# Copyright 2021 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 argparse

import torch

import lib.medzoo as medzoo

    

def pth2onnx():

    args = get_arguments()

    input_file = args.input

    output_file = args.output

    model, optimizer = medzoo.create_model(args)

    checkpoint = torch.load(input_file, map_location="cpu")

    model.load_state_dict(checkpoint, False)



    model.eval()

    input_names = ["image"]

    output_names = ["class"]

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

    dummy_input = torch.randn(1, 4, 64, 64, 64)

    torch.onnx.export(model, dummy_input, output_file, input_names = input_names, dynamic_axes = dynamic_axes, output_names = output_names, verbose=False, 

    opset_version=11)





def get_arguments():

    parser = argparse.ArgumentParser()

    parser.add_argument('--classes', type=int, default=4)

    parser.add_argument('--inChannels', type=int, default=4)

    parser.add_argument('--model', type=str, default='UNET3D',

                        choices=('VNET', 'VNET2', 'UNET3D', 'DENSENET1', 'DENSENET2', 'DENSENET3', 'HYPERDENSENET'))

    parser.add_argument('--input', type=str, default='none')

    parser.add_argument('--output', type=str, default='none')

    args = parser.parse_args()

    return args





if __name__ == '__main__':

    pth2onnx()