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# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import mmengine
import torch
from mmengine.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)
mmengine.mkdir_or_exist(osp.dirname(args.dst))
torch.save(weight, args.dst)
if __name__ == '__main__':
main()
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