Formatting

docker/rockchip/rk.mk

@@ -9,7 +9,7 @@ build-rk: version
 	docker buildx bake --file=docker/rockchip/rk.hcl rk \
 		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk
 
-push-rk: build-rk
+push-rk: version
 	docker buildx bake --file=docker/rockchip/rk.hcl rk \
-		--set rk.tags=$(IMAGE_REPO):${GITHUB_REF_NAME}-$(COMMIT_HASH)-rk \
+		--set rk.tags=crzynik/frigate:rk \
 		--push

frigate/embeddings/onnx/runner.py

@@ -181,7 +181,7 @@ class RKNNModelRunner:
         """Get input names for the model."""
         # For CLIP models, we need to determine the model type from the path
         model_name = os.path.basename(self.model_path).lower()
-        
+
         if "vision" in model_name:
             return ["pixel_values"]
         else:
@@ -189,7 +189,7 @@ class RKNNModelRunner:
             if self.model_type and "jina-clip" in self.model_type:
                 if "vision" in self.model_type:
                     return ["pixel_values"]
-            
+
             # Generic fallback
             return ["input"]
 
@@ -209,7 +209,7 @@ class RKNNModelRunner:
         try:
             input_names = self.get_input_names()
             rknn_inputs = []
-            
+
             for name in input_names:
                 if name in inputs:
                     if name == "pixel_values":
@@ -224,21 +224,23 @@ class RKNNModelRunner:
                         rknn_inputs.append(inputs[name])
                 else:
                     logger.warning(f"Input '{name}' not found in inputs, using default")
-                    
+
                     if name == "pixel_values":
                         batch_size = 1
                         if inputs:
                             for val in inputs.values():
-                                if hasattr(val, 'shape') and len(val.shape) > 0:
+                                if hasattr(val, "shape") and len(val.shape) > 0:
                                     batch_size = val.shape[0]
                                     break
                         # Create default in NHWC format as expected by RKNN
-                        rknn_inputs.append(np.zeros((batch_size, 224, 224, 3), dtype=np.float32))
+                        rknn_inputs.append(
+                            np.zeros((batch_size, 224, 224, 3), dtype=np.float32)
+                        )
                     else:
                         batch_size = 1
                         if inputs:
                             for val in inputs.values():
-                                if hasattr(val, 'shape') and len(val.shape) > 0:
+                                if hasattr(val, "shape") and len(val.shape) > 0:
                                     batch_size = val.shape[0]
                                     break
                         rknn_inputs.append(np.zeros((batch_size, 1), dtype=np.float32))

frigate/util/rknn_converter.py

@@ -38,6 +38,7 @@ MODEL_TYPE_CONFIGS = {
     },
 }
 
+
 def get_rknn_model_type(model_path: str) -> str | None:
     if all(keyword in model_path for keyword in ["jina-clip-v1", "vision"]):
         return "jina-clip-v1-vision"
@@ -49,6 +50,7 @@ def get_rknn_model_type(model_path: str) -> str | None:
 
     return None
 
+
 def is_rknn_compatible(model_path: str, model_type: str | None = None) -> bool:
     """
     Check if a model is compatible with RKNN conversion.
@@ -111,6 +113,7 @@ def ensure_rknn_toolkit() -> bool:
     """Ensure RKNN toolkit is available."""
     try:
         from rknn.api import RKNN  # type: ignore # noqa: F401
+
         logger.debug("RKNN toolkit is already available")
         return True
     except ImportError as e:
@@ -438,7 +441,7 @@ def auto_convert_model(
 
             if not model_type:
                 model_type = get_rknn_model_type(base_path)
-            
+
             if wait_for_conversion_completion(model_type, rknn_path, lock_file_path):
                 return str(rknn_path)
             else: