# 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 sys

import torch

import argparse

sys.path.append('./models')

from mtcnn import PNet_truncated, RNet_truncated, ONet_truncated





def MTCNN_pth2onnx(opt):

    if opt.model == 'PNet':

        model = PNet_truncated()

    elif opt.model == 'RNet':

        model = RNet_truncated()

    elif opt.model == 'ONet':

        model = ONet_truncated()

    else:

        print("Error network")

        return -1

    model = model.eval()

    input_names = ['image']

    if opt.model == 'PNet':

        output_names = ["probs", "reg"]

        dynamic_axes = {'image': {0: '-1', 2: '-1', 3: '-1'}, 'probs': {0: '-1', 2: '-1', 3: '-1'},

                        'reg': {0: '-1', 2: '-1', 3: '-1'}}

        dummy_input = torch.randn(1, 3, 1229, 1000)

    elif opt.model == 'RNet':

        output_names = ['regs', 'cls']

        dynamic_axes = {'image': {0: '-1'}, 'regs': {0: '-1'}, 'cls': {0: '-1'}}

        dummy_input = torch.randn(20, 3, 24, 24)

    else:

        output_names = ['landmark', 'regs', 'cls']

        dynamic_axes = {'image': {0: '-1'}, 'landmark': {0: '-1'}, 'regs': {0: '-1'}, 'cls': {0: '-1'}}

        dummy_input = torch.randn(20, 3, 48, 48)



    torch.onnx.export(model, dummy_input, opt.output_file, input_names=input_names, dynamic_axes=dynamic_axes,

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





if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument('--model', type=str, help='[PNet/RNet/ONet]')

    parser.add_argument('--output_file', type=str, default='.', help='output path')

    arg = parser.parse_args()

    MTCNN_pth2onnx(arg)