def parse(parser):
parser.add_argument('--conf', '-c', help='path to config file')
parser.add_argument('--path', '-p', help='path to model file')
parser.add_argument('--device', '-d', default='-1', help='ID of GPU to use')
parser.add_argument('--seed', '-s', default=1, type=int, help='seed for generating random numbers')
parser.add_argument('--threads', '-t', default=16, type=int, help='max num of threads')
parser.add_argument('--batch-size', default=5000, type=int, help='batch size')
parser.add_argument("--local_rank", type=int, default=-1, help='node rank for distributed training')
args, unknown = parser.parse_known_args()
args, _ = parser.parse_known_args(unknown, args)
args = Config(**vars(args))
Parser = args.pop('Parser')
torch.set_num_threads(args.threads)
torch.manual_seed(args.seed)
init_device(args.device, args.local_rank)
init_logger(logger, f"{args.path}.{args.mode}.log")
logger.info('\n' + str(args))
if args.mode == 'train':
parser = Parser.build(**args)
parser.train(**args)
elif args.mode == 'evaluate':
parser = Parser.load(args.path)
print(args)
parser.evaluate(**args)
elif args.mode == 'predict':
parser = Parser.load(args.path)
parser.predict(**args)
def parse(parser):
parser.add_argument('--conf', '-c', help='path to config file')
parser.add_argument('--path', '-p', help='path to model file')
parser.add_argument('--device',
'-d',
default='-1',
help='ID of GPU to use')
parser.add_argument('--seed',
'-s',
default=1,
type=int,
help='seed for generating random numbers')
parser.add_argument('--threads',
'-t',
default=16,
type=int,
help='max num of threads')
parser.add_argument('--batch-size',
default=5000,
type=int,
help='batch size')
parser.add_argument("--local_rank",
type=int,
default=-1,
help='node rank for distributed training')
parser.add_argument(
'--min_freq',
default=2,
type=int,
help='minimum frequency needed to include a token in the vocabulary')
args, unknown = parser.parse_known_args()
args, _ = parser.parse_known_args(unknown, args)
args = Config(**vars(args))
Parser = args.pop('Parser')
torch.set_num_threads(args.threads)
torch.manual_seed(args.seed)
init_device(args.device, args.local_rank)
init_logger(logger, f"{args.path}.{args.mode}.log")
logger.info('\n' + str(args))
if args.mode == 'train':
# min_freq看看是多少
parser = Parser.build(**args)
args.update({'mu': .0, 'nu': 0.95, 'lr': 1e-3, 'weight_decay': 3e-9})
parser.train(**args)
elif args.mode == 'evaluate':
parser = Parser.load(args.path)
parser.evaluate(**args)
elif args.mode == 'predict':
parser = Parser.load(args.path)
parser.predict(**args)