from torch.utils.tensorboard import SummaryWriter
import torch.nn as nn
import torch
dataset_transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor()
])
test_data = torchvision.datasets.CIFAR10(root="./test10_dataset", train=False, transform=dataset_transform)
test_loader = DataLoader(dataset=test_data, batch_size=64)
class MyNet(nn.Module):
def __init__(self) -> None:
super(MyNet, self).__init__()
self.conv1 = Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)
def forward(self, x):
x = self.conv1(x)
return x
MyNet = MyNet()
writer = SummaryWriter("CIFAR10")
step = 0
for data in test_loader:
imgs, target = data
output = MyNet(imgs)
writer.add_images("input", imgs, step)
output = torch.reshape(output, (-1, 3, 30, 30))
writer.add_images("output", output, step)
step = step + 1
writer.close()
卷积层
Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=0)
输入为3,输出为6,卷积核大小为3,步长为1
在terminal中使用:
tensorboard --logdir=CIFAR10
tensorboard :
输入:
输出: