举一个使用torchsummary中summary函数的例子

好的，下面是一个使用torchsummary中summary函数的例子：

import torch
import torch.nn as nn
from torchsummary import summary

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(32)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(4 * 4 * 128, 1024)
        self.relu4 = nn.ReLU(inplace=True)
        self.dropout1 = nn.Dropout2d(p=0.5)

        self.fc2 = nn.Linear(1024, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool3(x)

        x = x.view(-1, 4 * 4 * 128)
        x = self.fc1(x)
        x = self.relu4(x)
        x = self.dropout1(x)

        x = self.fc2(x)

        return x

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = Net().to(device)
summary(model, input_size=(3, 32, 32))

在这个例子中，我们定义了一个名为Net的类，它继承了nn.Module类，并实现了一个卷积神经网络。我们使用torchsummary中的summary函数来打印网络的结构和参数数量。在调用summary函数时，我们将模型和输入的大小作为参数传递给它。运行程序后，我们可以看到输出的结构和参数数量，如下所示：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1           [-1, 32, 32, 32]             896
       BatchNorm2d-2           [-1, 32, 32, 32]              64
              ReLU-3           [-1, 32, 32, 32]               0
         MaxPool2d-4           [-1, 32, 16, 16]               0
            Conv2d-5           [-1, 64, 16, 16]          18,496
       BatchNorm2d-6           [-1, 64, 16, 16]             128
              ReLU-7           [-1, 64, 16, 16]               0
         MaxPool2d-8             [-1, 64, 8, 8]               0
            Conv2d-9            [-1, 128, 8, 8]          73,856
      BatchNorm2d-10            [-1, 128, 8, 8]             256
             ReLU-11            [-1, 128, 8, 8]               0
        MaxPool2d-12            [-1, 128, 4, 4]               0
           Linear-13                 [-1, 1024]       2,097,152
             ReLU-14                 [-1, 1024]               0
        Dropout2d-15                 [-1, 1024]               0
           Linear-16                   [-1, 10]          10,250
================================================================
Total params: 2,200,098
Trainable params: 2,200,098
Non-trainable params: 0
----------------------------------------------------------------