# 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 os

import sys

import torch

sys.path.append("./MGN")

from MGN.network import MGN

os.environ['CUDA_VISIBLE_DEVICES'] = '0'





def pth2onnx(input_file, output_file, batch_size):

    model = MGN()

    model = model.to('cpu')

    model.load_state_dict(torch.load(input_file,  map_location=torch.device('cpu')))

    model.eval()

    input_names = ["image"]

    output_names = ["features"]

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

    dummy_input = torch.randn(batch_size, 3, 384, 128)

    torch.onnx.export(model, dummy_input, output_file, input_names = input_names,

                      dynamic_axes = dynamic_axes, output_names = output_names,

                      opset_version=11, verbose=True)

    print("***********************************Convert to ONNX model file SUCCESS!***"

          "*******************************************")





if __name__ == '__main__':

    pth2onnx(sys.argv[1], sys.argv[2], int(sys.argv[3]))