administrator/frigate
1
0
Fork 0
mirror of https://github.com/blakeblackshear/frigate.git synced 2026-04-15 11:32:09 +03:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

Share tensor

Browse Source
This commit is contained in:
Nicolas Mowen 2025-09-13 17:10:16 -06:00
parent 19685df6f0
commit 2da049b9b3
1 changed files with 15 additions and 9 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

24
frigate/detectors/plugins/openvino.py
View File

@ -51,8 +51,17 @@ class OvDetector(DetectionApi):
model=detector_config.model.path, device_name=detector_config.device model=detector_config.model.path, device_name=detector_config.device
) )
# Create a single reusable inference request for optimal performance # Create a single reusable resources for optimal performance
self.infer_request = self.interpreter.create_infer_request() self.infer_request = self.interpreter.create_infer_request()
input_shape = self.interpreter.inputs[0].get_shape()
self.input_tensor = ov.Tensor(ov.Type.f32, input_shape)
# For dfine models, also pre-allocate target sizes tensor
if self.ov_model_type == ModelTypeEnum.dfine:
self.target_sizes_tensor = ov.Tensor(
np.array([[self.h, self.w]], dtype=np.int64)
)
self.model_invalid = False self.model_invalid = False
if self.ov_model_type not in self.supported_models: if self.ov_model_type not in self.supported_models:
@ -131,15 +140,12 @@ class OvDetector(DetectionApi):
] ]
def detect_raw(self, tensor_input): def detect_raw(self, tensor_input):
# TODO: see if we can use shared_memory=True # Copy input data to pre-allocated tensor to avoid allocation overhead
input_tensor = ov.Tensor(array=tensor_input) np.copyto(self.input_tensor.data, tensor_input)
if self.ov_model_type == ModelTypeEnum.dfine: if self.ov_model_type == ModelTypeEnum.dfine:
self.infer_request.set_tensor("images", input_tensor) self.infer_request.set_tensor("images", self.input_tensor)
target_sizes_tensor = ov.Tensor( self.infer_request.set_tensor("orig_target_sizes", self.target_sizes_tensor)
np.array([[self.h, self.w]], dtype=np.int64)
)
self.infer_request.set_tensor("orig_target_sizes", target_sizes_tensor)
self.infer_request.infer() self.infer_request.infer()
tensor_output = ( tensor_output = (
self.infer_request.get_output_tensor(0).data, self.infer_request.get_output_tensor(0).data,
@ -148,7 +154,7 @@ class OvDetector(DetectionApi):
) )
return post_process_dfine(tensor_output, self.w, self.h) return post_process_dfine(tensor_output, self.w, self.h)
self.infer_request.infer(input_tensor) self.infer_request.infer(self.input_tensor)
detections = np.zeros((20, 6), np.float32) detections = np.zeros((20, 6), np.float32)