① Dataset只是去告诉我们程序，我们的数据集在什么位置，数据集第一个数据给它一个索引0，它对应的是哪一个数据。

② Dataloader就是把数据加载到神经网络当中，Dataloader所做的事就是每次从Dataset中取数据，至于怎么取，是由Dataloader中的参数决定的。

import torchvision
from torch.utils.data import DataLoader# 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) #torch.Size([3, 32, 32])
print(img)'''
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]]])
'''# 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
for data in test_loader:# 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imgs, targets = data              print(imgs.shape)print(targets)

2. Tensorboard展示 

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter# 准备的测试数据集
test_data = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor())               
# 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=64,shuffle=True,num_workers=0,drop_last=False)      
# 用for循环取出DataLoader打包好的四个数据
writer = SummaryWriter("logs")
step = 0
for data in test_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             writer.add_images("test_data",imgs,step)step = step + 1writer.close()

① 在 Anaconda 终端里面，激活py3.6.3环境，再输入 tensorboard --logdir=C:\Users\wangy\Desktop\03CV\logs 命令，将网址赋值浏览器的网址栏，回车，即可查看tensorboard显示日志情况。 

 3. Dataloader多轮次

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter# 准备的测试数据集
test_data = torchvision.datasets.CIFAR10("./dataset",train=False,transform=torchvision.transforms.ToTensor())               
# 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=64,shuffle=True,num_workers=0,drop_last=True)      
# 用for循环取出DataLoader打包好的四个数据
writer = SummaryWriter("logs")
for epoch in range(2):step = 0for data in test_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             writer.add_images("Epoch：{}".format(epoch),imgs,step)step = step + 1writer.close()