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
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
log_dir = os.path.join('runs', current_time + '_' + socket.gethostname() )
writer = SummaryWriter(log_dir=log_dir) # Tensorboard日志
logger = create_logger(log_dir=log_dir ,filename="log") # logger日志
logger.info(f"Creating model:{model}")
logger.info(f"criterion:{criterion}")
logger.info(f"optimizer:{optimizer}")
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 logging
def 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 be
consumed by TensorBoard.
The `SummaryWriter` class provides a high-level API to create an event file
in a given directory and add summaries and events to it. The class updates the
file contents asynchronously. This allows a training program to call methods
to add data to the file directly from the training loop, without slowing down
training.
"""
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 summaries
to the event file.
Args:
log_dir (string): Save directory location. Default is
runs/**CURRENT_DATETIME_HOSTNAME**, which changes after each run.
Use hierarchical folder structure to compare
between 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 be
purged 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 and
summaries before one of the 'add' calls forces a flush to disk.
Default is ten items.
flush_secs (int): How often, in seconds, to flush the
pending events and summaries to disk. Default is every two minutes.
filename_suffix (string): Suffix added to all event filenames in
the log_dir directory. More details on filename construction in
tensorboard.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 socket
from datetime import datetime
current_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
