在Pytorch 训练模型的时候，需要日志帮助开发者记录些重要信息和参数，以方便开发者更好的调节模型及参数，常见的日志非 Tensorboard不可，但是Pytorch 对 Tensorboard 的支持不是十分完美，在记录模型重要参数时 Tensorboard 的 writer.add_text() 记录日志不是很方便，Logger 便可替代 Tensorboard 记录文本日志，Tensorboard 专注 模型训练图，两者搭配使用极大提高效率，且讲两者放在 Tensorboard 日志文件夹下，方便调用查看。

• 【解析】TensorBoard 可视化：https://blog.csdn.net/ViatorSun/article/details/116275485

1、train 文件

#  !/usr/bin/env  python
#  -*- coding:utf-8 -*-
# @Time     : 2022.01
# @Author   : 绿色羽毛
# @Email    : lvseyumao@foxmail.com
# @Blog     : https://blog.csdn.net/ViatorSun
# @version  : "1.0" 
# @Note     : Tensorboard + logger 日志使用import os
import socket
from datetime import datetime
from torch.utils.tensorboard import SummaryWriter""" ********************************* Tensorboard/日志 配置 ********************************* """
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
log_dir = os.path.join('runs', current_time + '_' + args.run_model + '_' + socket.gethostname())writer = SummaryWriter(log_dir)
logger = create_logger(log_dir, current_time + args.log_name)
config_from_args = args.__dict__
total_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad )
logger.info(f"=============================== Modle Arguments ===============================")
logger.info(f">>> Model Name        : {args.model_name, args.run_model}")
logger.info(f">>> Training time     : {current_time}")
logger.info(f">>> Computation with  : {current_time}")
logger.info(f"== Model Total params : %.2f MB \n" % (total_parameters / 1e6))
logger.info(f"== ArgumentParser:{json.dumps(config_from_args, indent=20)}")
logger.info(f"=============================== Train Arguments ===============================")
logger.info(f"== criterion     :{criterion}")
logger.info(f"== optimizer     :{optimizer.__class__}")
logger.info(f"                 :{json.dumps(optimizer.defaults, indent=20)}")logger.info(f"== lr_scheduler  :{lr_scheduler.__class__}")
logger.info(f"                 :{json.dumps(lr_scheduler.state_dict(), indent=20)}")
logger.info(f"==============================================================================")logger.info(f"Creating model:{args.model_name}")
logger.info(model)

生成的日志

2、创建logger 日志

#  !/usr/bin/env  python
#  -*- coding:utf-8 -*-
# @Time     : 2022.01
# @Author   : 绿色羽毛
# @Email    : lvseyumao@foxmail.com
# @Blog     : https://blog.csdn.net/ViatorSun
# @version  : "1.0" 
# @Note     : Pytorch 文本日志import os
import loggingdef create_logger(log_dir, filename, verbosity=1, name=None):level_dict = {0: logging.DEBUG, 1: logging.INFO, 2: logging.WARNING}formatter  = logging.Formatter( "[%(asctime)s][%(filename)s][line:%(lineno)d][%(levelname)s] %(message)s" )logger     = logging.getLogger(name)logger.setLevel(level_dict[verbosity])# fh = logging.FileHandler(filename, "w")fh = logging.FileHandler(os.path.join(log_dir, filename+'.txt'), mode='a')fh.setFormatter(formatter)logger.addHandler(fh)sh = logging.StreamHandler()sh.setFormatter(formatter)logger.addHandler(sh)return logger

3、Tensorboard 日志

在此截取一部分 Tensorboard 接口代码，可见在没有用户输入 log_dir 时，Tensorboard 会自动创建 runs文件夹，然后以当前时间 current_time + gethostname() + comment 命名Tensorboard日志的文件名，

class SummaryWriter(object):"""Writes entries directly to event files in the log_dir to beconsumed by TensorBoard.The `SummaryWriter` class provides a high-level API to create an event filein a given directory and add summaries and events to it. The class updates thefile contents asynchronously. This allows a training program to call methodsto add data to the file directly from the training loop, without slowing downtraining."""def __init__(self, log_dir=None, comment='', purge_step=None, max_queue=10,flush_secs=120, filename_suffix=''):"""Creates a `SummaryWriter` that will write out events and summariesto the event file.Args:log_dir (string): Save directory location. Default isruns/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.Use hierarchical folder structure to comparebetween runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc.for each new experiment to compare across them.comment (string): Comment log_dir suffix appended to the default``log_dir``. If ``log_dir`` is assigned, this argument has no effect.purge_step (int):When logging crashes at step :math:`T+X` and restarts at step :math:`T`,any events whose global_step larger or equal to :math:`T` will bepurged and hidden from TensorBoard.Note that crashed and resumed experiments should have the same ``log_dir``.max_queue (int): Size of the queue for pending events andsummaries before one of the 'add' calls forces a flush to disk.Default is ten items.flush_secs (int): How often, in seconds, to flush thepending events and summaries to disk. Default is every two minutes.filename_suffix (string): Suffix added to all event filenames inthe log_dir directory. More details on filename construction intensorboard.summary.writer.event_file_writer.EventFileWriter.Examples::from torch.utils.tensorboard import SummaryWriter# create a summary writer with automatically generated folder name.writer = SummaryWriter()# folder location: runs/May04_22-14-54_s-MacBook-Pro.local/# create a summary writer using the specified folder name.writer = SummaryWriter("my_experiment")# folder location: my_experiment# create a summary writer with comment appended.writer = SummaryWriter(comment="LR_0.1_BATCH_16")# folder location: runs/May04_22-14-54_s-MacBook-Pro.localLR_0.1_BATCH_16/"""torch._C._log_api_usage_once("tensorboard.create.summarywriter")if not log_dir:import socketfrom datetime import datetimecurrent_time = datetime.now().strftime('%b%d_%H-%M-%S')log_dir = os.path.join('runs', current_time + '_' + socket.gethostname() + comment)self.log_dir = log_dir