FCA注意力机制代码

FCA（Feature-wise Linear Modulation with Composite Attention）注意力机制是一种用于计算机视觉任务的注意力模型。下面是一个示例代码，展示了如何实现FCA注意力机制：

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

class FCAAttention(nn.Module):
    def __init__(self, input_dim, hidden_dim):
        super(FCAAttention, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, 1)
    
    def forward(self, input):
        x = F.relu(self.fc1(input))
        attention_weights = F.softmax(self.fc2(x), dim=1)
        attended_input = torch.mul(attention_weights, input)
        output = torch.sum(attended_input, dim=1)
        return output

# 示例用法
input_dim = 512  # 输入维度
hidden_dim = 256  # 隐藏层维度

attention = FCAAttention(input_dim, hidden_dim)
input = torch.randn(32, input_dim)  # 输入数据，32表示batch size
output = attention(input)  # FCA注意力机制的输出

print(output.shape)  # 输出形状