Dataloader

1.去PyTorch官网上查找Dataloader的文档

使用测试集的原因是测试集样本数更少，运行起来时间会短一些（指用DataLoader遍历的时间）：

import torchvision

# 准备的测试数据集
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor())

test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=False)

# 测试数据中的第一张图片
img, target = test_data[0]
print(img.shape)
print(target)

writer = SummaryWriter("dataloader")
step = 0
for data in test_loader:
    imgs, targets = data
    # print(imgs.shape)
    # print(targets)
    writer.add_images("test_data", imgs, step)
    step = step + 1

writer.close()

drop_last参数

windows系统下如果出现BrokenPipeError的错误，可以考虑将 num_workers设置为0

2.采样器默认是随机采样

import torchvision

# 准备的测试数据集
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter

test_data = torchvision.datasets.CIFAR10("./dataset", train=False, transform=torchvision.transforms.ToTensor())

test_loader = DataLoader(dataset=test_data, batch_size=64, shuffle=True, num_workers=0, drop_last=True)

# 测试数据中的第一张图片
img, target = test_data[0]
print(img.shape)
print(target)

writer = SummaryWriter("dataloader")

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()

用TensorBoard展示图片

①注意要用add_images而不是之前用过的add_image
②一定要等到程序运行结束了再在Terminal（终端）里使用tensorboard命令，不然会出现图片加载不全的情况！可以在程序末尾加一行语句来打印程序结束。