
+        "observation.images.base_0_rgb": PolicyFeature(
+            type=FeatureType.VISUAL,
+            shape=(3, 224, 224),
+        ),
+    }
+
+    config.output_features = {
+        "action": PolicyFeature(
+            type=FeatureType.ACTION,
+            shape=(7,),
+        ),
+    }
+
     assert config.tokenizer_max_length == 200, (
         f"Expected tokenizer_max_length=200 for pi05, got {config.tokenizer_max_length}"
     )
@@ -40,6 +61,10 @@ def test_pi05_model_architecture():
             "mean": torch.zeros(7),
             "std": torch.ones(7),
         },
+        "observation.images.base_0_rgb": {
+            "mean": torch.zeros(3, 224, 224),
+            "std": torch.ones(3, 224, 224),
+        },
     }
 
     # Instantiate policy
@@ -82,6 +107,27 @@ def test_pi05_forward_pass():
         n_action_steps=16,  # Shorter action steps for testing
     )
 
+    # Set up input_features and output_features in the config
+    from lerobot.configs.types import FeatureType, PolicyFeature
+
+    config.input_features = {
+        "observation.state": PolicyFeature(
+            type=FeatureType.STATE,
+            shape=(14,),
+        ),
+        "observation.images.base_0_rgb": PolicyFeature(
+            type=FeatureType.VISUAL,
+            shape=(3, 224, 224),
+        ),
+    }
+
+    config.output_features = {
+        "action": PolicyFeature(
+            type=FeatureType.ACTION,
+            shape=(7,),
+        ),
+    }
+
     # Create dummy dataset stats
     dataset_stats = {
         "observation.state": {
@@ -92,6 +138,10 @@ def test_pi05_forward_pass():
             "mean": torch.zeros(7),
             "std": torch.ones(7),
         },
+        "observation.images.base_0_rgb": {
+            "mean": torch.zeros(3, 224, 224),
+            "std": torch.ones(3, 224, 224),
+        },
     }
 
     # Instantiate policy

tests/policies/test_pi0_openpi.py

@@ -18,6 +18,27 @@ def test_policy_instantiation():
     # Create config
     config = PI0OpenPIConfig(max_action_dim=7, max_state_dim=14, dtype="float32")
 
+    # Set up input_features and output_features in the config
+    from lerobot.configs.types import FeatureType, PolicyFeature
+
+    config.input_features = {
+        "observation.state": PolicyFeature(
+            type=FeatureType.STATE,
+            shape=(14,),
+        ),
+        "observation.images.base_0_rgb": PolicyFeature(
+            type=FeatureType.VISUAL,
+            shape=(3, 224, 224),
+        ),
+    }
+
+    config.output_features = {
+        "action": PolicyFeature(
+            type=FeatureType.ACTION,
+            shape=(7,),
+        ),
+    }
+
     # Create dummy dataset stats
     dataset_stats = {
         "observation.state": {
@@ -28,6 +49,10 @@ def test_policy_instantiation():
             "mean": torch.zeros(7),
             "std": torch.ones(7),
         },
+        "observation.images.base_0_rgb": {
+            "mean": torch.zeros(3, 224, 224),
+            "std": torch.ones(3, 224, 224),
+        },
     }
 
     # Instantiate policy

tests/policies/test_pi0_pi05_hub.py

@@ -36,7 +36,7 @@ def create_dummy_stats(config):
     }
 
     # Add stats for image keys if they exist
-    for key in config.image_keys:
+    for key in config.image_features.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]),
@@ -205,7 +205,7 @@ def _test_hub_loading(model_id, model_name):
     }
 
     # Add images if they're in the config
-    for key in policy.config.image_keys:
+    for key in policy.config.image_features.keys():
         batch[key] = torch.rand(batch_size, 3, 224, 224, dtype=torch.float32, device=device)
 
     try:
@@ -358,7 +358,7 @@ def test_all_base_models_hub_loading(model_id, model_type, policy_class):
     }
 
     # Add images based on config
-    for key in policy.config.image_keys:
+    for key in policy.config.image_features.keys():
         batch[key] = torch.rand(batch_size, 3, 224, 224, dtype=torch.float32, device=device)
 
     # Test forward pass