#!/usr/bin/env python3
# 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.optim as optim
checkpoint_path = sys.argv[1]
output_path = sys.argv[2]


def build_model():
    sys.path.append(r"./pytorch-image-models")
    import timm
    model = timm.create_model('tresnet_m', checkpoint_path=checkpoint_path)
    return model


def pth2onnx(output_file):
    model = build_model()
    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, 
                    opset_version=11, 
                    verbose=True)
            

def main():
    pth2onnx(output_path)


if __name__ == '__main__':
    main()