# Copyright (c) OpenMMLab. All rights reserved.

import argparse

import os.path as osp

import mmcv

import torch

from mmcv.runner import CheckpointLoader

def convert_stdc(ckpt, stdc_type):

    new_state_dict = {}

    if stdc_type == 'STDC1':

        stage_lst = ['0', '1', '2.0', '2.1', '3.0', '3.1', '4.0', '4.1']

    else:

        stage_lst = [

            '0', '1', '2.0', '2.1', '2.2', '2.3', '3.0', '3.1', '3.2', '3.3',

            '3.4', '4.0', '4.1', '4.2'

        ]

    for k, v in ckpt.items():

        ori_k = k

        flag = False

        if 'cp.' in k:

            k = k.replace('cp.', '')

        if 'features.' in k:

            num_layer = int(k.split('.')[1])

            feature_key_lst = 'features.' + str(num_layer) + '.'

            stages_key_lst = 'stages.' + stage_lst[num_layer] + '.'

            k = k.replace(feature_key_lst, stages_key_lst)

            flag = True

        if 'conv_list' in k:

            k = k.replace('conv_list', 'layers')

            flag = True

        if 'avd_layer.' in k:

            if 'avd_layer.0' in k:

                k = k.replace('avd_layer.0', 'downsample.conv')

            elif 'avd_layer.1' in k:

                k = k.replace('avd_layer.1', 'downsample.bn')

            flag = True

        if flag:

            new_state_dict[k] = ckpt[ori_k]

    return new_state_dict

def main():

    parser = argparse.ArgumentParser(

        description='Convert keys in official pretrained STDC1/2 to '

        'MMSegmentation style.')

    parser.add_argument('src', help='src model path')

    # The dst path must be a full path of the new checkpoint.

    parser.add_argument('dst', help='save path')

    parser.add_argument('type', help='model type: STDC1 or STDC2')

    args = parser.parse_args()

    checkpoint = CheckpointLoader.load_checkpoint(args.src, map_location='cpu')

    if 'state_dict' in checkpoint:

        state_dict = checkpoint['state_dict']

    elif 'model' in checkpoint:

        state_dict = checkpoint['model']

    else:

        state_dict = checkpoint

    assert args.type in ['STDC1',

                         'STDC2'], 'STD type should be STDC1 or STDC2!'

    weight = convert_stdc(state_dict, args.type)

    mmcv.mkdir_or_exist(osp.dirname(args.dst))

    torch.save(weight, args.dst)

if __name__ == '__main__':

    main()