Pytorch深度学习------TensorBoard的使用

1.什么是TensorBoard

TensorBoard是一个工具，主要用于数据可视化，用大白话的语言来说就是可以记录在机器学习中表格数据、非表格数据（图片、文本、音频等）等变化，从而在模型中更直观的显示。

2.安装

一般安装新版的pytorch会自动安装，如果没安装，则在终端命令行下使用pip install tensorboard语法指令即可安装

3.启动

在终端下使用如下语法：tensorboard --logdir=
其中directory_name为log文件所在的目录，因为Tensorboard面板中展示的数据都来源于log文件，一次完整的运行生成一份log文件。
如下：

此时访问：http://localhost:6006/?darkMode=true
出现如下界面即表示成功

可以自己指定端口：
此时访问就是6007端口了

4.Pytorch 使用 TensorBoard

SummaryWriter类，该类的初始化参数主要有如下几个：

    """

    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 (str): 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 (str): 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 (str): 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/

        """

主要的参数是log_dir，他的设定就是对应log文件所对应的目录文件夹。
主要方法

add_image（）添加图片
add_scalar（）添加标量数据

4.1 写入数据

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image

writer = SummaryWriter("logs")  # 创建SummaryWriter，为log文件起目录

for i in range(100):
    writer.add_scalar("y=2x",2*i,i)  # 第一个参数相当于标题，第二个参数就相当于纵坐标的值，第三个参数就相当于横坐标的值

writer.close()

命令行打开TensorBoard面板：logs为上面创建的文件夹

访问：http://localhost:6006/?darkMode=true#timeseries 就能看到

4.2 写入图像

add_image（）方法

    def add_images(
        self, tag, img_tensor, global_step=None, walltime=None, dataformats="NCHW"
    ):
        """Add batched image data to summary.

        Note that this requires the ``pillow`` package.

        Args:
            tag (str): Data identifier
            img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): Image data
            global_step (int): Global step value to record
            walltime (float): Optional override default walltime (time.time())
              seconds after epoch of event
            dataformats (str): Image data format specification of the form
              NCHW, NHWC, CHW, HWC, HW, WH, etc.
        Shape:
            img_tensor: Default is :math:`(N, 3, H, W)`. If ``dataformats`` is specified, other shape will be
            accepted. e.g. NCHW or NHWC.

        Examples::

            from torch.utils.tensorboard import SummaryWriter
            import numpy as np

            img_batch = np.zeros((16, 3, 100, 100))
            for i in range(16):
                img_batch[i, 0] = np.arange(0, 10000).reshape(100, 100) / 10000 / 16 * i
                img_batch[i, 1] = (1 - np.arange(0, 10000).reshape(100, 100) / 10000) / 16 * i

            writer = SummaryWriter()
            writer.add_images('my_image_batch', img_batch, 0)
            writer.close()

tag这个参数就是定义标题
img_tensor (torch.Tensor, numpy.ndarray, or string/blobname): 可见这个参数规定了读取的图片类型需要为torch.Tensor, numpy.ndarray, or string/blobname这几种类型，所以需要利用OpenCV读取图片，获得numpy类型数据、或者numpy直接转。下面将使用numpy获取。
global_step=None这个参数是指定图片在第几步显示，因为打开TensorBoard面板后会有可移动的step进度条，能供移动显示该图片是位于当前tag下的第几张。
walltime=None这个参数记录发生的时间，默认为 time.time()。
dataformats="NCHW"这个参数是图像数据的格式（图像都有长宽通道三个数值），默认为 ‘CHW’，即 Channel x Height x Width，还可以是 ‘CHW’、‘HWC’ 或 ‘HW’ 等。

from torch.utils.tensorboard import SummaryWriter
import numpy as np
from PIL import Image

writer = SummaryWriter("logs")  # 创建SummaryWriter，为log文件起目录
img_path = "hymenoptera_data/train/ants_image/5650366_e22b7e1065.jpg"
img_PIL = Image.open(img_path)
# 因为add_image（）这个函数的第二个参数需要的类型为 (torch.Tensor, numpy.ndarray, or string/blobname)这几个才可以，而原本的img_PIL类型不属于那几个
img_array = np.array(img_PIL)
writer.add_image("test",img_array,2,dataformats="HWC")
writer.close()