run.py

from configs.base import ParamManager
from dataloaders.base import DataManager
from backbones.base import ModelManager
from methods import method_map
from utils.functions import save_results
import logging
import argparse
import sys
import os
import datetime

def parse_arguments():

    parser = argparse.ArgumentParser()

    parser.add_argument('--type', type=str, default='open_intent_detection', help="Type style.")

    parser.add_argument('--logger_name', type=str, default='Detection', help="Logger name for open intent detection.")

    parser.add_argument('--log_dir', type=str, default='logs', help="Logger directory.")

    parser.add_argument("--dataset", default='banking', type=str, help="The name of the dataset to train selected")

    parser.add_argument("--known_cls_ratio", default=0.75, type=float, help="The number of known classes")
    
    parser.add_argument("--labeled_ratio", default=1.0, type=float, help="The ratio of labeled samples in the training set")
    
    parser.add_argument("--method", type=str, default='ADB', help="which method to use")

    parser.add_argument("--train", action="store_true", help="Whether to train the model")

    parser.add_argument("--pretrain", action="store_true", help="Whether to pre-train the model")

    parser.add_argument("--save_model", action="store_true", help="save trained-model for open intent detection")

    parser.add_argument("--backbone", type=str, default='bert', help="which backbone to use")

    parser.add_argument("--config_file_name", type=str, default='ADB.py', help = "The name of the config file.")

    parser.add_argument('--seed', type=int, default=0, help="random seed for initialization")

    parser.add_argument("--gpu_id", type=str, default='0', help="Select the GPU id")

    parser.add_argument("--pipe_results_path", type=str, default='pipe_results', help="the path to save results of pipeline methods")
    
    parser.add_argument("--data_dir", default = sys.path[0]+'/../data', type=str,
                        help="The input data dir. Should contain the .csv files (or other data files) for the task.")

    parser.add_argument("--output_dir", default= '/home/sharing/disk1/zhaoshaojie/baseline_test/TEXTOIR', type=str, 
                        help="The output directory where all train data will be written.") 

    parser.add_argument("--model_dir", default='models', type=str, 
                        help="The output directory where the model predictions and checkpoints will be written.") 

    parser.add_argument("--load_pretrained_method", default=None, type=str, 
                        help="The output directory where the model predictions and checkpoints will be written.") 

    parser.add_argument("--result_dir", type=str, default = 'results', help="The path to save results")

    parser.add_argument("--results_file_name", type=str, default = 'results.csv', help="The file name of all the results.")

    parser.add_argument("--save_results", action="store_true", help="save final results for open intent detection")

    parser.add_argument("--loss_fct", default="CrossEntropyLoss", help="The loss function for training.")

    args = parser.parse_args()

    return args

def set_logger(args):


    if not os.path.exists(args.log_dir):
        os.makedirs(args.log_dir)
    
    time = datetime.datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
    file_name = f"{args.method}_{args.dataset}_{args.backbone}_{args.known_cls_ratio}_{args.labeled_ratio}_{time}.log"
    
    logger = logging.getLogger(args.logger_name)
    logger.setLevel(logging.DEBUG)

    fh = logging.FileHandler(os.path.join(args.log_dir, file_name))
    fh_formatter = logging.Formatter('%(asctime)s - %(name)s - %(message)s')
    fh.setFormatter(fh_formatter)
    fh.setLevel(logging.DEBUG)
    logger.addHandler(fh)

    ch = logging.StreamHandler()
    ch.setLevel(logging.INFO)
    ch_formatter = logging.Formatter('%(name)s - %(message)s')
    ch.setFormatter(ch_formatter)
    logger.addHandler(ch)

    return logger

def run(args, data, model, logger):

    method_manager = method_map[args.method]
    method = method_manager(args, data, model, logger_name = args.logger_name)
    
    if args.train:
        
        logger.info('Training Begin...')
        method.train(args, data)
        logger.info('Training Finished...')

    logger.info('Testing begin...')
    outputs = method.test(args, data)
    logger.info('Testing finished...')

    if args.save_results:
        logger.info('Results saved in %s', str(os.path.join(args.result_dir, args.results_file_name)))
        save_results(args, outputs)


if __name__ == '__main__':
    
    sys.path.append('.')
    args = parse_arguments()
    logger = set_logger(args)

    logger.info('Open Intent Detection Begin...')
    logger.info('Parameters Initialization...')
    param = ParamManager(args)
    args = param.args

    logger.debug("="*30+" Params "+"="*30)
    for k in args.keys():
        logger.debug(f"{k}:\t{args[k]}")
    logger.debug("="*30+" End Params "+"="*30)

    logger.info('Data and Model Preparation...')
    data = DataManager(args, logger_name = args.logger_name)
    model = ModelManager(args, data, logger_name = args.logger_name)


    run(args, data, model, logger)
    logger.info('Open Intent Detection Finished...')