# 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 torch

import sys

import os

from GloRe.network.resnet50_3d_gcn_x5 import RESNET50_3D_GCN_X5

def pth2onnx(input_file, output_file):

    net = RESNET50_3D_GCN_X5(num_classes=101, pretrained=False)

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

    net.load_state_dict(state_dict['state_dict'])

    net.eval()

    input_names = ["image"]

    output_names = ["class"]

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

    dummy_input = torch.randn(1, 3, 8, 224, 224)

    torch.onnx.export(net, dummy_input, output_file, input_names=input_names, dynamic_axes=dynamic_axes,

                      output_names=output_names, opset_version=11, verbose=True)





if __name__ == '__main__':

    args = sys.argv

    pth2onnx(args[1], args[2])