Use model type to decide if model can use full optimization

frigate/detectors/detection_runners.py

@@ -6,8 +6,6 @@ from abc import ABC, abstractmethod
 from typing import Any
 
 import numpy as np
-from frigate.detectors.detector_types import ModelTypeEnum
-from frigate.embeddings.types import EnrichmentModelTypeEnum
 import onnxruntime as ort
 
 from frigate.util.model import get_ort_providers
@@ -105,6 +103,10 @@ class CudaGraphRunner(BaseModelRunner):
 
     @staticmethod
     def is_complex_model(model_type: str) -> bool:
+        # Import here to avoid circular imports
+        from frigate.detectors.detector_config import ModelTypeEnum
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+        
         return model_type in [
             ModelTypeEnum.yolonas.value,
             EnrichmentModelTypeEnum.paddleocr.value,
@@ -169,6 +171,9 @@ class OpenVINOModelRunner(BaseModelRunner):
 
     @staticmethod
     def is_complex_model(model_type: str) -> bool:
+        # Import here to avoid circular imports
+        from frigate.embeddings.types import EnrichmentModelTypeEnum
+        
         return model_type in [EnrichmentModelTypeEnum.paddleocr.value]
 
     def __init__(self, model_path: str, device: str, model_type: str, **kwargs):

frigate/detectors/plugins/onnx.py

@@ -39,7 +39,7 @@ class ONNXDetector(DetectionApi):
         self.runner = get_optimized_runner(
             path,
             detector_config.device,
-            complex_model=False,
+            model_type=detector_config.model.model_type,
         )
 
         self.onnx_model_type = detector_config.model.model_type

frigate/detectors/plugins/openvino.py

@@ -45,7 +45,7 @@ class OvDetector(DetectionApi):
         self.runner = OpenVINOModelRunner(
             model_path=detector_config.model.path,
             device=detector_config.device,
-            complex_model=False,
+            model_type=detector_config.model.model_type,
         )
 
         # For dfine models, also pre-allocate target sizes tensor