import torchvision as tv
import torchvision.transforms as transforms
from torchvision.transforms import ToPILImage
show=ToPILImage()#可以把Tensor转成Image,方便可视化


#定义对数据的预处理
transform=transforms.Compose([transforms.ToTensor(),#转化为Tensor
                             transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5),)#归一化
                             ])


#训练集
trainset=tv.datasets.CIFAR10(
    root='/media/z/CC/',
    train=True,
    download=True,
    transform=transform)

trainloader=t.utils.data.DataLoader(trainset,
                                   batch_size=4,
                                   shuffle=True,
                                   num_workers=1)

#测试集
testset=tv.datasets.CIFAR10('/media/z/CC/',
                           train=False,
                           download=True,
                           transform=transform)

testloader=t.utils.data.DataLoader(testset,
                                  batch_size=4,
                                  shuffle=False,
                                  num_workers=1)

classes=('plane','car','bird','cat',
        'deer','dog','frog','horse','ship','truck')
#(data,label)=trainset[100]
#print(classes[label])
#show((data+1)/2).resize((100,100))
dataiter=iter(trainloader)
images,labels=dataiter.next()#返回4张图片及标签
print(''.join('%11s'%classes[labels[j]] for j in range(4)))
show(tv.utils.make_grid((images+1)/2)).resize((400,100))
                                  

输出 

  plane        car      truck       deer