# 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

from collections import OrderedDict

sys.path.append(r"./ShuffleNet-Series/ShuffleNetV1")

from network  import ShuffleNetV1



def pth2onnx(input_file, output_file,batch_size):

    model = ShuffleNetV1(model_size="1.0x", group=3)

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

    new_state_dict = OrderedDict()

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

        name = k[7:]

        new_state_dict[name] = v

    model.load_state_dict(new_state_dict)

    model.eval()

    input_names = ["image"]

    output_names = ["class"]

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

    batch_size=int(batch_size)

    dummy_input = torch.rand(batch_size, 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)



if __name__=="__main__":

    pth2onnx(sys.argv[1], sys.argv[2],sys.argv[3])