请问我怎么在swin transformer中添加注意力机制

在Swin Transformer中添加注意力机制需要进行以下步骤：

1. 导入需要的模块

在Swin Transformer的代码中，需要导入以下模块：

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

2. 实现注意力机制

在Swin Transformer中，可以通过实现自定义的注意力机制来添加注意力机制。

class Attention(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=1, stride=1):
        super(Attention, self).__init__()
        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding=kernel_size // 2, groups=out_channels)
        self.norm = nn.BatchNorm2d(out_channels)
        self.act = nn.ReLU(inplace=True)
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.conv_atten = nn.Conv2d(out_channels, out_channels, kernel_size=1, stride=1)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        x = self.conv(x)
        x = self.norm(x)
        x = self.act(x)
        x = self.pool(x)
        x = self.conv_atten(x)
        x = self.sigmoid(x)
        return x

在这个自定义的注意力模块中，使用了卷积、BN、ReLU、全局平均池化、卷积、Sigmoid等操作，来实现对输入特征图的注意力加权。

3. 在Swin Transformer中使用注意力机制

在Swin Transformer中，可以在需要添加注意力机制的地方，将Attention模块加入到网络中。

例如，在Swin Transformer的基础块中，可以在第二个分支的卷积之前添加注意力模块：

class SwinTransformerBlock(nn.Module):
    def __init__(self, dim, input_resolution, num_heads, window_size=7, shift_size=0,
                 mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0.,
                 drop_path=0., act_layer=nn.ReLU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.input_resolution = input_resolution
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.qk_scale = qk_scale
        self.drop = drop
        self.attn_drop = attn_drop
        self.drop_path = drop_path
        self.norm1_name, norm2_name = norm_layer.__name__.split('.')[-1], norm_layer.__name__.split('.')[-1]
        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, window_size=window_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
        self.norm2 = norm_layer(dim)
        self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
        self.conv_atten = Attention(dim, dim//8)  # 添加注意力模块

    def forward(self, x, mask_matrix=None):
        H, W = self.input_resolution
        B, L, C = x.shape
        assert L == H * W, "input feature has wrong size"
        # norm before attn
        x = self.norm1(x)
        # calculate attention mask
        if mask_matrix is None:
            mask_matrix = torch.zeros((1, H, W, H, W), dtype=x.dtype, device=x.device)  # 生成全零的mask矩阵
        if self.window_size == H and self.shift_size == 0:
            # use global attention
            attn_mask = mask_matrix
        else:
            # calculate attention mask for SW-MSA
            attn_mask = self.calculate_mask(mask_matrix)
        # atention
        x = x.reshape(B, H, W, C).permute(0, 3, 1, 2)
        # 添加注意力模块
        x = x * self.conv_atten(x)
        x = x.permute(0, 2, 3, 1).reshape(B, H * W, C)
        x, attn = self.attn(x, attn_mask)
        # drop path
        if self.drop_path > 0.:
            x = drop_path(x, self.drop_path, self.training)
        # reesidual connection
        x = x + self.drop_path(self.mlp(self.norm2(x)), self.drop_path, self.training)
        return x, attn, mask_matrix

在这个Swin Transformer基础块的第二个分支的卷积之前，加入了Attention模块，并用该模块对输入特征图进行了注意力加权。