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Correctly calculate input data type for OV (#20066)

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* Correctly calculate input data type for OV

* Formatting
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Nicolas Mowen 2025-09-14 07:15:41 -06:00 committed by GitHub
parent c74e86dff2
commit ff0430964c
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2 changed files with 38 additions and 12 deletions
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35
frigate/detectors/detection_runners.py
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@ -149,7 +149,8 @@ class OpenVINOModelRunner(BaseModelRunner):
# Create reusable inference request
self.infer_request = self.compiled_model.create_infer_request()
input_shape = self.compiled_model.inputs[0].get_shape()
self.input_tensor = ov.Tensor(ov.Type.f32, input_shape)
input_element_type = self.compiled_model.inputs[0].get_element_type()
self.input_tensor = ov.Tensor(input_element_type, input_shape)
def get_input_names(self) -> list[str]:
"""Get input names for the model."""
@ -161,20 +162,44 @@ class OpenVINOModelRunner(BaseModelRunner):
# Assuming NCHW format, width is the last dimension
return int(input_shape[-1])
def run(self, input_data: np.ndarray) -> list[np.ndarray]:
def run(self, inputs: dict[str, Any]) -> list[np.ndarray]:
"""Run inference with the model.
Args:
input_data: Input tensor data
inputs: Dictionary mapping input names to input data
Returns:
List of output tensors
"""
# Copy input data to pre-allocated tensor
# Handle single input case for backward compatibility
if len(inputs) == 1 and len(self.compiled_model.inputs) == 1:
# Single input case - use the pre-allocated tensor for efficiency
input_data = list(inputs.values())[0]
np.copyto(self.input_tensor.data, input_data)
self.infer_request.infer(self.input_tensor)
else:
# Multiple inputs case - set each input by name
for input_name, input_data in inputs.items():
# Find the input by name
input_port = None
for port in self.compiled_model.inputs:
if port.get_any_name() == input_name:
input_port = port
break
if input_port is None:
raise ValueError(f"Input '{input_name}' not found in model")
# Create tensor with the correct element type
input_element_type = input_port.get_element_type()
input_tensor = ov.Tensor(input_element_type, input_data.shape)
np.copyto(input_tensor.data, input_data)
# Set the input tensor
self.infer_request.set_input_tensor(input_tensor)
# Run inference
self.infer_request.infer(self.input_tensor)
self.infer_request.infer()
# Get all output tensors
outputs = []

11
frigate/detectors/plugins/openvino.py
View File

@ -141,16 +141,17 @@ class OvDetector(DetectionApi):
"images": tensor_input,
"orig_target_sizes": np.array([[self.h, self.w]], dtype=np.int64),
}
outputs = self.runner.run_with_named_inputs(inputs)
outputs = self.runner.run(inputs)
tensor_output = (
outputs["output0"],
outputs["output1"],
outputs["output2"],
outputs[0],
outputs[1],
outputs[2],
)
return post_process_dfine(tensor_output, self.w, self.h)
# Run inference using the runner
outputs = self.runner.run(tensor_input)
input_name = self.runner.get_input_names()[0]
outputs = self.runner.run({input_name: tensor_input})
detections = np.zeros((20, 6), np.float32)