train_bert_multi_label.py

#encoding:utf-8
import torch
import warnings
from pybert.train.losses import BCEWithLogLoss
from pybert.train.trainer import Trainer
from torch.utils.data import DataLoader
from pybert.io.dataset import CreateDataset
from pybert.io.data_transformer import DataTransformer
from pybert.utils.logginger import init_logger
from pybert.utils.utils import seed_everything
from pybert.config.basic_config import configs as config
from pybert.callback.lrscheduler import BertLR
from pybert.model.nn.bert_fine import BertFine
from pybert.preprocessing.preprocessor import EnglishPreProcessor
from pybert.callback.modelcheckpoint import ModelCheckpoint
from pybert.callback.trainingmonitor import TrainingMonitor
from pybert.train.metrics import F1Score,AccuracyThresh,MultiLabelReport
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.optimization import BertAdam
warnings.filterwarnings("ignore")

import os

# 主函数
def main():
    # **************************** 基础信息 ***********************
    logger = init_logger(log_name=config['model']['arch'], log_dir=config['output']['log_dir'])
    logger.info(f"seed is {config['train']['seed']}")
    device = f"cuda: {config['train']['n_gpu'][0] if len(config['train']['n_gpu']) else 'cpu'}"
    seed_everything(seed=config['train']['seed'],device=device)
    logger.info('starting load data from disk')
    id2label = {value: key for key, value in config['label2id'].items()}

    # **************************** 数据生成 ***********************
    DT = DataTransformer(logger = logger,seed = config['train']['seed'])
    # 读取数据集以及数据划分
    targets,sentences,_ = DT.read_data(raw_data_path = config['data']['raw_data_path'],
                                    preprocessor = EnglishPreProcessor(),
                                    is_train = True)

    train, valid = DT.train_val_split(X = sentences,y = targets,save=True,shuffle=True,stratify=False,
                                      valid_size  = config['train']['valid_size'],
                                      train_path  = config['data']['train_file_path'],
                                      valid_path  = config['data']['valid_file_path'],
                                      dev_path = config['data']['dev_file_path'])

    tokenizer = BertTokenizer(vocab_file=config['pretrained']['bert']['vocab_path'],
                              do_lower_case=config['train']['do_lower_case'])

    # train
    train_dataset   = CreateDataset(data = train,
                                    tokenizer = tokenizer,
                                    max_seq_len = config['train']['max_seq_len'],
                                    seed = config['train']['seed'],
                                    example_type = 'train')
    # valid
    valid_dataset   = CreateDataset(data= valid,
                                    tokenizer = tokenizer,
                                    max_seq_len  = config['train']['max_seq_len'],
                                    seed = config['train']['seed'],
                                    example_type = 'valid')
    #加载训练数据集
    train_loader = DataLoader(dataset     = train_dataset,
                              batch_size  = config['train']['batch_size'],
                              num_workers = config['train']['num_workers'],
                              shuffle     = True,
                              drop_last   = False,
                              pin_memory  = False)
    # 验证数据集
    valid_loader = DataLoader(dataset     = valid_dataset,
                              batch_size  = config['train']['batch_size'],
                              num_workers = config['train']['num_workers'],
                              shuffle     = False,
                              drop_last   = False,
                              pin_memory  = False)

    # **************************** 模型 ***********************
    logger.info("initializing model")
    model = BertFine.from_pretrained(config['pretrained']['bert']['bert_model_dir'],
                                     cache_dir=config['output']['cache_dir'],
                                     num_classes = len(id2label))

    # ************************** 优化器 *************************
    param_optimizer = list(model.named_parameters())
    no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
    optimizer_grouped_parameters = [
        {'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
        {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
        ]

    num_train_steps = int(
        len(train_dataset.examples) / config['train']['batch_size'] / config['train']['gradient_accumulation_steps'] * config['train']['epochs'])
    # t_total: total number of training steps for the learning rate schedule
    # warmup: portion of t_total for the warmup
    optimizer = BertAdam(optimizer_grouped_parameters,
                         lr = config['train']['learning_rate'],
                         warmup = config['train']['warmup_proportion'],
                         t_total = num_train_steps)

    # **************************** callbacks ***********************
    logger.info("initializing callbacks")
    # 模型保存
    model_checkpoint = ModelCheckpoint(checkpoint_dir   = config['output']['checkpoint_dir'],
                                       mode             = config['callbacks']['mode'],
                                       monitor          = config['callbacks']['monitor'],
                                       save_best_only   = config['callbacks']['save_best_only'],
                                       arch             = config['model']['arch'],
                                       logger           = logger)
    # 监控训练过程
    train_monitor = TrainingMonitor(file_dir = config['output']['figure_dir'],
                                    arch = config['model']['arch'])
    # 学习率机制
    lr_scheduler = BertLR(optimizer = optimizer,
                          learning_rate = config['train']['learning_rate'],
                          t_total = num_train_steps,
                          warmup = config['train']['warmup_proportion'])

    # **************************** training model ***********************
    logger.info('training model....')

    train_configs = {
        'model': model,
        'logger': logger,
        'optimizer': optimizer,
        'resume': config['train']['resume'],
        'epochs': config['train']['epochs'],
        'n_gpu': config['train']['n_gpu'],
        'gradient_accumulation_steps': config['train']['gradient_accumulation_steps'],
        'epoch_metrics':[F1Score(average='micro',task_type='binary'),MultiLabelReport(id2label = id2label)],
        'batch_metrics':[AccuracyThresh(thresh=0.5)],
        'criterion': BCEWithLogLoss(),
        'model_checkpoint': model_checkpoint,
        'training_monitor': train_monitor,
        'lr_scheduler': lr_scheduler,
        'early_stopping': None,
        'verbose': 1
    }

    trainer = Trainer(train_configs=train_configs)
    # 拟合模型
    trainer.train(train_data = train_loader,valid_data=valid_loader)
    # 释放显存
    if len(config['train']['n_gpu']) > 0:
        torch.cuda.empty_cache()

if __name__ == '__main__':
    main()