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import os |
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import onnxruntime |
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class ONNXEngine: |
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def __init__(self, onnx_path, use_gpu): |
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""" |
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:param onnx_path: |
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""" |
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if not os.path.exists(onnx_path): |
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raise Exception(f'{onnx_path} is not exists') |
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providers = ['CPUExecutionProvider'] |
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if use_gpu: |
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providers = ([ |
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'TensorrtExecutionProvider', |
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'CUDAExecutionProvider', |
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'CPUExecutionProvider', |
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], ) |
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self.onnx_session = onnxruntime.InferenceSession(onnx_path, |
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providers=providers) |
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self.input_name = self.get_input_name(self.onnx_session) |
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self.output_name = self.get_output_name(self.onnx_session) |
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def get_output_name(self, onnx_session): |
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""" |
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output_name = onnx_session.get_outputs()[0].name |
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:param onnx_session: |
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:return: |
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""" |
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output_name = [] |
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for node in onnx_session.get_outputs(): |
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output_name.append(node.name) |
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return output_name |
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def get_input_name(self, onnx_session): |
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""" |
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input_name = onnx_session.get_inputs()[0].name |
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:param onnx_session: |
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:return: |
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""" |
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input_name = [] |
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for node in onnx_session.get_inputs(): |
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input_name.append(node.name) |
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return input_name |
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def get_input_feed(self, input_name, image_numpy): |
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""" |
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input_feed={self.input_name: image_numpy} |
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:param input_name: |
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:param image_numpy: |
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:return: |
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""" |
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input_feed = {} |
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for name in input_name: |
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input_feed[name] = image_numpy |
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return input_feed |
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def run(self, image_numpy): |
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input_feed = self.get_input_feed(self.input_name, image_numpy) |
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result = self.onnx_session.run(self.output_name, input_feed=input_feed) |
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return result |
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