LPR device selection (#17592)

* config changes

* use device

* docs

* docs

* reset state

* remove auto

* use device

* docs

frigate/config/classification.py

@@ -19,6 +19,11 @@ class SemanticSearchModelEnum(str, Enum):
     jinav2 = "jinav2"
 
 
+class LPRDeviceEnum(str, Enum):
+    GPU = "GPU"
+    CPU = "CPU"
+
+
 class BirdClassificationConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable bird classification.")
     threshold: float = Field(
@@ -94,6 +99,10 @@ class CameraFaceRecognitionConfig(FrigateBaseModel):
 
 class LicensePlateRecognitionConfig(FrigateBaseModel):
     enabled: bool = Field(default=False, title="Enable license plate recognition.")
+    device: Optional[LPRDeviceEnum] = Field(
+        default=LPRDeviceEnum.CPU,
+        title="The device used for license plate recognition.",
+    )
     detection_threshold: float = Field(
         default=0.7,
         title="License plate object confidence score required to begin running recognition.",

frigate/data_processing/common/license_plate/model.py

@@ -12,13 +12,13 @@ class LicensePlateModelRunner(DataProcessorModelRunner):
     def __init__(self, requestor, device: str = "CPU", model_size: str = "large"):
         super().__init__(requestor, device, model_size)
         self.detection_model = PaddleOCRDetection(
-            model_size=model_size, requestor=requestor, device="CPU"
+            model_size=model_size, requestor=requestor, device=device
         )
         self.classification_model = PaddleOCRClassification(
-            model_size=model_size, requestor=requestor, device="CPU"
+            model_size=model_size, requestor=requestor, device=device
         )
         self.recognition_model = PaddleOCRRecognition(
-            model_size=model_size, requestor=requestor, device="CPU"
+            model_size=model_size, requestor=requestor, device=device
         )
         self.yolov9_detection_model = LicensePlateDetector(
             model_size=model_size, requestor=requestor, device=device

frigate/embeddings/maintainer.py

@@ -108,7 +108,9 @@ class EmbeddingMaintainer(threading.Thread):
 
         # model runners to share between realtime and post processors
         if self.config.lpr.enabled:
-            lpr_model_runner = LicensePlateModelRunner(self.requestor, device="AUTO")
+            lpr_model_runner = LicensePlateModelRunner(
+                self.requestor, device=self.config.lpr.device
+            )
 
         # realtime processors
         self.realtime_processors: list[RealTimeProcessorApi] = []

frigate/embeddings/onnx/runner.py

@@ -94,6 +94,10 @@ class ONNXModelRunner:
         if self.type == "ov":
             infer_request = self.interpreter.create_infer_request()
 
+            # This ensures the model starts with a clean state for each sequence
+            # Important for RNN models like PaddleOCR recognition
+            infer_request.reset_state()
+
             outputs = infer_request.infer(input)
 
             return outputs