门控注意力机制(Gated Attention)

门控注意力机制引入了门控单元,通过门控信号来控制注意力的分配。门控单元可以根据输入的特征动态地调整注意力权重,使得模型能够更加灵活地关注不同的特征或区域。常见的门控单元有 Sigmoid 门控和 Tanh 门控等。

import torch
import torch.nn as nn

class GatedAttention(nn.Module):
    def __init__(self, in_channels):
        super(GatedAttention, self).__init__()
        self.gate = nn.Conv2d(in_channels, 1, kernel_size=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        gate = self.gate(x)
        gate = self.sigmoid(gate)
        out = x * gate
        return out

你可以使用以下代码实现门控轴向自注意力机制Gated Axial Self-Attention): ```python import torch import torch.nn as nn class GatedAxialSelfAttention(nn.Module): def __init__(self, dim, max_length): super(GatedAxialSelfAttention, self).__init__() self.dim = dim self.max_length = max_length # 定义参数矩阵 self.W_q = nn.Linear(dim, dim, bias=False) self.W_k = nn.Linear(dim, dim, bias=False) self.W_v = nn.Linear(dim, dim, bias=False) self.W_g = nn.Linear(dim, dim, bias=False) self.W_o = nn.Linear(dim, dim, bias=False) def forward(self, x): batch_size, seq_length, _ = x.size() q = self.W_q(x) # (batch_size, seq_length, dim) k = self.W_k(x) # (batch_size, seq_length, dim) v = self.W_v(x) # (batch_size, seq_length, dim) # 水平方向的自注意力 attention_h = torch.matmul(q.transpose(1, 2), k.transpose(1, 2).contiguous()) # (batch_size, dim, seq_length) attention_h = attention_h.softmax(dim=-1) # 竖直方向的自注意力 attention_v = torch.matmul(k.transpose(1, 2).contiguous(), q.transpose(1, 2)) # (batch_size, seq_length, seq_length) attention_v = attention_v.softmax(dim=-1) # 门控机制 g_h = self.W_g(attention_h.transpose(1, 2).contiguous()) # (batch_size, seq_length, dim) g_v = self.W_g(attention_v) # (batch_size, seq_length, dim) # 融合水平和竖直的注意力 combined_attention = attention_h.transpose(1, 2).contiguous() * g_h + attention_v * g_v # 输出 output = torch.matmul(combined_attention, v.transpose(1, 2)) # (batch_size, seq_length, dim) output = self.W_o(output) return output # 使用示例 dim = 256 max_length = 100 batch_size = 32 seq_length = 50 input_data = torch.randn(batch_size, seq_length, dim) attention = GatedAxialSelfAttention(dim, max_length) output = attention(input_data) print(output.size()) ``` 这个代码实现了一个门控轴向自注意力机制的模块,输入是一个三维张量 `(batch_size, seq_length, dim)`,其中 `batch_size` 是批量大小,`seq_length` 是序列长度,`dim` 是特征维度。模块将输入通过门控轴向自注意力机制进行处理,并返回输出 `(batch_size, seq_length, dim)`。
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