# -*- coding: utf-8 -*- # @Time : 2020/10/20 11:03 下午 # @Author : lishouxian # @Email : [email protected] # @File : main.py # @Software: PyCharm from engines.data import DataManager from engines.utils.logger import get_logger from engines.train import train from engines.predict import Predictor from engines.utils.word2vec import Word2VecUtils from config import mode, classifier_config, word2vec_config import json if __name__ == '__main__': logger = get_logger('./logs') # 训练分类器 if mode == 'train_classifier': logger.info(json.dumps(classifier_config, indent=2)) data_manage = DataManager(logger) logger.info('mode: train_classifier') logger.info('model: {}'.format(classifier_config['classifier'])) train(data_manage, logger) # 测试分类 elif mode == 'interactive_predict': logger.info(json.dumps(classifier_config, indent=2)) data_manage = DataManager(logger) logger.info('mode: predict_one') logger.info('model: {}'.format(classifier_config['classifier'])) predictor = Predictor(data_manage, logger) predictor.predict_one('warm start')
print('vocabs fold not found, creating...')
if hasattr(configures, vocabs_dir):
os.mkdir(configures.vocabs_dir)
else:
os.mkdir(configures.datasets_fold + '/vocabs')
log_dir = 'log_dir'
if not os.path.exists(configures.log_dir):
print('log fold not found, creating...')
if hasattr(configures, log_dir):
os.mkdir(configures.log_dir)
else:
os.mkdir(configures.datasets_fold + '/vocabs')
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Bert_nlu_joint')
parser.add_argument('--config_file',
default='system.config',
help='Configuration File')
args = parser.parse_args()
configs = Configure(config_file=args.config_file)
fold_check(configs)
logger = get_logger(configs.log_dir)
configs.show_data_summary(logger)
set_env(configs)
mode = configs.mode.lower()
dataManager = DataManager(configs, logger)
train(configs, dataManager, logger)
if not os.path.exists(configures.datasets_fold):
print('datasets fold not found')
exit(1)
checkpoints_dir = 'checkpoints_dir'
if not os.path.exists(configures.checkpoints_dir) or not hasattr(
configures, checkpoints_dir):
print('checkpoints fold not found, creating...')
paths = configures.checkpoints_dir.split('/')
if len(paths) == 2 and os.path.exists(
paths[0]) and not os.path.exists(configures.checkpoints_dir):
os.mkdir(configures.checkpoints_dir)
else:
os.mkdir('checkpoints')
log_dir = 'log_dir'
if not os.path.exists(configures.log_dir):
print('log fold not found, creating...')
if hasattr(configures, log_dir):
os.mkdir(configures.log_dir)
else:
os.mkdir(configures.datasets_fold + '/vocabs')
if __name__ == '__main__':
logger = get_logger('logs')
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
mode = 'train'
if mode == 'train':
train(device, logger)