e25f20f7a3
commit 3f4938648950a7f3bf9a19c320ca9fae7c52de20 Author: sophgo-forum-service <forum_service@sophgo.com> Date: Mon May 13 13:44:23 2024 +0800 [feat] cviruntime opensource for cv18xx soc. - a4b6a3, add cumsum and gatherelements_pt.
52 lines
2.3 KiB
Python
52 lines
2.3 KiB
Python
#!/usr/bin/python3
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"""
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Copyright (C) Cvitek Co., Ltd. 2019-2020. All rights reserved.
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"""
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import onnx
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from cvi_toolkit.transform.BaseConverter import TensorType
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from cvi_toolkit.transform.onnx_converter import OnnxConverter
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from cvi_toolkit.transform.tflite_converter_int8 import TFLiteConverter
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from cvi_toolkit.transform.tensorflow_converter import TFConverter
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from cvi_toolkit.utils.log_setting import setup_logger
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from cvi_toolkit.data.preprocess import add_preprocess_parser, preprocess
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logger = setup_logger('root', log_level="INFO")
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class MyOnnxConverter(OnnxConverter):
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def __init__(self, model_name, onnx_model, mlir_file_path, batch_size=1, preprocessor=None):
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super().__init__(model_name, onnx_model, mlir_file_path, batch_size, preprocessor.to_dict())
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self.onnxop_factory['LeakyRelu'] = lambda node: self.convert_leaky_relu(node);
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def convert_leaky_relu(self, onnx_node):
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assert(onnx_node.op_type == "LeakyRelu")
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alpha = onnx_node.attrs.get("alpha", 0.01)
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custom_op_param = {
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'tpu': True,
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'do_quant': True,
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'operation_name': 'leaky_relu',
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'threshold_overwrite': 'backward',
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'param': {
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'negative_slope': float(alpha)
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}
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}
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op, input_shape, tensor_type = self.getOperand(onnx_node.inputs[0])
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operands = list()
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operands.append(op)
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output_shape = input_shape
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custom_op = self.CVI.add_custom_op("{}_{}".format(onnx_node.name, onnx_node.op_type),
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operands, output_shape, **custom_op_param)
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self.addOperand(onnx_node.name, custom_op, output_shape, TensorType.ACTIVATION)
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if __name__ == "__main__":
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onnx_model = onnx.load('model/yolov3-416.onnx')
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preprocessor = preprocess()
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preprocessor.config(net_input_dims="416,416",
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resize_dims="416,416", crop_method='center', keep_aspect_ratio=True,
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raw_scale=1.0, mean='0,0,0', std='1,1,1', input_scale=1.0,
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channel_order='bgr', pixel_format=None, data_format='nchw',
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aligned=False, gray=False)
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c = MyOnnxConverter('yolo_v3', 'model/yolov3-416.onnx',
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'yolo_v3_416.mlir', batch_size=1, preprocessor=preprocessor)
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c.run() |