def parse_args():
parser = configargparse.ArgumentParser(
description='preprocess.py',
config_file_parser_class=configargparse.YAMLConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
opts.config_opts(parser)
opts.add_md_help_argument(parser)
opts.preprocess_opts(parser)
opt = parser.parse_args()
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
return opt
def parse_args():
parser = configargparse.ArgumentParser(
description='preprocess.py',
config_file_parser_class=configargparse.YAMLConfigFileParser,
formatter_class=configargparse.ArgumentDefaultsHelpFormatter)
# 预处理所需要的全部参数均在opts模块中
opts.config_opts(parser)
opts.preprocess_opts(parser)
opt = parser.parse_args()
# 指定一个随机数会使得每次按顺序使用rand...产生的数值均相同
# 不知道在不同机器上是否相同, 如果相同的话结果便可以复现
torch.manual_seed(opt.seed)
return opt
def parse_args():
""" Parsing arguments """
parser = argparse.ArgumentParser(
description='preprocess.py',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# parser.add_argument("")
# group.add_argument('-train_dir', required=True, default='data/race_train.json',
# help="Path to the training data")
# group.add_argument('-valid_dir', required=True, default='data/race_dev.json',
# help="Path to the validation data")
# group.add_argument('-data_type', choices=["text"], help="""text""")
# group.add_argument('-save_data', required=True, default='data/processed',
# help="Output file for the prepared data")
opts.add_md_help_argument(parser)
opts.preprocess_opts(parser)
#print (parser.parse_args())
opt = parser.parse_args()
torch.manual_seed(opt.seed)
return opt
def parse_args():
""" Parsing arguments """
parser = argparse.ArgumentParser(
description='preprocess.py',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
opts.add_md_help_argument(parser)
opts.preprocess_opts(parser)
parser.add_argument('-parrel_run',
action='store_true',
default=False,
help='生成靶标')
parser.add_argument('-with_3d_confomer',
action='store_true',
default=False,
help='原子特征是否在最后3个维度加上坐标')
opt = parser.parse_args()
torch.manual_seed(opt.seed)
check_existing_pt_files(opt)
return opt
fields = inputters.get_fields(opt.data_type,
src_nfeats,
tgt_nfeats,
dynamic_dict=opt.dynamic_dict,
src_truncate=opt.src_seq_length_trunc,
tgt_truncate=opt.tgt_seq_length_trunc)
src_reader = inputters.str2reader[opt.data_type].from_opt(opt)
tgt_reader = inputters.str2reader["text"].from_opt(opt)
logger.info("Building & saving training data...")
train_dataset_files = build_save_dataset('train', fields, src_reader,
tgt_reader, opt)
if opt.valid_src and opt.valid_tgt:
logger.info("Building & saving validation data...")
build_save_dataset('valid', fields, src_reader, tgt_reader, opt)
logger.info("Building & saving vocabulary...")
build_save_vocab(train_dataset_files, fields, opt)
if __name__ == "__main__":
parser = ArgumentParser(description='preprocess.py')
opts.config_opts(parser)
opts.preprocess_opts(parser)
opt = parser.parse_args()
main(opt)
def _get_parser():
parser = ArgumentParser(description='preprocess.py')
opts.config_opts(parser)
opts.preprocess_opts(parser)
return parser
def _get_parser():
parser = ArgumentParser(description='preprocess.py')
opts.config_opts(parser)
opts.preprocess_opts(parser)
return parser
