Normanize归一化

from PIL import Image
from torch.utils.tensorboard import SummaryWriter
from torchvision import transforms
# 3.2 Normanize归一化
img_path = "D:/05深度学习笔记/Data/FirstTypeData/train/ants/0013035.jpg"
img = Image.open(img_path)

writer = SummaryWriter("logs1")

tensor_trans = transforms.ToTensor()
tensor_img = tensor_trans(img)  # 转化为张量
# 当一张图片被转换为张量时，像素值会被缩放到0-1
# 这通常是通过将像素值除以255来实现的
print(tensor_img[0][0][0])
# input[channel]=(input[chnnel]-mean[channel])/std[channel]

tensor_norm = transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
norm_img = tensor_norm(tensor_img)  # 归一化

print(norm_img[0][0][0])
writer.add_image("tensor_img", tensor_img)
writer.add_image("norm_img", norm_img)
writer.close()