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Cleanup onnx detector (#20128)

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* Cleanup onnx detector

* Fix

* Fix classification cropping

* Deprioritize openvino

* Send model type

* Use model type to decide if model can use full optimization

* Clenanup

* Cleanup
This commit is contained in:
Nicolas Mowen
2025-09-18 15:12:09 -06:00
committed by GitHub
parent c05e260ae9
commit 68f806bb61
10 changed files with 82 additions and 83 deletions
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frigate/detectors/detection_runners.py
+37 -10
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@@ -101,6 +101,19 @@ class CudaGraphRunner(BaseModelRunner):
for more complex models like CLIP or PaddleOCR.
"""
@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,
EnrichmentModelTypeEnum.jina_v1.value,
EnrichmentModelTypeEnum.jina_v2.value,
]
def __init__(self, session: ort.InferenceSession, cuda_device_id: int):
self._session = session
self._cuda_device_id = cuda_device_id
@@ -156,10 +169,17 @@ class CudaGraphRunner(BaseModelRunner):
class OpenVINOModelRunner(BaseModelRunner):
"""OpenVINO model runner that handles inference efficiently."""
def __init__(self, model_path: str, device: str, complex_model: bool, **kwargs):
@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):
self.model_path = model_path
self.device = device
self.complex_model = complex_model
self.complex_model = OpenVINOModelRunner.is_complex_model(model_type)
if not os.path.isfile(model_path):
raise FileNotFoundError(f"OpenVINO model file {model_path} not found.")
@@ -183,7 +203,7 @@ class OpenVINOModelRunner(BaseModelRunner):
self.infer_request = self.compiled_model.create_infer_request()
self.input_tensor: ov.Tensor | None = None
if not complex_model:
if not self.complex_model:
try:
input_shape = self.compiled_model.inputs[0].get_shape()
input_element_type = self.compiled_model.inputs[0].get_element_type()
@@ -381,28 +401,35 @@ class RKNNModelRunner(BaseModelRunner):
def get_optimized_runner(
model_path: str, device: str | None, complex_model: bool = True, **kwargs
model_path: str, device: str | None, model_type: str, **kwargs
) -> BaseModelRunner:
"""Get an optimized runner for the hardware."""
device = device or "AUTO"
if is_rknn_compatible(model_path):
rknn_path = auto_convert_model(model_path)
if rknn_path:
return RKNNModelRunner(rknn_path)
if device != "CPU" and is_openvino_gpu_npu_available():
return OpenVINOModelRunner(
model_path, device or "AUTO", complex_model, **kwargs
)
providers, options = get_ort_providers(device == "CPU", device, **kwargs)
if providers[0] == "CPUExecutionProvider":
# In the default image, ONNXRuntime is used so we will only get CPUExecutionProvider
# In other images we will get CUDA / ROCm which are preferred over OpenVINO
# There is currently no way to prioritize OpenVINO over CUDA / ROCm in these images
if device != "CPU" and is_openvino_gpu_npu_available():
return OpenVINOModelRunner(model_path, device, model_type, **kwargs)
ortSession = ort.InferenceSession(
model_path,
providers=providers,
provider_options=options,
)
if not complex_model and providers[0] == "CUDAExecutionProvider":
if (
not CudaGraphRunner.is_complex_model(model_type)
and providers[0] == "CUDAExecutionProvider"
):
return CudaGraphRunner(ortSession, options[0]["device_id"])
return ONNXModelRunner(ortSession)