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)))