def parse_argument():
parser = argparse.ArgumentParser(description="POS tagging")
parser.add_argument("-c",
"--config",
dest="config_file",
type=str,
default="./Config/config.cfg",
help="config path")
args = parser.parse_args()
config = configurable.Configurable(config_file=args.config_file)
# save file
config.mulu = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
subset_name = config.train_file.split('/')[-3]
subset_name_dir = os.path.join(config.save_checkpoint, subset_name)
if not os.path.isdir(subset_name_dir): os.makedirs(subset_name_dir)
config.save_dir = os.path.join(subset_name_dir, config.mulu)
if not os.path.isdir(config.save_dir): os.makedirs(config.save_dir)
logger = get_logger(os.path.join(config.save_dir, 'system.log'))
config.logger = logger
return config
def parse_argument():
"""
:argument
:return:
"""
parser = argparse.ArgumentParser(description="multi-SRL")
parser.add_argument("-c",
"--config",
dest="config_file",
type=str,
default="./Config/config.cfg",
help="config path")
args = parser.parse_args()
config = configurable.Configurable(config_file=args.config_file)
# save file
config.mulu = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
subset_name_dir = os.path.join(config.save_checkpoint)
if not os.path.isdir(subset_name_dir): os.makedirs(subset_name_dir)
config.save_dir = os.path.join(subset_name_dir, config.mulu + '.log')
logger = get_logger(config.save_dir)
config.logger = logger
return config
def parse_argument():
"""
:argument
:return:
"""
parser = argparse.ArgumentParser(description="Text Classification") #创建解析器
# 添加参数
parser.add_argument("-c", "--config", dest="config_file", type=str, default="./Config/config.cfg",
help="config path")
parser.add_argument("-device", "--device", dest="device", type=str, default="cpu",
help="device[‘cpu’,‘cuda:0’,‘cuda:1’,......]")
parser.add_argument("--train", dest="train", action="store_true", default=True, help="train model")
parser.add_argument("-p", "--process", dest="process", action="store_true", default=True, help="data process")
parser.add_argument("-t", "--test", dest="test", action="store_true", default=False, help="test model")
parser.add_argument("--t_model", dest="t_model", type=str, default=None, help="model for test")
parser.add_argument("--t_data", dest="t_data", type=str, default=None,
help="data[train dev test None] for test model")
parser.add_argument("--predict", dest="predict", action="store_true", default=False, help="predict model")
args = parser.parse_args() # 解析参数
config = configurable.Configurable(config_file=args.config_file)
config.device = args.device
config.train = args.train
config.process = args.process
config.test = args.test
config.t_model = args.t_model
config.t_data = args.t_data
config.predict = args.predict
# config
if config.test is True:
config.train = False
if config.t_data not in [None, "train", "dev", "test"]:
print("\nUsage")
parser.print_help()
print("t_data : {}, not in [None, 'train', 'dev', 'test']".format(config.t_data))
exit()
print("***************************************")
print("Data Process : {}".format(config.process))
print("Device : {}".format(config.device))
print("Train model : {}".format(config.train))
print("Test model : {}".format(config.test))
print("t_model : {}".format(config.t_model))
print("t_data : {}".format(config.t_data))
print("predict : {}".format(config.predict))
print("***************************************")
return config
"""
# define word dict
define_dict()
# load data
train_iter, dev_iter, test_iter = Load_Data()
# load pretrain embedding
load_preEmbedding()
# update config and print
update_arguments()
save_arguments()
model = load_model()
start_train(model, train_iter, dev_iter, test_iter)
if __name__ == "__main__":
print("Process ID {}, Process Parent ID {}".format(os.getpid(),
os.getppid()))
parser = argparse.ArgumentParser(description="Neural Networks")
parser.add_argument('--config_file', default="./Config/config.cfg")
config = parser.parse_args()
config = configurable.Configurable(config_file=config.config_file)
if config.cuda is True:
print("Using GPU To Train......")
# torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(seed_num)
torch.cuda.manual_seed_all(seed_num)
print("torch.cuda.initial_seed", torch.cuda.initial_seed())
main()
def parse_print():
parser = argparse.ArgumentParser(description="NER")
parser.add_argument("-c",
"--config",
dest="config_file",
type=str,
default="./Config/config.cfg",
help="config path")
parser.add_argument("-device",
"--device",
dest="device",
type=str,
default="cuda:0",
help="device[‘cpu’,‘cuda:0’,‘cuda:1’,......]")
