The “freeze_support()“ line can be omitted if the program is not going to be frozen to produ

这是在pytorch官网60分钟学习时遇到的一个问题，训练图像分类器中，一开始要下载训练集和测试集，其中下载代码如下

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

在训练的时候download都设置为True，因为我们要下载，但是在下载完之后要打开图片，就需要改为False，并且，还应该把训练图像的代码放入下方中，这样才可以正常运行

if __name__ == '__main__':

完整代码如下：

# Training an image classifier训练图像分类器
# 1. Loading and normalizing CIFAR10加载并标准化CIFAR10
import torch
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np


# If running on Windows and you get a BrokenPipeError, try setting the num_worker of torch.utils.data.DataLoader() to 0.
# 如果在Windows上运行时遇到BrokenPipeError，请尝试设置torch.utils.data.DataLoader（）为0。


# 下载训练集和测试集
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=False, 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=False, 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')

if __name__ == '__main__':

    # functions to show an image
    def imshow(img):
        img = img / 2 + 0.5     # unnormalize
        npimg = img.numpy()
        plt.imshow(np.transpose(npimg, (1, 2, 0)))
        plt.show()


    # get some random training images
    dataiter = iter(trainloader)
    images, labels = dataiter.next()

    # show images
    imshow(torchvision.utils.make_grid(images))
    # print labels
    print(' '.join('%5s' % classes[labels[j]] for j in range(4)))