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