计算机视觉注意力机制【一】常用注意力机制整理

在做目标检测项目，尤其是基于 YOLOv5 或 YOLOv7 的改进实验时，我发现不同注意力机制对模型性能的提升确实有明显影响，比如提高小目标检测能力、增强特征表达等。但每次找代码都得翻论文、找 GitHub，效率很低。所以我干脆把常见的注意力模块（比如 SE、CBAM、ShuffleAttention、SimAM 等）都整理到一起，统一了格式和接口，方便自己后续做结构替换和对比实验。这个整理也能帮助我更系统地理解各类注意力机制的原理和实现方式，也希望能为有类似需求的人提供一些参考。

后续会基于注意机制与 YOLO 目标检测进行融合，欢迎各位关注➕收藏

🔹 1. SEAttention（Squeeze-and-Excitation Attention）

来源：
https://arxiv.org/abs/1709.01507
机制：
全局平均池化 → 两层 MLP → Sigmoid → 通道权重调整

class SEAttention(nn.Module):

    def __init__(self, channel=512,reduction=16):
        super().__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )


    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y.expand_as(x)

🔹 2. ShuffleAttention

来源：
https://arxiv.org/pdf/2102.00240.pdf
机制：
通道注意力 + 空间注意力，利用 GroupNorm 和 Shuffle 操作

class ShuffleAttention(nn.Module):

    def __init__(self, channel=512,reduction=16,G=8):
        super().__init__()
        self.G=G
        self.channel=channel
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.gn = nn.GroupNorm(channel // (2 * G), channel // (2 * G))
        self.cweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
        self.cbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
        self.sweight = Parameter(torch.zeros(1, channel // (2 * G), 1, 1))
        self.sbias = Parameter(torch.ones(1, channel // (2 * G), 1, 1))
        self.sigmoid=nn.Sigmoid()


    def init_weights(self):
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                init.kaiming_normal_(m.weight, mode='fan_out')
                if m.bias is not None:
                    init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                init.constant_(m.weight, 1)
                init.constant_(m.bias, 0)
            elif isinstance(m, nn.Linear):
                init.normal_(m.weight, std=0.001)
                if m.bias is not None:
                    init.constant_(m.bias, 0)


    @staticmethod
    def channel_shuffle(x, groups):
        b, c, h, w = x.shape
        x = x.reshape(b, groups, -1, h, w)
        x = x.permute(0, 2, 1, 3, 4)

        # flatten
        x = x.reshape(b, -1, h, w)

        return x

    def forward(self, x):
        b, c, h, w = x.size()
        #group into subfeatures
        x=x.view(b*self.G,-1,h,w) #bs*G,c//G,h,w

        #channel_split
        x_0,x_1=x.chunk(2,dim=1) #bs*G,c//(2*G),h,w

        #channel attention
        x_channel=self.avg_pool(x_0) #bs*G,c//(2*G),1,1
        x_channel=self.cweight*x_channel+self.cbias #bs*G,c//(2*G),1,1
        x_channel=x_0*self.sigmoid(x_channel)

        #spatial attention
        x_spatial=self.gn(x_1) #bs*G,c//(2*G),h,w
        x_spatial=self.sweight*x_spatial+self.sbias #bs*G,c//(2*G),h,w
        x_spatial=x_1*self.sigmoid(x_spatial) #bs*G,c//(2*G),h,w

        # concatenate along channel axis
        out=torch.cat([x_channel,x_spatial],dim=1)  #bs*G,c//G,h,w
        out=out.contiguous().view(b,-1,h,w)

