DataLoader()使用_train_dataloader = dataloader(dataset=train_data,

1. dataLoader的作用就是把图片按照一定的batchsize作为一个部分
2. 四张图片，3通道，32*32，tensor就是她的target
3. 一组imgs,targets 组合成了代码中的 for data in train_loader中的一个data

import torchvision
from tensorboard.compat.proto.summary_pb2 import Summary
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
 
dataset_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
train_data = torchvision.datasets.CIFAR10(root='./dataset',train=True,transform=dataset_transform,download=True)
test_data = torchvision.datasets.CIFAR10(root='./dataset',train=False,transform=dataset_transform,download=True)
 
train_loader = DataLoader(train_data,batch_size=64,shuffle=True,num_workers=0,drop_last=False)
# 训练集的第一张图片
image ,target = train_data[0]
writer = SummaryWriter('logs')
for epoch in range(2):
    step = 0
    for data in train_loader:
        images ,targets = data
        # add_images才能在一个step放多张图片
        writer.add_images('epoch:{}'.format(epoch),images,global_step=step)
        step =step+1
        # print(images.shape)
        # print(targets)
writer.close()