用pytorch写一个带注意力机制的CNN卷积网络的代码

好的，以下是一个简单的带注意力机制的卷积神经网络的 PyTorch 代码示例：

import torch
import torch.nn as nn

class Attention(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Attention, self).__init__()

        self.query_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)

    def forward(self, x):
        # x shape: (batch_size, in_channels, h, w)

        # project input tensor to query, key, and value tensor
        query = self.query_conv(x)  # (batch_size, out_channels, h, w)
        key = self.key_conv(x)  # (batch_size, out_channels, h, w)
        value = self.value_conv(x)  # (batch_size, out_channels, h, w)

        # calculate attention weights
        key = key.permute(0, 2, 3, 1).contiguous()  # (batch_size, h, w, out_channels)
        query = query.permute(0, 2, 3, 1).contiguous()  # (batch_size, h, w, out_channels)
        attn_weights = torch.matmul(query, key.transpose(2, 3))
        attn_weights = nn.functional.softmax(attn_weights, dim=-1)

        # apply attention weights to value tensor
        value = value.permute(0, 2, 3, 1).contiguous()  # (batch_size, h, w, out_channels)
        attn_values = torch.matmul(attn_weights, value)
        attn_values = attn_values.permute(0, 3, 1, 2).contiguous()  # (batch_size, out_channels, h, w)

        return attn_values


class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()

        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, 3, padding=1)

        self.attention1 = Attention(32, 32)
        self.attention2 = Attention(64, 64)
        self.attention3 = Attention(128, 128)

        self.fc = nn.Linear(128 * 4 * 4, 10)

    def forward(self, x):
        x = nn.functional.relu(self.conv1(x))
        x = self.attention1(x) + x
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv2(x), 2))
        x = self.attention2(x) + x
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv3(x), 2))
        x = self.attention3(x) + x
        x = x.view(-1, 128 * 4 * 4)
        x = self.fc(x)
        return x

这个网络包含了三个卷积层和三个注意力机制，注意力机制被用于加强中间层的特征，以提高模型的准确性。 要注意的是，在实际使用中，这个简单的示例可能需要细微的调整来适应特定的问题。