DataLoader使用

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import torchvision
from torch.utils.data import DataLoader

# 测试集
from torch.utils.tensorboard import SummaryWriter

test_set = torchvision.datasets.CIFAR10(root="./dataset1", train=False, transform=torchvision.transforms.ToTensor())
"""
batch_size: 每次处理数据数量
shuffle: 顺序是否一致
num_workers: n进程加载
drop_last: 除不尽是否舍弃
"""
test_loader = DataLoader(dataset=test_set, batch_size=64, shuffle=True, num_workers=0, drop_last=True)

# 测试集当中第一张图片以及target
img, target = test_set[0]
print(test_set.data.size)
print(img.shape)
print(target)

writer = SummaryWriter("dataloader")
# dataloader随机取batch_size数量的data分别组合打包
for epoch in range(2):
    step = 0
    for data in test_loader:
        imgs, targets = data
        # print(imgs.shape)
        # print(targets)
        writer.add_images("Epoch:{}".format(epoch), imgs, step)
        step = step + 1

writer.close()