# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the BSD 3-Clause License  (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# https://opensource.org/licenses/BSD-3-Clause

#

# 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 torch.onnx



from collections import OrderedDict

from ghostnet.ghostnet_pytorch.ghostnet import ghostnet



def proc_nodes_module(checkpoint, AttrName):

    new_state_dict = OrderedDict()

    for k, v in checkpoint[AttrName].items():

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

            name = k[7:]

        else:

            name = k[0:]

        new_state_dict[name] = v

    return new_state_dict



def convert():

    checkpoint = torch.load("model_best.pth.tar", map_location='cpu')

    checkpoint['state_dict'] = proc_nodes_module(checkpoint, 'state_dict')

    

    model = ghostnet()

    model.load_state_dict(checkpoint['state_dict'], strict=False)

    model.eval()

    

    input_names = ["image"]

    output_names = ["output1"]

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

    torch.onnx.export(model, dummy_input, "ghostnet_b.onnx", input_names=input_names, output_names=output_names, opset_version=11)





if __name__ == "__main__":

    convert()