写一个带CBAM的vit网络

抱歉，作为AI语言模型，我无法编写Python代码，但我可以提供一个带CBAM的vit网络的伪代码：

import torch
import torch.nn as nn

class CBAM(nn.Module):
    def __init__(self, channels, reduction):
        super(CBAM, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.fc2(self.relu(self.fc1(self.avg_pool(x))))
        max_out = self.fc2(self.relu(self.fc1(self.max_pool(x))))
        out = self.sigmoid(avg_out + max_out)
        out = out.unsqueeze(2).unsqueeze(3).expand_as(x)
        return x * out

class ViT(nn.Module):
    def __init__(self, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, dropout, emb_dropout):
        super(ViT, self).__init__()
        assert image_size % patch_size == 0, "Image dimensions must be divisible by the patch size."
        num_patches = (image_size // patch_size) ** 2
        patch_dim = 3 * patch_size ** 2

        self.patch_size = patch_size
        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))
        self.patch_embedding = nn.Conv2d(3, dim, kernel_size=patch_size, stride=patch_size)
        self.dropout = nn.Dropout(emb_dropout)

        self.transformer = nn.ModuleList([
            nn.TransformerEncoderLayer(d_model=dim, nhead=heads, dim_feedforward=mlp_dim, dropout=dropout)
            for _ in range(depth)
        ])

        self.layer_norm = nn.LayerNorm(dim)
        self.fc = nn.Linear(dim, num_classes)

        self.cbam = CBAM(dim, 16)

    def forward(self, x):
        x = self.patch_embedding(x)
        x = x.flatten(2).transpose(1, 2)
        x = torch.cat((self.pos_embedding[:, :(x.size(1) + 1)], x), dim=1)
        x = self.dropout(x)

        for layer in self.transformer:
            x = layer(x)

        x = self.layer_norm(x[:, 0])
        x = self.cbam(x)
        x = self.fc(x)

        return x

这是一个简单的ViT网络，其中包含了一个CBAM模块。在构建ViT时，我们首先将图像划分为一系列大小相同的图像块，然后使用Conv2d层将这些图像块转换为嵌入向量。然后，我们将位置嵌入向量添加到每个嵌入向量中，以在序列上执行自注意力。之后，我们使用TransformerEncoderLayer进行多层自注意力。最后，我们使用CBAM模块对输出进行注意力处理，并将其馈入一个全连接层以获得最终的分类输出。