Pytorch学习--Tensorboard的使用

一、目标

辅助显示中间结果：

二、头文件

from torch.utils.tensorboard import SummaryWriter

三、SummaryWriter 用法

只需要关注第一个参数即可。将文件写到 log_dir 文件夹中（通过第一个参数指定 log_dir 的地址）。

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/

        """

四、SummaryWriter 的主要函数

from torch.utils.tensorboard import SummaryWriter
writer=SummaryWriter("logs")
writer.add_image()
writer.add_scalar()
writer.close()

add_scalar()的用法：

    def add_scalar(
        self,
        tag,
        scalar_value,
        global_step=None,
        walltime=None,
        new_style=False,
        double_precision=False,
    ):
        """Add scalar data to summary.

        Args:
            tag (string): Data identifier
            scalar_value (float or string/blobname): Value to save
            global_step (int): Global step value to record
            walltime (float): Optional override default walltime (time.time())
              with seconds after epoch of event
            new_style (boolean): Whether to use new style (tensor field) or old
              style (simple_value field). New style could lead to faster data loading.
        Examples::

            from torch.utils.tensorboard import SummaryWriter
            writer = SummaryWriter()
            x = range(100)
            for i in x:
                writer.add_scalar('y=2x', i * 2, i)
            writer.close()

        Expected result:

        .. image:: _static/img/tensorboard/add_scalar.png
           :scale: 50 %

        """

我们的代码：

from torch.utils.tensorboard import SummaryWriter
writer=SummaryWriter("logs")
# writer.add_image()
# y=x
for i in range(100):
    writer.add_scalar("y=x",i,i)
writer.close()

展示 tensorboard 的指令：tensorboard --logdir=logs --port=6007，输入到终端，终端显示：

TensorFlow installation not found - running with reduced feature set.
Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all
TensorBoard 2.18.0 at http://localhost:6006/ (Press CTRL+C to quit)

所以我们要在网址：http://localhost:6006/ 打开它

如果图片第一次运行显示的是正确的，后面显示的是错误的，可能原因为：TensorBoard 可能在同一个 run 中展示了多个训练过程或多次运行的数据。如果不同的数据文件被加载到同一张图中，曲线可能会被覆盖或扭曲。尝试清理缓存并确保只加载你期望的运行日志。

add_image()的用法

首先，我们下载数据集 data ，我将数据集存入了以下路径：“E:\pytorch学习\data”

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

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

        Args:
            tag (string): Data identifier
            img_tensor (torch.Tensor, numpy.array, 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 (string): Image data format specification of the form
              CHW, HWC, HW, WH, etc.
        Shape:
            img_tensor: Default is :math:`(3, H, W)`. You can use ``torchvision.utils.make_grid()`` to
            convert a batch of tensor into 3xHxW format or call ``add_images`` and let us do the job.
            Tensor with :math:`(1, H, W)`, :math:`(H, W)`, :math:`(H, W, 3)` is also suitable as long as
            corresponding ``dataformats`` argument is passed, e.g. ``CHW``, ``HWC``, ``HW``.

        Examples::

            from torch.utils.tensorboard import SummaryWriter
            import numpy as np
            img = np.zeros((3, 100, 100))
            img[0] = np.arange(0, 10000).reshape(100, 100) / 10000
            img[1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000

            img_HWC = np.zeros((100, 100, 3))
            img_HWC[:, :, 0] = np.arange(0, 10000).reshape(100, 100) / 10000
            img_HWC[:, :, 1] = 1 - np.arange(0, 10000).reshape(100, 100) / 10000

            writer = SummaryWriter()
            writer.add_image('my_image', img, 0)

            # If you have non-default dimension setting, set the dataformats argument.
            writer.add_image('my_image_HWC', img_HWC, 0, dataformats='HWC')
            writer.close()

        Expected result:

        .. image:: _static/img/tensorboard/add_image.png
           :scale: 50 %

        """

把图片从 PIL 格式转换成 ndarray 格式：
我们的代码：

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

writer=SummaryWriter("logs")
# 1 --test
image_path="data/train/ants_image/0013035.jpg"
img_PIL=Image.open(image_path)
img_array=np.array(img_PIL)

# 2 --test
image_path_2="data/train/bees_image/17209602_fe5a5a746f.jpg"
img_PIL_2=Image.open(image_path_2)
img_array_2=np.array(img_PIL_2)

# 3 --train
image_path_3="data/train/bees_image/36900412_92b81831ad.jpg"
img_PIL_3=Image.open(image_path_3)
img_array_3=np.array(img_PIL_3)

writer.add_image("test",img_array,1,dataformats="HWC")
writer.add_image("test",img_array_2,2,dataformats="HWC")
writer.add_image("train",img_array_3,1,dataformats="HWC")

# y=2x
for i in range(100):
    writer.add_scalar("y=2x",2*i,i)
writer.close()

参考视频：https://www.bilibili.com/video/BV1hE411t7RN?spm_id_from=333.788.videopod.episodes&vd_source=c80b4e7c14c4bcf14561eb370cf8a2c8&p=9