DataLoader

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

# 准备的测试数据集  数据放在了CIFAR10文件夹下

test_data = torchvision.datasets.CIFAR10("./CIFAR10",
 train=False, transform=torchvision.transforms.ToTensor())
test_loader = DataLoader(dataset=test_data, batch_size=4, 
shuffle=True, num_workers=0, drop_last=False)

# 测试数据集中第一张图片及target
img, target = test_data[0]
print(img.shape)
print(target)

# 在定义test_loader时，设置了batch_size=4，表示一次性从数据集中取出4个数据
for data in test_loader:
    imgs, targets = data
    print(imgs.shape)
    print(targets)

# 在定义test_loader时，设置了batch_size=4，表示一次性从数据集中取出4个数据
writer = SummaryWriter("logs")
for epoch in range(2):
    step = 0
    for data in test_loader:
        imgs, targets = data
        writer.add_images("Epoch: {}".format(epoch), imgs, step)
        step = step + 1
writer.close()
                           

 把CIFAR10做成一个数据集，然后得到迭代器

每个迭代器包括图像和标签

下面是tensorboard的用法