        # channel shuffle
        out = self.channel_shuffle(out, 2)
        return out

🔹 3. CrissCrossAttention（CCA）

来源： CCNet-Pure-Pytorch
机制： 分别在 H 和 W 方向做注意力交叉计算，融合上下文

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


def INF(B,H,W):
     return -torch.diag(torch.tensor(float("inf")).repeat(H),0).unsqueeze(0).repeat(B*W,1,1)


class CrissCrossAttention(nn.Module):
    """ Criss-Cross Attention Module"""
    def __init__(self, in_dim):
        super(CrissCrossAttention,self).__init__()
        self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1)
        self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1)
        self.softmax = Softmax(dim=3)
        self.INF = INF
        self.gamma = nn.Parameter(torch.zeros(1))


    def forward(self, x):
        m_batchsize, _, height, width = x.size()
        proj_query = self.query_conv(x)
        proj_query_H = proj_query.permute(0,3,1,2).contiguous().view(m_batchsize*width,-1,height).permute(0, 2, 1)
        proj_query_W = proj_query.permute(0,2,1,3).contiguous().view(m_batchsize*height,-1,width).permute(0, 2, 1)
        proj_key = self.key_conv(x)
        proj_key_H = proj_key.permute(0,3,1,2).contiguous().view(m_batchsize*width,-1,height)
        proj_key_W = proj_key.permute(0,2,1,3).contiguous().view(m_batchsize*height,-1,width)
        proj_value = self.value_conv(x)
        proj_value_H = proj_value.permute(0,3,1,2).contiguous().view(m_batchsize*width,-1,height)
        proj_value_W = proj_value.permute(0,2,1,3).contiguous().view(m_batchsize*height,-1,width)
        energy_H = (torch.bmm(proj_query_H, proj_key_H)+self.INF(m_batchsize, height, width)).view(m_batchsize,width,height,height).permute(0,2,1,3)
        energy_W = torch.bmm(proj_query_W, proj_key_W).view(m_batchsize,height,width,width)
        concate = self.softmax(torch.cat([energy_H, energy_W], 3))

        att_H = concate[:,:,:,0:height].permute(0,2,1,3).contiguous().view(m_batchsize*width,height,height)
        #print(concate)
        #print(att_H) 
        att_W = concate[:,:,:,height:height+width].contiguous().view(m_batchsize*height,width,width)
        out_H = torch.bmm(proj_value_H, att_H.permute(0, 2, 1)).view(m_batchsize,width,-1,height).permute(0,2,3,1)
        out_W = torch.bmm(proj_value_W, att_W.permute(0, 2, 1)).view(m_batchsize,height,-1,width).permute(0,2,1,3)
        #print(out_H.size(),out_W.size())
        return self.gamma*(out_H + out_W) + x

🔹 4. S2-MLPv2 Attention

来源：
https://arxiv.org/abs/2108.01072
机制：
Spatial Shift + MLP + 分支融合

def spatial_shift1(x):
    b,w,h,c = x.size()
    x[:,1:,:,:c//4] = x[:,:w-1,:,:c//4]
    x[:,:w-1,:,c//4:c//2] = x[:,1:,:,c//4:c//2]
    x[:,:,1:,c//2:c*3//4] = x[:,:,:h-1,c//2:c*3//4]
    x[:,:,:h-1,3*c//4:] = x[:,:,1:,3*c//4:]
    return x


def spatial_shift2(x):
    b,w,h,c = x.size()
    x[:,:,1:,:c//4] = x[:,:,:h-1,:c//4]
    x[:,:,:h-1,c//4:c//2] = x[:,:,1:,c//4:c//2]
    x[:,1:,:,c//2:c*3//4] = x[:,:w-1,:,c//2:c*3//4]
    x[:,:w-1,:,3*c//4:] = x[:,1:,:,3*c//4:]
    return x


class SplitAttention(nn.Module):
    def __init__(self,channel=512,k=3):
        super().__init__()
        self.channel=channel
        self.k=k
        self.mlp1=nn.Linear(channel,channel,bias=False)
        self.gelu=nn.GELU()
        self.mlp2=nn.Linear(channel,channel*k,bias=False)
        self.softmax=nn.Softmax(1)
    
