Pytorch用Tensorboard来观察数据

pip install tensorboard

from torch.utils.tensorboard import SummaryWriter #调包

writer = SummaryWriter('logs') # 这里你创建了一个logs的文件装你的add_scalar生成的曲线，
#其中writer.add_scalar（）第一个量是曲线的名字， 
#第二个量是纵坐标scalar_value,第三个量是横坐标global_step（也可以理解为损失值得步长）
for i in range(100):
    writer.add_scalar("quadratic", i ** 2, i)

writer.close()

tensorboard --logdir=logs
#1.这个logdir的文件名必须要与之前所创建的文件名一致，不然很容易报错，No dashboards are active for the current data set.
#2.这个tensorboard输入的命令，必须是在logs文件的上一层文件中，不然也很容易报错，No dashboards are active for the current data set.

from torch.utils.tensorboard import SummaryWriter
import cv2

writer = SummaryWriter('logs')
aligned_img_path = "D:\\data\\basic\\Image\\aligned\\test_0001_aligned.jpg"
original_img_path = "D:\\data\\basic\\Image\\original\\test_0001.jpg"
aligned_img = cv2.imread(aligned_img_path)
original_img = cv2.imread(original_img_path)
print(type(aligned_img)) # numpy
print(aligned_img.shape)
# writer.add_image("img", aligned_img, 1, dataformats='HWC') #此图已经在我第一次测试add_image()用过了
writer.add_image("img", original_img, 2, dataformats='HWC')#此图是我在第二个测试
writer.close()