Handle model type inference

frigate/embeddings/onnx/runner.py

@@ -30,7 +30,7 @@ class ONNXModelRunner:
         self.type = "ort"
 
         try:
-            if is_rknn_compatible(model_path):
+            if device != "CPU" and is_rknn_compatible(model_path):
                 # Try to auto-convert to RKNN format
                 rknn_path = auto_convert_model(model_path)
                 if rknn_path:

frigate/util/rknn_converter.py

@@ -38,6 +38,16 @@ 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"
+
+    model_name = os.path.basename(model_path).lower()
+
+    if any(keyword in model_name for keyword in ["yolo", "yolox", "yolonas"]):
+        return model_name
+
+    return None
 
 def is_rknn_compatible(model_path: str, model_type: str | None = None) -> bool:
     """
@@ -57,17 +67,12 @@ def is_rknn_compatible(model_path: str, model_type: str | None = None) -> bool:
     if soc is None:
         return False
 
+    if not model_type:
+        model_type = get_rknn_model_type(model_path)
+
     if model_type and model_type in MODEL_TYPE_CONFIGS:
         return True
 
-    model_name = os.path.basename(model_path).lower()
-
-    if any(keyword in model_name for keyword in ["jina", "clip", "vision"]):
-        return True
-
-    if any(keyword in model_name for keyword in ["yolo", "yolox", "yolonas"]):
-        return True
-
     return False
 
 
@@ -108,9 +113,7 @@ def ensure_torch_dependencies() -> bool:
 def ensure_rknn_toolkit() -> bool:
     """Ensure RKNN toolkit is available."""
     try:
-        import rknn  # type: ignore  # noqa: F401
         from rknn.api import RKNN  # type: ignore # noqa: F401
-
         logger.debug("RKNN toolkit is already available")
         return True
     except ImportError:
@@ -379,7 +382,7 @@ def wait_for_conversion_completion(
 
 
 def auto_convert_model(
-    model_path: str, model_type: str, quantization: bool = False
+    model_path: str, model_type: str | None = None, quantization: bool = False
 ) -> Optional[str]:
     """
     Automatically convert a model to RKNN format if needed.
@@ -418,6 +421,9 @@ def auto_convert_model(
                 logger.info(f"Converting {model_path} to RKNN format...")
                 rknn_path.parent.mkdir(parents=True, exist_ok=True)
 
+                if not model_type:
+                    model_type = get_rknn_model_type(base_path)
+
                 if convert_onnx_to_rknn(
                     str(base_path), str(rknn_path), model_type, quantization
                 ):