'''

# Copyright 2020 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

sys.path.insert(0, './M2Det')

import torch

import torch.onnx

from collections import OrderedDict

import argparse

print(sys.path)

from m2det import build_net

import torch.utils.data as data

import torch.backends.cudnn as cudnn

from configs.CC import Config

from utils.core import init_net







parser = argparse.ArgumentParser(description='pth2onnx')

parser.add_argument('-c', '--config', default='M2Det/configs/m2det512_vgg16.py')

parser.add_argument('-d', '--dataset', default='COCO', help='VOC or COCO dataset')

parser.add_argument('--resume_net', default=None, help='resume net for retraining')

parser.add_argument('--resume_epoch', default=0, type=int, help='resume iter for retraining')

parser.add_argument('-pth', '--pth_path', default='weights/m2det512_vgg.pth')

parser.add_argument('-onnx', '--onnx_path', default='m2det512.onnx')

Args = parser.parse_args()



def proc_nodes_module(checkpoint):

    '''

    Args:

        checkpoint: Network parameters.

    Returns:

        Create a new dictionary, remove the unnecessary key value "module"

    '''

    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 convert(args, cfg):

    '''

    Args:

        args.pth_path: Weight file path

        args.onnx_path: onnx file path

        cfg: configs

    '''

    print('pth:{}'.format(args.pth_path))

    print('onnx:{}'.format(args.onnx_path))

    net = build_net('test', 

                    size = cfg.model.input_size, # Only 320, 512, 704 and 800 are supported

                    config = cfg.model.m2det_config)

    init_net(net, cfg, args.resume_net)

    model = net

    

    checkpoint = torch.load(args.pth_path, map_location='cpu')

    checkpoint = proc_nodes_module(checkpoint)

    model.load_state_dict(checkpoint)



    model.eval()

    input_names = ["image"]

    output_names = ["scores", "boxes"]

    dynamic_axes = {'image': {0: '-1'}, 'scores': {0: '-1'}, 'boxes': {0: '-1'}}

    dummy_input = torch.randn(1, 3, 512, 512)

    torch.onnx.export(model, dummy_input, args.onnx_path, input_names=input_names,

                      dynamic_axes=dynamic_axes, output_names=output_names,

                      verbose=True, opset_version=11)





if __name__ == "__main__":

    Cfg = Config.fromfile(Args.config)

    convert(Args, Cfg)