python
import torchvision
from torch.utils.data import DataLoader
from torch.utils.tensorboard import SummaryWriter
# 准备的测试数据集 数据放在了CIFAR10文件夹下
test_data = torchvision.datasets.CIFAR10("./CIFAR10",
train=False, transform=torchvision.transforms.ToTensor())
test_loader = DataLoader(dataset=test_data, batch_size=4,
shuffle=True, num_workers=0, drop_last=False)
# 测试数据集中第一张图片及target
img, target = test_data[0]
print(img.shape)
print(target)
# 在定义test_loader时,设置了batch_size=4,表示一次性从数据集中取出4个数据
for data in test_loader:
imgs, targets = data
print(imgs.shape)
print(targets)
# 在定义test_loader时,设置了batch_size=4,表示一次性从数据集中取出4个数据
writer = SummaryWriter("logs")
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()
把CIFAR10做成一个数据集,然后得到迭代器
每个迭代器包括图像和标签
下面是tensorboard的用法