self attention pytorch 在图像中运用的代码

Self-Attention 机制最初是在自然语言处理领域中使用的，但是它也可以被应用在计算机视觉中，例如图像分类、图像分割、目标检测等任务中。

在 PyTorch 中，可以使用 `torch.nn.MultiheadAttention` 模块来实现 Self-Attention 机制。下面是一个使用 Self-Attention 机制进行图像分类的示例代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class SelfAttention(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(SelfAttention, self).__init__()
        self.out_channels = out_channels
        
        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)
        
        self.gamma = nn.Parameter(torch.zeros(1))
        
    def forward(self, x):
        batch_size, channels, height, width = x.size()
        
        query = self.query_conv(x).view(batch_size, self.out_channels, -1)
        key = self.key_conv(x).view(batch_size, self.out_channels, -1)
        value = self.value_conv(x).view(batch_size, self.out_channels, -1)
        
        attention_weights = F.softmax(torch.bmm(query.transpose(1, 2), key), dim=2)
        attention_output = torch.bmm(value, attention_weights.transpose(1, 2))
        attention_output = attention_output.view(batch_size, self.out_channels, height, width)
        
        out = self.gamma * attention_output + x
        return out

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.sa1 = SelfAttention(64, 64)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.sa2 = SelfAttention(128, 128)
        self.conv3 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.sa3 = SelfAttention(256, 256)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(256 * 4 * 4, 1024)
        self.fc2 = nn.Linear(1024, 10)
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.sa1(x)
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.sa2(x)
        x = self.pool(x)
        x = F.relu(self.conv3(x))
        x = self.sa3(x)
        x = self.pool(x)
        x = x.view(-1, 256 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

这个示例代码定义了一个包含 Self-Attention 机制的卷积神经网络（CNN）用于图像分类。`SelfAttention` 类表示 Self-Attention 模块，它接受输入张量 `x`，并返回经过 Self-Attention 机制处理后的输出张量。`Net` 类表示整个 CNN，它包含了三个卷积层和三个 Self-Attention 模块。在每个卷积层之后都使用了一个池化层，并且在最后的全连接层之前使用了一个展平层。在 `forward` 方法中，首先对输入进行卷积操作，然后使用 Self-Attention 模块进行特征提取，接着进行池化操作，最后进行全连接操作得到分类结果。