    def forward(self,x_all):
        b,k,h,w,c=x_all.shape
        x_all=x_all.reshape(b,k,-1,c) 
        a=torch.sum(torch.sum(x_all,1),1) 
        hat_a=self.mlp2(self.gelu(self.mlp1(a))) 
        hat_a=hat_a.reshape(b,self.k,c) 
        bar_a=self.softmax(hat_a) 
        attention=bar_a.unsqueeze(-2) 
        out=attention*x_all 
        out=torch.sum(out,1).reshape(b,h,w,c)
        return out


class S2Attention(nn.Module):

    def __init__(self, channels=512 ):
        super().__init__()
        self.mlp1 = nn.Linear(channels,channels*3)
        self.mlp2 = nn.Linear(channels,channels)
        self.split_attention = SplitAttention()

    def forward(self, x):
        b,c,w,h = x.size()
        x=x.permute(0,2,3,1)
        x = self.mlp1(x)
        x1 = spatial_shift1(x[:,:,:,:c])
        x2 = spatial_shift2(x[:,:,:,c:c*2])
        x3 = x[:,:,:,c*2:]
        x_all=torch.stack([x1,x2,x3],1)
        a = self.split_attention(x_all)
        x = self.mlp2(a)
        x=x.permute(0,3,1,2)
        return x
  

🔹 5. SimAM

机制： 使用方差引导通道激活，无需参数

import torch
import torch.nn as nn


class SimAM(torch.nn.Module):
    def __init__(self, channels = None,out_channels = None, e_lambda = 1e-4):
        super(SimAM, self).__init__()

        self.activaton = nn.Sigmoid()
        self.e_lambda = e_lambda

    def __repr__(self):
        s = self.__class__.__name__ + '('
        s += ('lambda=%f)' % self.e_lambda)
        return s

    @staticmethod
    def get_module_name():
        return "simam"

    def forward(self, x):

        b, c, h, w = x.size()
        
        n = w * h - 1

        x_minus_mu_square = (x - x.mean(dim=[2,3], keepdim=True)).pow(2)
        y = x_minus_mu_square / (4 * (x_minus_mu_square.sum(dim=[2,3], keepdim=True) / n + self.e_lambda)) + 0.5 #atbriass

        return x * self.activaton(y)  

🔹 6. SKAttention（Selective Kernel）

机制： 多尺度卷积 + Soft attention 选择合适卷积核

class SKAttention(nn.Module):

    def __init__(self, channel=512,kernels=[1,3,5,7],reduction=16,group=1,L=32):
        super().__init__()
        self.d=max(L,channel//reduction)
        self.convs=nn.ModuleList([])
        for k in kernels:
            self.convs.append(
                nn.Sequential(OrderedDict([
                    ('conv',nn.Conv2d(channel,channel,kernel_size=k,padding=k//2,groups=group)),
                    ('bn',nn.BatchNorm2d(channel)),
                    ('relu',nn.ReLU())
                ]))
            )
        self.fc=nn.Linear(channel,self.d)
        self.fcs=nn.ModuleList([])
        for i in range(len(kernels)):
            self.fcs.append(nn.Linear(self.d,channel))
        self.softmax=nn.Softmax(dim=0)



    def forward(self, x):
        bs, c, _, _ = x.size()
        conv_outs=[]
        ### split atbriass
        for conv in self.convs:
            conv_outs.append(conv(x))
        feats=torch.stack(conv_outs,0)#k,bs,channel,h,w

        ### fuse
        U=sum(conv_outs) #bs,c,h,w

        ### reduction channel
        S=U.mean(-1).mean(-1) #bs,c
        Z=self.fc(S) #bs,d

        ### calculate attention weight
        weights=[]
        for fc in self.fcs:
            weight=fc(Z)
            weights.append(weight.view(bs,c,1,1)) #bs,channel
        attention_weughts=torch.stack(weights,0)#k,bs,channel,1,1
        attention_weughts=self.softmax(attention_weughts)#k,bs,channel,1,1

