pytorch数据集

import torch
import torchvision
import torchvision.transforms as transforms
#torchvision 数据集的输出是范围在[0,1]之间的 PILImage，我们将他们转换成归一化范围为[-1,1]之间的张量 Tensors。
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset = torchvision.datasets.CIFAR10(root='./data',train=True,download=True,transform=transform)
trainloader = torch.utils.data.DataLoader(trainset,batch_size=4,shuffle=True,num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data',train=False,download=True,transform=transform)

testloader= torch.utils.data.DataLoader(testset,batch_size=4,shuffle=False,num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')

import matplotlib.pyplot as plt
import numpy as np

def imshow(img):
    img = img/2+0.5
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg,(1,2,0)))
    plt.show()
dataiter = iter(trainloader)
images, labels = dataiter.next()

imshow(torchvision.utils.make_grid(images))

print(''.join('%5s' % classes[labels[j]] for j in range(4)))