1. Dataloader 使用
在 PyTorch 中，数据加载主要依赖两个核心类：Dataset与DataLoader。
- Dataset：负责告诉程序数据集存放在什么位置，以及如何通过索引（如索引 0）获取对应的数据样本。
- DataLoader：负责将数据加载到神经网络中。它每次从
Dataset中取数据，具体取多少、如何取，由DataLoader的参数决定。
1.1 代码示例
importtorchvisionfromtorch.utils.dataimportDataLoader# 准备的测试数据集test_data=torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor())img,target=test_data[0]print(img.shape)print(img)# batch_size=4 使得 img0, target0 = dataset[0]、img1, target1 = dataset[1]、# img2, target2 = dataset[2]、img3, target3 = dataset[3]，# 然后这四个数据作为 Dataloader 的一个返回test_loader=DataLoader(dataset=test_data,batch_size=4,shuffle=True,num_workers=0,drop_last=False)# 用 for 循环取出 DataLoader 打包好的四个数据fordataintest_loader:imgs,targets=data# 每个 data 都是由 4 张图片组成，# imgs.size 为 [4,3,32,32]，四张 32×32 图片三通道，# targets 由四个标签组成print(imgs.shape)print(targets)1.2 运行结果
torch.Size([3, 32, 32]) tensor([[[0.6196, 0.6235, 0.6471, ..., 0.5373, 0.4941, 0.4549], [0.5961, 0.5922, 0.6235, ..., 0.5333, 0.4902, 0.4667], [0.5922, 0.5922, 0.6196, ..., 0.5451, 0.5098, 0.4706], ..., [0.2667, 0.1647, 0.1216, ..., 0.1490, 0.0510, 0.1569], [0.2392, 0.1922, 0.1373, ..., 0.1020, 0.1137, 0.0784], [0.2118, 0.2196, 0.1765, ..., 0.0941, 0.1333, 0.0824]], [[0.4392, 0.4353, 0.4549, ..., 0.3725, 0.3569, 0.3333], [0.4392, 0.4314, 0.4471, ..., 0.3725, 0.3569, 0.3451], [0.4314, 0.4275, 0.4353, ..., 0.3843, 0.3725, 0.3490], ..., [0.4863, 0.3922, 0.3451, ..., 0.3804, 0.2510, 0.3333], [0.4549, 0.4000, 0.3333, ..., 0.3216, 0.3216, 0.2510], [0.4196, 0.4118, 0.3490, ..., 0.3020, 0.3294, 0.2627]], [[0.1922, 0.1843, 0.2000, ..., 0.1412, 0.1412, 0.1294], [0.2000, 0.1569, 0.1765, ..., 0.1216, 0.1255, 0.1333], [0.1843, 0.1294, 0.1412, ..., 0.1333, 0.1333, 0.1294], ..., [0.6941, 0.5804, 0.5373, ..., 0.5725, 0.4235, 0.4980], [0.6588, 0.5804, 0.5176, ..., 0.5098, 0.4941, 0.4196], [0.6275, 0.5843, 0.5176, ..., 0.4863, 0.5059, 0.4314]]]) torch.Size([4, 3, 32, 32]) tensor([2, 6, 4, 5]) torch.Size([4, 3, 32, 32]) tensor([7, 2, 1, 6]) torch.Size([4, 3, 32, 32]) tensor([6, 7, 4, 4]) torch.Size([4, 3, 32, 32]) tensor([9, 6, 1, 3]) torch.Size([4, 3, 32, 32]) tensor([1, 5, 8, 5]) torch.Size([4, 3, 32, 32]) tensor([1, 8, 4, 6]) torch.Size([4, 3, 32, 32]) tensor([9, 8, 7, 1]) torch.Size([4, 3, 32, 32]) tensor([6, 0, 