在python model train里如何驯服野生log?

关键词：python 、epoch、loss、log

🤖: 记录模型的训练过程的步骤如下：

1. 导入logging模块。
2. 配置日志记录器，设置日志文件名、日志级别、日志格式等。
3. 在每个epoch结束时，使用logging模块记录性能指标、损失值、准确率等信息。
4. 在训练过程中，记录其他重要信息，比如学习率的变化、每个batch的损失值等。
5. 日志记录的信息可以帮助你更好地理解模型的训练过程，以及在后续分析和调试中提供有用的信息。

一、定义logger类

1. util里定义Logger

class Logger(object):"""Write console output to external text file.Code imported from https://github.com/Cysu/open-reid/blob/master/reid/utils/logging.py."""def __init__(self, fpath=None):self.console = sys.stdoutself.file = Noneif fpath is not None:self.file = open(fpath, 'a')def __del__(self):self.close()def __enter__(self):passdef __exit__(self, *args):self.close()def write(self, msg):self.console.write(msg+'\n')if self.file is not None:self.file.write(msg+'\n')def flush(self):self.console.flush()if self.file is not None:self.file.flush()os.fsync(self.file.fileno())def close(self):self.console.close()if self.file is not None:self.file.close()

2. train里调用

log_path = pjoin('./result', 'train', args.city, f'{args.tinterval}')
logger = util.Logger(pjoin(log_path, 'test.log'))
logger.write(f'\nTesting configs: {args}')
# use tensorboard to draw the curves.
train_writer = SummaryWriter(pjoin('./result', 'train', args.city, f'{args.tinterval}'))
val_writer = SummaryWriter(pjoin('./result', 'val', args.city, f'{args.tinterval}'))

logger.write(“文本提示”)

    logger.write("start training...")

• best id
• loss
• {变量: 格式d/f}
• 占位符：03d是一个格式化字符串，其中的0表示用0来填充空位，3表示总共占据3位，d表示这是一个十进制整数。因此，当i的值小于100时，会用0来填充，确保输出的字符串总共占据3位。

        if i%args.print_every == 0:logger.write(f'Epoch: {i:03d}, MAE: {mtrain_mae:.2f}, RMSE: {mtrain_rmse:.2f}, MAPE: {mtrain_mape:.2f}, Valid MAE: {mvalid_mae:.2f}, RMSE: {mvalid_rmse:.2f}, MAPE: {mvalid_mape:.2f}')torch.save(engine.model.state_dict(), save_path+"_epoch_"+str(i)+"_"+str(round(mvalid_mae,2))+".pth")logger.write("Average Training Time: {:.4f} secs/epoch".format(np.mean(train_time)))bestid = np.argmin(his_loss)engine.model.load_state_dict(torch.load(save_path+"_epoch_"+str(bestid+1)+"_"+str(round(his_loss[bestid],2))+".pth"))logger.write("Training finished")logger.write(f"The valid loss on best model is {str(round(his_loss[bestid],4))}")

二、自定义print log

def print_log(*values, log=None, end="\n"):print(*values, end=end)if log:if isinstance(log, str):log = open(log, "a")print(*values, file=log, end=end)log.flush()

1. 初始化日志文件

1. 记录时间
2. 保存路径
3. 文件名称

    # ------------------------------- make log file ------------------------------ #now = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")log_path = f"../logs/"if not os.path.exists(log_path):os.makedirs(log_path)log = os.path.join(log_path, f"{model_name}-{dataset}-{now}.log")log = open(log, "a")log.seek(0)log.truncate()

2. 模型记录epoch

    # --------------------------- train and test model --------------------------- #print_log(f"Loss: {criterion._get_name()}", log=log)print_log(log=log)model = train(model,trainset_loader,valset_loader,optimizer,scheduler,criterion,clip_grad=cfg.get("clip_grad"),max_epochs=cfg.get("max_epochs", 200),early_stop=cfg.get("early_stop", 10),verbose=1,log=log,save=save,)print_log(f"Saved Model: {save}", log=log)test_model(model, testset_loader, log=log)log.close()