        ### fuse
        V=(attention_weughts*feats).sum(0)
        return V

🔹 7. NAMAttention（Normalization-based Attention）

机制： 使用 BN 参数的归一化特性引导注意力

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


class Channel_Att(nn.Module):
    def __init__(self, channels, t=16):
        super(Channel_Att, self).__init__()
        self.channels = channels
      
        self.bn2 = nn.BatchNorm2d(self.channels, affine=True)


    def forward(self, x):
        residual = x

        x = self.bn2(x)
        weight_bn = self.bn2.weight.data.abs() / torch.sum(self.bn2.weight.data.abs())
        x = x.permute(0, 2, 3, 1).contiguous()
        x = torch.mul(weight_bn, x)
        x = x.permute(0, 3, 1, 2).contiguous()
        
        x = torch.sigmoid(x) * residual #
        
        return x


class NAMAttention(nn.Module):
    def __init__(self, channels, out_channels=None, no_spatial=True):
        super(NAMAttention, self).__init__()
        self.Channel_Att = Channel_Att(channels)
  
    def forward(self, x):
        x_out1=self.Channel_Att(x)
 
        return x_out1  

🔹 8. SOCA（Second-order Channel Attention）

机制： 基于协方差池化和矩阵平方根的高阶通道注意力机制

import numpy as np
import torch
from torch import nn
from torch.nn import init

from torch.autograd import Function

class Covpool(Function):
     @staticmethod
     def forward(ctx, input):
         x = input
         batchSize = x.data.shape[0]
         dim = x.data.shape[1]
         h = x.data.shape[2]
         w = x.data.shape[3]
         M = h*w
         x = x.reshape(batchSize,dim,M)
         I_hat = (-1./M/M)*torch.ones(M,M,device = x.device) + (1./M)*torch.eye(M,M,device = x.device)
         I_hat = I_hat.view(1,M,M).repeat(batchSize,1,1).type(x.dtype)
         y = x.bmm(I_hat).bmm(x.transpose(1,2))
         ctx.save_for_backward(input,I_hat)
         return y
     @staticmethod
     def backward(ctx, grad_output):
         input,I_hat = ctx.saved_tensors
         x = input
         batchSize = x.data.shape[0]
         dim = x.data.shape[1]
         h = x.data.shape[2]
         w = x.data.shape[3]
         M = h*w
         x = x.reshape(batchSize,dim,M)
         grad_input = grad_output + grad_output.transpose(1,2)
         grad_input = grad_input.bmm(x).bmm(I_hat)
         grad_input = grad_input.reshape(batchSize,dim,h,w)
         return grad_input

