生成一个不同图片交叉注意力pytorch代码

以下是一个生成不同图片交叉注意力的 PyTorch 代码示例：

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

class DifferentImageCrossAttention(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(DifferentImageCrossAttention, self).__init__()
        self.conv_query = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv_key = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        self.conv_value = nn.Conv2d(in_channels, out_channels, kernel_size=1)
        
    def forward(self, x1, x2):
        query = self.conv_query(x1)
        key = self.conv_key(x2)
        value = self.conv_value(x2)
        
        query = query.view(query.size(0), -1, query.size(2) * query.size(3))
        query = query.permute(0, 2, 1)
        key = key.view(key.size(0), -1, key.size(2) * key.size(3))
        attention = torch.bmm(query, key)
        attention = F.softmax(attention, dim=-1)
        
        value = value.view(value.size(0), -1, value.size(2) * value.size(3))
        attention_value = torch.bmm(value, attention)
        attention_value = attention_value.view(value.size(0), -1, x1.size(2), x1.size(3))
        
        return attention_value

这是一个基本的不同图片交叉注意力模块，输入两个图片 x1 和 x2，然后通过三个卷积层将它们映射到同一维度，接着计算注意力矩阵并将其应用于 x2 的 value，最终得到输出 attention_value。这个模块的实现可能需要根据具体应用做出一些修改和调整。