parser.add_argument("--train",
dest="train",
action="store_true",
default=True,
help="TrainModel")
parser.add_argument("-p",
"--process",
dest="process",
action="store_true",
default=True,
help="DataProcess")
parser.add_argument("-t",
"--test",
dest="test",
action="store_true",
default=False,
help="TestModel")
parser.add_argument("--t_model",
dest="t_model",
type=str,
default=None,
help="model for test")
parser.add_argument("--t_data",
dest="t_data",
type=str,
default=None,
help="data[train dev test]")
parser.add_argument("--predict",
dest="predict",
action="store_true",
default=False,
help="PredictModel")
args = parser.parse_args()
# print(args.config_file)
# exit()
config = configurable.Configurable(config_file=args.config_file)
config.device = args.device
config.train = args.train
config.process = args.process
config.test = args.test
config.t_model = args.t_model
config.t_data = args.t_data
config.predict = args.predict
if config.test is True:
config.train = False
if config.t_data not in [None, "train", "dev", "test"]:
print("\nUsage")
parser.print_help()
print("t_data : {}, not in [None, 'train, 'dev', 'test']".format(
config.t_data))
exit()
print("+++++++++++++++++++++++++++++++++++++++++++")
print("DataProcess : {}".format(config.process))
print("TrainModel : {}".format(config.train))
print("TestModel : {}".format(config.test))
print("t_model : {}".format(config.t_model))
print("t_data : {}".format(config.t_data))
print("predict : {}".format(config.predict))
print("++++++++++++++++++++++++++++++++++++++++++++")
return config
if __name__ == "__main__":
random.seed(seed_num)
np.random.seed(seed_num)
print("Process ID {}, Process Parent ID {}".format(os.getpid(),
os.getppid()))
argparser = argparse.ArgumentParser(description="Script parameters")
argparser.add_argument('--config_file', default='../Config/config.cfg')
argparser.add_argument('--use-cuda', action='store_true', default=False)
argparser.add_argument('--thread', default=1, type=int, help='thread num')
argparser.add_argument('--tgt-word-file', default=None)
argparser.add_argument('--use-pretrain',
action='store_true',
default=False)
args, extra_args = argparser.parse_known_args()
config = configurable.Configurable(config_file=args.config_file,
extra_args=extra_args)
torch.set_num_threads(args.thread)
config.use_cuda = False
if gpu and args.use_cuda: config.use_cuda = True
print("\nGPU using status: ", config.use_cuda)
if config.cuda and torch.cuda.is_available():
print("Using GPU To Train......")
torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(seed_num)
torch.cuda.manual_seed_all(seed_num)
print("torch.cuda.initial_seed", torch.cuda.initial_seed())
main()
start_eval(model, test_data, test_y_data, weights)
if __name__ == "__main__":
print("Process ID {}, Process Parent ID {}".format(os.getpid(),
os.getppid()))
parser = argparse.ArgumentParser(description="Neural Networks")
parser.add_argument('--config_file', default="./Config/config.cfg")
parser.add_argument('--mode',
default="train",
choices=['train', 'eval'],
help='What mode?')
parser.add_argument('--model',
default="HCL_CLSTM",
choices=['HCL', 'HCL_CLSTM', 'HCL_CLSTM_CLSTM'],
help='What model?')
args = parser.parse_args()
config = configurable.Configurable(config_file=args.config_file)
if config.cuda is True:
print("Using GPU To Train......")
# torch.backends.cudnn.enabled = True
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed(seed_num)
torch.cuda.manual_seed_all(seed_num)
print("torch.cuda.initial_seed", torch.cuda.initial_seed())
main(args.mode, args.model)
def parse_argument():
"""
:argument
:return:
"""
parser = argparse.ArgumentParser(description="NER & POS")
parser.add_argument("-c",
"--config",
dest="config_file",
type=str,
default="./Config/config.cfg",
help="config path")
parser.add_argument("-device",
"--device",
dest="device",
type=str,
default="cpu",
help="device[‘cpu’,‘cuda:0’,‘cuda:1’,......]")
parser.add_argument("--train",
dest="train",
action="store_true",
default=True,
help="train model")
parser.add_argument("-p",
"--process",
dest="process",
action="store_true",
default=True,
help="data process")
parser.add_argument("-t",
"--test",
dest="test",
action="store_true",
default=False,
help="test model")
parser.add_argument("--t_model",
dest="t_model",
type=str,
default=None,
help="model for test")
parser.add_argument("--t_data",
dest="t_data",
type=str,
default=None,
help="data[train, dev, test, None] for test model")
parser.add_argument("--predict",
dest="predict",
action="store_true",
default=False,
help="predict model")
parser.add_argument("-tr1",
dest="train_repo1",
type=str,
help="train with repo1")
parser.add_argument("-test1",
dest="test_repo1",
type=str,
help="test with repo1")
args = parser.parse_args()
# print(vars(args))
config = configurable.Configurable(config_file=args.config_file)
config.device = args.device
config.train = args.train
config.process = args.process
config.test = args.test
config.t_model = args.t_model
config.t_data = args.t_data
config.predict = args.predict
# config
if args.train_repo1 == 'crf':
main_rep1('train', 'crf')
elif args.train_repo1 == 'bilstm':
main_rep1('train', 'bilstm')
elif args.train_repo1 == 'bilstm-crf':
main_rep1('train', 'bilstm-crf')
elif args.test_repo1 == 'crf':
main_rep1('test', 'crf')
elif args.test_repo1 == 'bilstm':
main_rep1('test', 'bilstm')
elif args.test_repo1 == 'bilstm-crf':
main_rep1('test', 'bilstm-crf')
else:
if config.test is True:
config.train = False
if config.t_data not in [None, "train", "dev", "test"]:
print("\nUsage")
parser.print_help()
print("t_data : {}, not in [None, 'train', 'dev', 'test']".format(
config.t_data))
exit()
print("***************************************")
print("Device : {}".format(config.device))
print("Data Process : {}".format(config.process))
print("Train model : {}".format(config.train))
print("Test model : {}".format(config.test))
print("t_model : {}".format(config.t_model))
print("t_data : {}".format(config.t_data))
print("predict : {}".format(config.predict))
print("***************************************")
return config
