# 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 os

import sys

import numpy as np

import torch

import torch.onnx



from modeling.deeplab import *



def main(input_file, output_file):

    model = DeepLab(num_classes=21,

                backbone="resnet",

                output_stride=16,

                sync_bn=False,

                freeze_bn=False)

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

    model.load_state_dict(checkpoint['state_dict'])

    model.eval()

    input_names = ["actual_input_1"]

    output_names = ["output1"]

    dummy_input = torch.randn(1, 3, 513, 513)

    dynamic_axes = {'actual_input_1':{0:'-1'},'output1':{0:'-1'}}

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



if __name__ == "__main__":

    input_file = sys.argv[1]

    output_file = sys.argv[2]

    main(input_file, output_file)