pytorch框架学习(7) tensorboard使用

from torch.utils.tensorboard import SummarWriter

SummarWriter 是一个写 可以被tensorboard解析 的事件文件的 类
Writes entries directly to event files in the log_dir to be consumed by TensorBoard.
’ SummaryWriter '类提供了一个高级API，可以在给定目录中创建一个事件文件，并向其中添加摘要和事件。该类异步更新文件内容。这允许训练程序调用方法直接从训练循环向文件中添加数据，而不会减慢训练速度。

以下是class SummarWriter(object)的__init__

    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/ """

创建一个实例

writer = SummaryWriter("")

add_scalar()的使用 【向Summary中添加标量】

    def add_scalar(self, tag, scalar_value, global_step=None, walltime=None):
        """Add scalar data to summary. Args: tag (string): Data identifier # 相当于图表的Title scalar_value (float or string/blobname): Value to save # 想要去保存的数值，相当于y轴 global_step (int): Global step value to record # 相当于x轴 walltime (float): Optional override default walltime (time.time()) with seconds after epoch of event 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 % """
        torch._C._log_api_usage_once("tensorboard.logging.add_scalar")
        if self._check_caffe2_blob(scalar_value):
            from caffe2.python import workspace
            scalar_value = workspace.FetchBlob(scalar_value)
        self._get_file_writer().add_summary(
            scalar(tag, scalar_value), global_step, walltime)

add_image()的使用【】

 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 # 标题 img_tensor (torch.Tensor, numpy.array, or string/blobname): Image data # 注意图像的类型应该是Tensor或者numpy.array或者string/blobname global_step (int): Global step value to record # 步骤 walltime (float): Optional override default walltime (time.time()) seconds after epoch of event 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 % """
        torch._C._log_api_usage_once("tensorboard.logging.add_image")
        if self._check_caffe2_blob(img_tensor):
            from caffe2.python import workspace
            img_tensor = workspace.FetchBlob(img_tensor)
        self._get_file_writer().add_summary(
            image(tag, img_tensor, dataformats=dataformats), global_step, walltime)

• 值得注意的是：输入图像的格式必须是tensor或者np.array或者str
• 格式应该是(C, H, W)否则会报错。当然可以命令来指定格式

如何打开tensorboard文件？

我经常使用的方法：
首先Win+R 输入cmd进入 Terminal后，激活相应的环境。
其次进入到events文件的所在文件夹的父级文件夹(这里是runs)

之后指定events文件的所在文件夹，命令如：tensorboard --logdir=文件夹(这里是Aug…)

最后在浏览器中把 http://localhost:6006/打开就OK了~~(下图所对应的结果展示如下下图)

• 追加一个小点(一般用不到)：端口默认是6006，如果冲突了我们可以更换端口。
  tensorboard --logdir=文件夹 --port=6007 就可以把端口换为6007