class Sqrtm(Function):
     @staticmethod
     def forward(ctx, input, iterN):
         x = input
         batchSize = x.data.shape[0]
         dim = x.data.shape[1]
         dtype = x.dtype
         I3 = 3.0*torch.eye(dim,dim,device = x.device).view(1, dim, dim).repeat(batchSize,1,1).type(dtype)
         normA = (1.0/3.0)*x.mul(I3).sum(dim=1).sum(dim=1)
         A = x.div(normA.view(batchSize,1,1).expand_as(x))
         Y = torch.zeros(batchSize, iterN, dim, dim, requires_grad = False, device = x.device)
         Z = torch.eye(dim,dim,device = x.device).view(1,dim,dim).repeat(batchSize,iterN,1,1)
         if iterN < 2:
            ZY = 0.5*(I3 - A)
            Y[:,0,:,:] = A.bmm(ZY)
         else:
            ZY = 0.5*(I3 - A)
            Y[:,0,:,:] = A.bmm(ZY)
            Z[:,0,:,:] = ZY
            for i in range(1, iterN-1):
               ZY = 0.5*(I3 - Z[:,i-1,:,:].bmm(Y[:,i-1,:,:]))
               Y[:,i,:,:] = Y[:,i-1,:,:].bmm(ZY)
               Z[:,i,:,:] = ZY.bmm(Z[:,i-1,:,:])
            ZY = 0.5*Y[:,iterN-2,:,:].bmm(I3 - Z[:,iterN-2,:,:].bmm(Y[:,iterN-2,:,:]))
         y = ZY*torch.sqrt(normA).view(batchSize, 1, 1).expand_as(x)
         ctx.save_for_backward(input, A, ZY, normA, Y, Z)
         ctx.iterN = iterN
         return y
     @staticmethod
     def backward(ctx, grad_output):
         input, A, ZY, normA, Y, Z = ctx.saved_tensors
         iterN = ctx.iterN
         x = input
         batchSize = x.data.shape[0]
         dim = x.data.shape[1]
         dtype = x.dtype
         der_postCom = grad_output*torch.sqrt(normA).view(batchSize, 1, 1).expand_as(x)
         der_postComAux = (grad_output*ZY).sum(dim=1).sum(dim=1).div(2*torch.sqrt(normA))
         I3 = 3.0*torch.eye(dim,dim,device = x.device).view(1, dim, dim).repeat(batchSize,1,1).type(dtype)
         if iterN < 2:
            der_NSiter = 0.5*(der_postCom.bmm(I3 - A) - A.bmm(der_sacleTrace))
         else:
            dldY = 0.5*(der_postCom.bmm(I3 - Y[:,iterN-2,:,:].bmm(Z[:,iterN-2,:,:])) -
                          Z[:,iterN-2,:,:].bmm(Y[:,iterN-2,:,:]).bmm(der_postCom))
            dldZ = -0.5*Y[:,iterN-2,:,:].bmm(der_postCom).bmm(Y[:,iterN-2,:,:])
            for i in range(iterN-3, -1, -1):
               YZ = I3 - Y[:,i,:,:].bmm(Z[:,i,:,:])
               ZY = Z[:,i,:,:].bmm(Y[:,i,:,:])
               dldY_ = 0.5*(dldY.bmm(YZ) - 
                         Z[:,i,:,:].bmm(dldZ).bmm(Z[:,i,:,:]) - 
                             ZY.bmm(dldY))
               dldZ_ = 0.5*(YZ.bmm(dldZ) - 
                         Y[:,i,:,:].bmm(dldY).bmm(Y[:,i,:,:]) -
                            dldZ.bmm(ZY))
               dldY = dldY_
               dldZ = dldZ_
            der_NSiter = 0.5*(dldY.bmm(I3 - A) - dldZ - A.bmm(dldY))
         grad_input = der_NSiter.div(normA.view(batchSize,1,1).expand_as(x))
         grad_aux = der_NSiter.mul(x).sum(dim=1).sum(dim=1)
         for i in range(batchSize):
             grad_input[i,:,:] += (der_postComAux[i] \
                                   - grad_aux[i] / (normA[i] * normA[i])) \
                                   *torch.ones(dim,device = x.device).diag()
         return grad_input, None

def CovpoolLayer(var):
    return Covpool.apply(var)

def SqrtmLayer(var, iterN):
    return Sqrtm.apply(var, iterN)

class SOCA(nn.Module):
    # second-order Channel attention
    def __init__(self, channel, reduction=8):
        super(SOCA, self).__init__()
        self.max_pool = nn.MaxPool2d(kernel_size=2)

        self.conv_du = nn.Sequential(
            nn.Conv2d(channel, channel // reduction, 1, padding=0, bias=True),
            nn.ReLU(inplace=True),
            nn.Conv2d(channel // reduction, channel, 1, padding=0, bias=True),
            nn.Sigmoid()
        )