3. 在每次调用的模型函数（train\test)里面保存需要的内容

train(arg, log = log)
将定义的log传入模型训练函数

out_str = f"Early stopping at epoch: {epoch+1}\n"out_str += f"Best at epoch {best_epoch+1}:\n"out_str += "Train Loss = %.5f\n" % train_loss_list[best_epoch]out_str += "Train RMSE = %.5f, MAE = %.5f, MAPE = %.5f\n" % (train_rmse,train_mae,train_mape,)out_str += "Val Loss = %.5f\n" % val_loss_list[best_epoch]out_str += "Val RMSE = %.5f, MAE = %.5f, MAPE = %.5f" % (val_rmse,val_mae,val_mape,)print_log(out_str, log=log)

print_log(需要保存的值, log = 定义的log)

4. log内容编辑

• f-strings 是指以f或F 开头的字符串，其中以 {}包含的表达式会进行值替换
• 在字符串前加r可防止字符串转义
• “文本提示字符串 ： { 属性值} 换行\n”
• %d 、%f

    for i in range(out_steps):rmse, mae, mape = RMSE_MAE_MAPE(y_true[:, i, :], y_pred[:, i, :])out_str += "Step %d RMSE = %.5f, MAE = %.5f, MAPE = %.5f\n" % (i + 1,rmse,mae,mape,)

5. 效果展示

---

三、 自定义logger

1. 函数定义

def get_logger(config, name=None):log_dir = './libcity/log'if not os.path.exists(log_dir):os.makedirs(log_dir)log_filename = '{}-{}-{}-{}.log'.format(config['exp_id'],config['model'], config['dataset'], get_local_time())logfilepath = os.path.join(log_dir, log_filename)logger = logging.getLogger(name)log_level = config.get('log_level', 'INFO')if log_level.lower() == 'info':level = logging.INFOelif log_level.lower() == 'debug':level = logging.DEBUGelif log_level.lower() == 'error':level = logging.ERRORelif log_level.lower() == 'warning':level = logging.WARNINGelif log_level.lower() == 'critical':level = logging.CRITICALelse:level = logging.INFOlogger.setLevel(level)formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')file_handler = logging.FileHandler(logfilepath)file_handler.setFormatter(formatter)console_formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')console_handler = logging.StreamHandler(sys.stdout)console_handler.setFormatter(console_formatter)logger.addHandler(file_handler)logger.addHandler(console_handler)logger.info('Log directory: %s', log_dir)return logger

2. 函数调用

    logger = get_logger(config)logger.info('Begin pipeline, task={}, model_name={}, dataset_name={}, exp_id={}'.format(str(task), str(model_name), str(dataset_name), str(exp_id)))logger.info(config.config)best_trial = result.get_best_trial("loss", "min", "last")logger.info("Best trial config: {}".format(best_trial.config))logger.info("Best trial final validation loss: {}".format(best_trial.last_result["loss"]))

四、自定义log_string()

1. utils.py

# log string
def log_string(log, string):log.write(string + '\n')log.flush()print(string)

2. main.py

import上面的utils

• 定义路径
• 打开log
• 写入log

parser.add_argument('--log_file', default='./data/log',help='log file')
args = parser.parse_args()
log = open(log_file, 'w')
# load data
log_string(log, 'loading data...')

• 记入时间：%.1fmin一位小数字符串，min分钟单位
• 调用变量值：%
• 或者字符串里：{:}.format(变量)

if __name__ == '__main__':start = time.time()loss_train, loss_val = train(model, args, log, loss_criterion, optimizer, scheduler)plot_train_val_loss(loss_train, loss_val, 'figure/train_val_loss.png')trainPred, valPred, testPred = test(args, log)end = time.time()log_string(log, 'total time: %.1fmin' % ((end - start) / 60))