9, 4]) torch.Size([4, 3, 32, 32]) tensor([7, 1, 9, 9]) torch.Size([4, 3, 32, 32]) tensor([6, 8, 7, 0]) torch.Size([4, 3, 32, 32]) tensor([6, 8, 1, 5]) torch.Size([4, 3, 32, 32]) tensor([5, 2, 7, 8]) torch.Size([4, 3, 32, 32]) tensor([7, 6, 7, 0]) torch.Size([4, 3, 32, 32]) tensor([9, 2, 0, 0]) torch.Size([4, 3, 32, 32]) tensor([0, 6, 0, 8]) torch.Size([4, 3, 32, 32]) tensor([6, 7, 3, 1]) torch.Size([4, 3, 32, 32]) tensor([3, 2, 6, 0]) torch.Size([4, 3, 32, 32]) tensor([3, 2, 6, 0]) torch.Size([4, 3, 32, 32]) tensor([9, 5, 0, 5]) torch.Size([4, 3, 32, 32]) tensor([5, 4, 2, 9]) torch.Size([4, 3, 32, 32]) tensor([9, 5, 9, 8]) torch.Size([4, 3, 32, 32]) tensor([3, 3, 8, 0]) torch.Size([4, 3, 32, 32]) tensor([2, 6, 1, 8]) torch.Size([4, 3, 32, 32]) tensor([1, 5, 6, 4]) torch.Size([4, 3, 32, 32]) tensor([9, 9, 6, 5]) torch.Size([4, 3, 32, 32]) tensor([7, 1, 2, 4]) torch.Size([4, 3, 32, 32]) tensor([1, 0, 8, 5]) torch.Size([4, 3, 32, 32]) tensor([2, 8, 1, 8]) torch.Size([4, 3, 32, 32]) tensor([7, 4, 6, 4]) torch.Size([4, 3, 32, 32]) tensor([5, 8, 5, 1]) torch.Size([4, 3, 32, 32]) tensor([8, 5, 9, 7]) torch.Size([4, 3, 32, 32]) tensor([2, 0, 9, 8]) torch.Size([4, 3, 32, 32]) tensor([2, 1, 4, 7]) torch.Size([4, 3, 32, 32]) tensor([9, 8, 1, 3]) torch.Size([4, 3, 32, 32]) tensor([4, 8, 1, 8]) torch.Size([4, 3, 32, 32]) tensor([5, 3, 8, 1]) torch.Size([4, 3, 32, 32]) tensor([2, 3, 7, 7]) torch.Size([4, 3, 32, 32]) tensor([6, 6, 8, 8]) torch.Size([4, 3, 32, 32]) tensor([4, 2, 7, 6]) torch.Size([4, 3, 32, 32]) tensor([2, 3, 1, 0]) torch.Size([4, 3, 32, 32]) tensor([0, 0, 5, 2]) torch.Size([4, 3, 32, 32]) tensor([0, 1, 1, 4]) torch.Size([4, 3, 32, 32]) tensor([1, 3, 2, 5]) torch.Size([4, 3, 32, 32]) tensor([2, 1, 1, 3]) torch.Size([4, 3, 32, 32]) tensor([5, 6, 1, 3]) torch.Size([4, 3, 32, 32]) tensor([8, 6, 2, 9]) torch.Size([4, 3, 32, 32]) tensor([5, 6, 4, 5]) torch.Size([4, 3, 32, 32]) tensor([9, 8, 5, 7]) torch.Size([4, 3, 32, 32]) tensor([9, 8, 5, 7]) torch.Size([4, 3, 32, 32]) tensor([6, 2, 8, 1]) torch.Size([4, 3, 32, 32]) tensor([6, 8, 7, 7]) torch.Size([4, 3, 32, 32]) tensor([1, 3, 6, 5]) torch.Size([4, 3, 32, 32]) tensor([6, 1, 8, 4]) torch.Size([4, 3, 32, 32]) tensor([1, 5, 7, 7]) torch.Size([4, 3, 32, 