    def forward(self, x):
        batch_size, C, h, w = x.shape  # x: NxCxHxW
        N = int(h * w)
        min_h = min(h, w)
        h1 = 1000
        w1 = 1000
        if h < h1 and w < w1:
            x_sub = x
        elif h < h1 and w > w1:
            W = (w - w1) // 2
            x_sub = x[:, :, :, W:(W + w1)]
        elif w < w1 and h > h1:
            H = (h - h1) // 2
            x_sub = x[:, :, H:H + h1, :]
        else:
            H = (h - h1) // 2
            W = (w - w1) // 2
            x_sub = x[:, :, H:(H + h1), W:(W + w1)]
        cov_mat = CovpoolLayer(x_sub) # Global Covariance pooling layer
        cov_mat_sqrt = SqrtmLayer(cov_mat,5) # Matrix square root layer( including pre-norm,Newton-Schulz iter. and post-com. with 5 iteration)
        cov_mat_sum = torch.mean(cov_mat_sqrt,1)
        cov_mat_sum = cov_mat_sum.view(batch_size,C,1,1)
        y_cov = self.conv_du(cov_mat_sum)
        return y_cov*x

🔹 9. CBAM（Convolutional Block Attention Module）

机制： Channel Attention + Spatial Attention 串联使用

class ChannelAttentionModule(nn.Module):
    def __init__(self, c1, reduction=16):
        super(ChannelAttentionModule, self).__init__()
        mid_channel = c1 // reduction
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)

        self.shared_MLP = nn.Sequential(
            nn.Linear(in_features=c1, out_features=mid_channel),
            nn.LeakyReLU(0.1, inplace=True),
            nn.Linear(in_features=mid_channel, out_features=c1)
        )
        self.act = nn.Sigmoid()
        #self.act=nn.SiLU()
    def forward(self, x):
        avgout = self.shared_MLP(self.avg_pool(x).view(x.size(0),-1)).unsqueeze(2).unsqueeze(3)
        maxout = self.shared_MLP(self.max_pool(x).view(x.size(0),-1)).unsqueeze(2).unsqueeze(3)
        return self.act(avgout + maxout)
        
class SpatialAttentionModule(nn.Module):
    def __init__(self):
        super(SpatialAttentionModule, self).__init__()
        self.conv2d = nn.Conv2d(in_channels=2, out_channels=1, kernel_size=7, stride=1, padding=3)
        self.act = nn.Sigmoid()
    def forward(self, x):
        avgout = torch.mean(x, dim=1, keepdim=True)
        maxout, _ = torch.max(x, dim=1, keepdim=True)
        out = torch.cat([avgout, maxout], dim=1)
        out = self.act(self.conv2d(out))
        return out

class CBAM(nn.Module):
    def __init__(self, c1,c2):
        super(CBAM, self).__init__()
        self.channel_attention = ChannelAttentionModule(c1)
        self.spatial_attention = SpatialAttentionModule()

    def forward(self, x):
        out = self.channel_attention(x) * x
        out = self.spatial_attention(out) * out
        return out

🔹 10. GAMAttention

原理图：

import numpy as np
import torch
from torch import nn
from torch.nn import init

class GAMAttention(nn.Module):
       #https://paperswithcode.com/paper/global-attention-mechanism-retain-information
    def __init__(self, c1, c2, group=True,rate=4):
        super(GAMAttention, self).__init__()
        
        self.channel_attention = nn.Sequential(
            nn.Linear(c1, int(c1 / rate)),
            nn.ReLU(inplace=True),
            nn.Linear(int(c1 / rate), c1)
        )
        self.spatial_attention = nn.Sequential(
            nn.Conv2d(c1, c1//rate, kernel_size=7, padding=3,groups=rate)if group else nn.Conv2d(c1, int(c1 / rate), kernel_size=7, padding=3), 
            nn.BatchNorm2d(int(c1 /rate)),
            nn.ReLU(inplace=True),
            nn.Conv2d(c1//rate, c2, kernel_size=7, padding=3,groups=rate) if group else nn.Conv2d(int(c1 / rate), c2, kernel_size=7, padding=3), 
            nn.BatchNorm2d(c2)
        )

