Optimize OpenVINO and ONNX Model Runners (#20063)

* Use re-usable inference request to reduce CPU usage

* Share tensor

* Don't count performance

* Create openvino runner class

* Break apart onnx runner

* Add specific note about inability to use CUDA graphs for some models

* Adjust rknn to use RKNNRunner

* Use optimized runner

* Add support for non-complex models for CudaExecutionProvider

* Use core mask for rknn

* Correctly handle cuda input

* Cleanup

* Sort imports

frigate/embeddings/onnx/face_embedding.py

@@ -6,12 +6,12 @@ import os
 import numpy as np
 
 from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.detection_runners import get_optimized_runner
 from frigate.log import redirect_output_to_logger
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
 from .base_embedding import BaseEmbedding
-from .runner import ONNXModelRunner
 
 try:
     from tflite_runtime.interpreter import Interpreter
@@ -148,9 +148,10 @@ class ArcfaceEmbedding(BaseEmbedding):
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 device=self.config.device or "GPU",
+                complex_model=False,
             )
 
     def _preprocess_inputs(self, raw_inputs):