PyTorch入门：（四）torchvision中数据集的使用

import torchvision

train_set = torchvision.datasets.CIFAR10(root="./dataset_CIFAR10", train=True, download=True) # 下载训练集
test_set = torchvision.datasets.CIFAR10(root="./dataset_CIFAR10", train=False, download=True) # 下载测试集

Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./dataset_CIFAR10\cifar-10-python.tar.gz

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# 查看图片和label
img,target = test_set[0]
img
test_set.classes[target]

'cat'

from torch.utils.tensorboard import SummaryWriter
dataset_transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])
train_set = torchvision.datasets.CIFAR10(root="./dataset_CIFAR10", train=True,transform=dataset_transform, download=False) # 转换训练集
test_set = torchvision.datasets.CIFAR10(root="./dataset_CIFAR10", train=False,transform=dataset_transform, download=False) # 转换测试集
writer = SummaryWriter("dataset_CIFAR10_tensor")
for i in range(10):
    img, target = train_set[i]
    writer.add_image("test_set", img, i)
writer.close()