dataloader的使用

dataset是准备数据集

dataloader是将准备好的数据集按照一定方式读取出来，输入到神经网络中。

import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
trans_dataset = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor()
])
#dataset准备好数据集，dataloader将数据集按一定方式读取
test_data = torchvision.datasets.CIFAR10(root='./dataset',train=True,transform=trans_dataset,download=True)
test_loader = DataLoader(dataset=test_data,batch_size=64,shuffle=True,drop_last=False)
# img,target = test_data[0]
# print(img)
# print(img.shape)
writer = SummaryWriter('dataloader')
#每个epoch中dataloader读取一次，训练两个epoch
for epoch in range(2):
    step = 0
    for data in test_loader:
        imgs,targets = data
        writer.add_images('epoch:{}'.format(epoch),imgs,step)
        step = step+1
writer.close()
print('over')