PyTorch中的TensorBoard

from tensorboardX import SummaryWriter
import numpy as np
from PIL import Image

#创建类的实例
writer = SummaryWriter("logs")#这个文件夹底下          
image_path = "dataset//train//ants//0013035.jpg"
img_PIL = Image.open(image_path)
img_array = np.array(img_PIL)


#两个方法
# writer.add_image()
# writer.add_scalar()      

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


#y=2x
for i in range(100):
    writer.add_scalar("y=2x",i*2,i)
       
                    #查看图像
writer.close()      #tensorboard --logdir=E:notebookpytorchpyTorch学习TensorBoardlogs

image_path = "dataset//train//ants//0013035.jpg"

from PIL import Image
img = Image.open(image_path)
print(type(img))  #图片类型为PIL类型

#转化图片类型
import numpy as np
img_array = np.array(img)
print(type(img_array))    #图片类型为numpy类型

from torchvision import transforms
from PIL import Image
import cv2
from torch.utilstransboard import SummaryWriter

#python的用法-》tensor 数据类型
#通过transforms.ToTensor去看两个问题
#1.transforms该如何使用
#2.为什么我们需要Tensor数据类型

#绝对路径：E:notebookpytorchpyTorch学习TensorBoarddatasettrainants013035.jpg
#相对路径：dataset/train/ants/0013035.jpg

img_path = "dataset/train/ants/0013035.jpg"
img = Image.open(img_path)
#print(img)  #可以看一下图片的类型

writer = SummaryWriter("logs")

#将图片类型转换为tensor类型
tensor_trans = transforms.ToTensor()    #1.transforms该如何使用
tensor_img = tensor_trans(img)          #先进行创建，需要什么创建什么工具transforms.ToTensor
#print(tensor_img)



writer.add_image("Tensor_img",tensor_img)
writer.close()

#在黑框框运行 tensorboard --logdir=logs 查看图片  或者tensorboard --logdir=E:notebookpytorchpyTorch学习TensorBoardlogs