    def forward(self, x):
        b, c, h, w = x.shape
        x_permute = x.permute(0, 2, 3, 1).view(b, -1, c)
        x_att_permute = self.channel_attention(x_permute).view(b, h, w, c)
        x_channel_att = x_att_permute.permute(0, 3, 1, 2)
        x = x * x_channel_att
 
        x_spatial_att = self.spatial_attention(x).sigmoid()
        x_spatial_att=channel_shuffle(x_spatial_att,4) #last shuffle 
        out = x * x_spatial_att
        return out  

def channel_shuffle(x, groups=2):
        B, C, H, W = x.size()
        out = x.view(B, groups, C // groups, H, W).permute(0, 2, 1, 3, 4).contiguous()
        out=out.view(B, C, H, W) 
        return out

🔹 11. Coordinate attention

class h_sigmoid(nn.Module):
    def __init__(self, inplace=True):
        super(h_sigmoid, self).__init__()
        self.relu = nn.ReLU6(inplace=inplace)

    def forward(self, x):
        return self.relu(x + 3) / 6

class h_swish(nn.Module):
    def __init__(self, inplace=True):
        super(h_swish, self).__init__()
        self.sigmoid = h_sigmoid(inplace=inplace)

    def forward(self, x):
        return x * self.sigmoid(x)
class CA(nn.Module):
    # Coordinate Attention for Efficient Mobile Network Design
    '''
        Recent studies on mobile network design have demonstrated the remarkable effectiveness of channel attention (e.g., the Squeeze-and-Excitation attention) for lifting
    model performance, but they generally neglect the positional information, which is important for generating spatially selective attention maps. In this paper, we propose a
    novel attention mechanism for mobile iscyy networks by embedding positional information into channel attention, which
    we call “coordinate attention”. Unlike channel attention
    that transforms a feature tensor to a single feature vector iscyy via 2D global pooling, the coordinate attention factorizes channel attention into two 1D feature encoding 
    processes that aggregate features along the two spatial directions, respectively
    '''
    def __init__(self, inp, oup, reduction=32):
        super(CA, self).__init__()

        mip = max(8, inp // reduction)

        self.conv1 = nn.Conv2d(inp, mip, kernel_size=1, stride=1, padding=0)
        self.bn1 = nn.BatchNorm2d(mip)
        self.act = h_swish()
        
        self.conv_h = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)
        self.conv_w = nn.Conv2d(mip, oup, kernel_size=1, stride=1, padding=0)
        

    def forward(self, x):
        identity = x
        
        n,c,h,w = x.size()
        pool_h = nn.AdaptiveAvgPool2d((h, 1))
        pool_w = nn.AdaptiveAvgPool2d((1, w))
        x_h = pool_h(x)
        x_w = pool_w(x).permute(0, 1, 3, 2)

        y = torch.cat([x_h, x_w], dim=2)
        y = self.conv1(y)
        y = self.bn1(y)
        y = self.act(y) 
        
        x_h, x_w = torch.split(y, [h, w], dim=2)
        x_w = x_w.permute(0, 1, 3, 2)
        a_h = self.conv_h(x_h).sigmoid()
        a_w = self.conv_w(x_w).sigmoid()

        out = identity * a_w * a_h

        return out   

🔹 12. Efficient Channel Attention（ECA）

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

class ECAttention(nn.Module):
    """Constructs a ECA module.
    Args:
        channel: Number of channels of the input feature map
        k_size: Adaptive selection of kernel size automg
    """
    def __init__(self, c1,c2, k_size=3):
        super(ECAttention, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.conv = nn.Conv1d(1, 1, kernel_size=k_size, padding=(k_size - 1) // 2, bias=False)
        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        y = self.avg_pool(x)
        y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)
        y = self.sigmoid(y)

        return x * y.expand_as(x)