32]) tensor([5, 7, 1, 5]) torch.Size([4, 3, 32, 32]) tensor([5, 7, 7, 3]) torch.Size([4, 3, 32, 32]) tensor([1, 7, 4, 8]) torch.Size([4, 3, 32, 32]) tensor([2, 3, 0, 5]) torch.Size([4, 3, 32, 32]) tensor([6, 6, 7, 4]) torch.Size([4, 3, 32, 32]) tensor([2, 2, 7, 9]) torch.Size([4, 3, 32, 32]) tensor([8, 5, 8, 7]) torch.Size([4, 3, 32, 32]) tensor([5, 0, 8, 4]) torch.Size([4, 3, 32, 32]) tensor([4, 5, 9, 8]) torch.Size([4, 3, 32, 32]) tensor([6, 6, 7, 2]) torch.Size([4, 3, 32, 32]) tensor([6, 6, 1, 1]) torch.Size([4, 3, 32, 32]) tensor([2, 8, 3, 2]) torch.Size([4, 3, 32, 32]) tensor([7, 5, 5, 5]) torch.Size([4, 3, 32, 32]) tensor([1, 5, 5, 7]) torch.Size([4, 3, 32, 32]) tensor([0, 7, 5, 0]) torch.Size([4, 3, 32, 32]) tensor([9, 1, 4, 1]) torch.Size([4, 3, 32, 32]) tensor([8, 4, 6, 1]) torch.Size([4, 3, 32, 32]) tensor([3, 3, 0, 1]) torch.Size([4, 3, 32, 32]) tensor([7, 8, 6, 5]) torch.Size([4, 3, 32, 32]) tensor([3, 9, 6, 0]) torch.Size([4, 3, 32, 32]) tensor([9, 4, 5, 7]) torch.Size([4, 3, 32, 32]) tensor([0, 3, 4, 9]) torch.Size([4, 3, 32, 32]) tensor([1, 2, 8, 8]) torch.Size([4, 3, 32, 32]) tensor([7, 5, 0, 9]) torch.Size([4, 3, 32, 32]) tensor([3, 9, 3, 5]) torch.Size([4, 3, 32, 32]) tensor([3, 9, 3, 5]) torch.Size([4, 3, 32, 32]) tensor([0, 4, 3, 5]) torch.Size([4, 3, 32, 32]) tensor([6, 3, 5, 6]) torch.Size([4, 3, 32, 32]) tensor([2, 8, 8, 7]) torch.Size([4, 3, 32, 32]) tensor([6, 3, 4, 3]) torch.Size([4, 3, 32, 32]) tensor([7, 3, 2, 5]) torch.Size([4, 3, 32, 32]) tensor([1, 7, 5, 5]) torch.Size([4, 3, 32, 32]) tensor([3, 5, 2, 5]) torch.Size([4, 3, 32, 32]) tensor([3, 1, 5, 7]) torch.Size([4, 3, 32, 32]) tensor([0, 4, 7, 4]) torch.Size([4, 3, 32, 32]) tensor([6, 9, 9, 5]) torch.Size([4, 3, 32, 32]) tensor([8, 8, 6, 6]) torch.Size([4, 3, 32, 32]) tensor([0, 9, 3, 9]) torch.Size([4, 3, 32, 32]) tensor([4, 2, 2, 2]) torch.Size([4, 3, 32, 32]) tensor([5, 7, 6, 8]) torch.Size([4, 3, 32, 32]) tensor([8, 5, 2, 3]) torch.Size([4, 3, 32, 32]) tensor([1, 4, 3, 8]) torch.Size([4, 3, 32, 32]) tensor([8, 0, 3, 7]) torch.Size([4, 3, 32, 32]) tensor([2, 7, 1, 9]) torch.Size([4, 3, 32, 32]) tensor([1, 4, 4, 8]) torch.Size([4, 3, 32, 32]) tensor([0, 8, 6, 3]) torch.Size([4, 3, 32, 32]) tensor([6, 7, 2, 1]) torch.Size([
