用pytorch构建一个可以可视化模型结构的代码

可以使用`torchsummary`库来构建一个可以可视化模型结构的代码。以下是一个简单的示例：

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

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.relu = nn.ReLU()
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.fc1 = nn.Linear(32 * 8 * 8, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = self.relu(x)
        x = self.pool(x)
        x = self.conv2(x)
        x = self.relu(x)
        x = self.pool(x)
        x = x.view(-1, 32 * 8 * 8)
        x = self.fc1(x)
        return x

model = MyModel()
summary(model, (3, 32, 32))

在这个例子中，我们首先定义了一个包含卷积层、池化层和全连接层的简单的神经网络模型`MyModel`。然后，我们使用`torchsummary`库中的`summary`函数来打印模型的结构信息，包括每一层的名称、输出形状、参数数量等等。在本例中，我们打印的是模型对输入尺寸为`(3, 32, 32)`的数据的响应。这段代码将输出以下信息：

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1        [-1, 16, 32, 32]             448
              ReLU-2        [-1, 16, 32, 32]               0
         MaxPool2d-3        [-1, 16, 16, 16]               0
            Conv2d-4        [-1, 32, 16, 16]           4,640
              ReLU-5        [-1, 32, 16, 16]               0
         MaxPool2d-6          [-1, 32, 8, 8]               0
            Linear-7                 [-1, 10]         20,490
================================================================
Total params: 25,578
Trainable params: 25,578
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.01
Forward/backward pass size (MB): 0.31
Params size (MB): 0.10
Estimated Total Size (MB): 0.42
----------------------------------------------------------------