# 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 onnx

import torch

import argparse



from BigGAN import Generator

from collections import OrderedDict





def proc_nodes_module(checkpoint):

    new_state_dict = OrderedDict()

    for k, v in checkpoint.items():

        if (k[0:7] == "module."):

            name = k[7:]

        else:

            name = k[0:]

        new_state_dict[name] = v

    return new_state_dict





def pth2onnx(input_file, output_file):

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

    checkpoint = proc_nodes_module(checkpoint)



    model = Generator(**{'G_lr':1e-4, 'SN_eps':1e-6, 'adam_eps':1e-6,

                         'G_ch':96, 'shared_dim':128,

                         'skip_init':True, 'no_optim': True,

                         'hier':True, 'dim_z':120})

    model.load_state_dict(checkpoint)

    model.eval()



    input_names = ['noise', 'label']

    output_names = ['image']

    dynamic_axes = {'noise': {0: '-1'}, 'label': {0: '-1'}, 'image': {0: '-1'}}



    dummy_z = torch.randn((1, 1, 20))

    dummy_y = torch.randn((1, 5, 148))



    torch.onnx.export(model, (dummy_z, dummy_y), output_file, dynamic_axes=dynamic_axes,

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





if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument('--source', type=str, default="./G_ema.pth")

    parser.add_argument('--target', type=str, default="./biggan.onnx")

    args = parser.parse_args()



    pth2onnx(args.source, args.target)