8TensorBoard的使用（二）

add_image（）的使用（常用来观察训练结果）

 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
        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``.

对于第二个参数img_tensor要求的格式
通过PIL工具打开图片，发现其不符合它的要求（通过opencv读入是numpy数组格式，直接符合）

安装opencv

利用numpy将刚的图片格式转为numpy格式

当把图片转换为numpy格式，仍报错，发现是numpy的数据存在不一致，需更改dataformats参数dataformats=‘HWC’，高宽通道

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

writer = SummaryWriter('logs')
image_path = "hymenoptera_data/train/ants_image/0013035.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
print(type(img_array))
print(img_array.shape)

writer.add_image("test",img_array,1,dataformats='HWC')


# y=2x
for i in range(100):
    writer.add_scalar("y=2x",3*i,i)     # scalar标量


writer.close()

在端口中查看

更改图片地址“hymenoptera_data/train/ants_image/5650366_e22b7e1065.jpg”，step=2
运行得，可以拖动

更改tag

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

writer = SummaryWriter('logs')
image_path = "hymenoptera_data/train/ants_image/7759525_1363d24e88.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)
print(type(img_array))
print(img_array.shape)

writer.add_image("train",img_array,1,dataformats='HWC')


# y=2x
for i in range(100):
    writer.add_scalar("y=2x",3*i,i)     # scalar标量

通过这种方式，可以很直观得看出我们在训练过程中给module提供了哪些数据，或对module进行测试时，可以看出每个阶段的输出结果。