import os
import torch.backends.cudnn as cudnn
import yaml
from train import train
from utils import AttrDict
cudnn.benchmark = True
cudnn.deterministic = False
def get_config(file_path):
    with open(file_path, 'r', encoding="utf8") as stream:
        opt = yaml.safe_load(stream)
#     print(opt)
#     opt['symbol'] = '''.:,;'(!?)+ -*.="'''
    opt = AttrDict(opt)
    if opt.lang_char == 'None':
        characters = ''
        for data in opt['select_data'].split('-'):
            csv_path = os.path.join(opt['train_data'], data, 'labels.csv')
            df = pd.read_csv(csv_path, sep='^([^,]+),', engine='python', usecols=['filename', 'words'], keep_default_na=False)
            all_char = ''.join(df['words'])
            characters += ''.join(set(all_char))
        characters = sorted(set(characters))
        opt.character= ''.join(characters)
    else:
        opt.character = opt.number + opt.symbol + opt.lang_char
    os.makedirs(f'./saved_models/{opt.experiment_name}', exist_ok=True)
    return opt
opt = get_config("config_files/custom_model.yaml")\
train(opt, amp=False)

我遇到了保存错误。但是，如果你查看train.py的第280行，你会发现是代码本身导致退出。尽管这个错误(实际上根本不是错误),你应该能在saved_model文件夹中找到已保存的模型。

当训练结束时，通常不会出现问题。