# Copyright 2020 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 sys

import torch

import torch.onnx

import torchvision.models as models



def pth2onnx(input_file, output_file):

    model = models.resnet101(pretrained=False)

    checkpoint = torch.load(input_file, map_location=None)

    model.load_state_dict(checkpoint)



    model.eval()

    input_names = ["image"]

    output_names = ["class"]

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

    dummy_input = torch.randn(1, 3, 224, 224)

    torch.onnx.export(model, dummy_input, output_file, input_names = input_names, dynamic_axes = dynamic_axes, output_names = output_names, verbose=True, opset_version=11)



if __name__ == "__main__":

    input_file = sys.argv[1]

    output_file = sys.argv[2]

    pth2onnx(input_file, output_file)