# Copyright 2022 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.





import torch

import sys



def pth2onnx(input_file, output_file):

    model = torch.load(input_file, map_location = torch.device('cpu'))  # map_location为转换设备

    model.eval()

    input_names = ["image"]

    output_names = ["class"]

    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}} 

    dummy_input = torch.randn(1, 3, 128, 128)  # 模型输入的shape

    torch.onnx.export(model.module, dummy_input, output_file,

                      input_names=input_names,dynamic_axes = dynamic_axes,

                      output_names=output_names, opset_version=13, verbose=False) # opset是可视化用的,verbose输出进度条显示



def main():

    input_file = sys.argv[1] 

    output_file = sys.argv[2]

    pth2onnx(input_file, output_file)



if __name__ == '__main__':

    main()