注意力机制详解(注意力机制有什么作用)

注意力机制详解 1.特性即插即用在特征提取效果显著微调模型的小技巧2.核心思想本质上与人类视觉选择性注意力机制类似，从众多信息中选出对当前任务目标更为关键的信息。通过手段获取每张特征图重要性的差异抑制无用信息，凸显有用信息（将注意力集中在图中重要区域）3.广泛应用自然语言处理图像识别语音识别4.卷积神经网络中注意力机制研究现状4.1单路注意力：

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1.SE-Net(Squeeze and Excitation)(2018年CVPR提出)：利用注意力机制思想，显式地建模特征图之间的相互依赖关系，并通过学习的方式来自适应地获取到每张特征图的重要性，然后依照这个重要程度去对原数据进行更新。提升有用的特征重要程度同时降低无用特征的重要性，并以不同通道的重要性为指导，将计算资源合理地投入不同通道当中。

 步骤解析如下： 基础的卷积操作：提取特征图 ，得到 C个大小为    H*W的feature map。公式如下：

 

      ps:Vc表示第c个卷积核，Xs表示第s个输入

Squeeze操作：全局平均池化将H*W*C的输入——变成1*1*C的输出

     ps:此步骤表明该层C个Feature map的数值分布情况（全局信息），是分别在某个channel的           feature map中操作。

 Excitation操作：W1*z全连接层操作，C*C/r (r:缩放参数，目的是减少channel个数降低计算量)，W1*z维度：1*1*C/r，再经过一个ReLU层，维度不变。再与W2相乘，同样也是一个全连接层操作：W2维度 C/r*C，输出维度为1*1*C，经过sigmoid函数，得到s。

      ps:s维度 1*1*C，s是用于刻画U中feature map的权重，σ为relu激活函数，δ代表sigmoid激活          函数。

Uc是一个二维矩阵，Sc是一个数即权重，因此相当于把Uc矩阵中的每个值都乘以Sc。

   缺点：降维对通道注意预测带来了副作用，捕获所有通道之间的依赖是低效的，也是不必要的。

   只注重通道内部信息的综合，没有考虑到相邻信道信息的重要性。

2.ECA-Net(Efficient Channel Attention)(2020CVPR提出，对SE-Net的改进)：不降低通道维数来进行跨通道信息交互的ECA模块。具体如下图所示：

创新点：

将SEBlock中MLP模块(FC->ReLU>FC->Sigmoid)，转变为一维卷积的形式，有效减少了参数计算量。一维卷积自带的功效就是非全连接，每一次卷积过程只和部分通道的作用，即实现了适当的跨通道交互而不是像全连接层一样全通道交互。

代码实现：

class eca_layer(nn.Module): def __init__(self, channel, k_size=3): super(eca_layer, 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): # x: input features with shape [b, c, h, w] b, c, h, w = x.size() # feature descriptor on the global spatial information y = self.avg_pool(x) # Two different branches of ECA module y = self.conv(y.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1) # Multi-scale information fusion y = self.sigmoid(y) return x * y.expand_as(x)

 SE-Net对比ECA-Net:

# SEBlock 采用全连接层方式def forward(self, x): b, c, _, _ = x.shape v = self.global_pooling(x).view(b, c) v = self.fc_layers(v).view(b, c, 1, 1) v = self.sigmoid(v) return x * v# ECABlock 采用一维卷积方式def forward(self, x):v = self.avg_pool(x)v = self.conv(v.squeeze(-1).transpose(-1, -2)).transpose(-1, -2).unsqueeze(-1)v = self.sigmoid(v)return x * v4.2多路注意力

1.SK-Net

2.Res-Net

3.CBAM(Convolutional Block Attention Module)(2018ECCV):对于卷积网络中的特征图而言，不仅通道蕴含着丰富的注意力信息，通道内部：即特征图像素点间也具有大量的注意力信息，以往注意力只关注通道，不关注空间。因而构建2个子模块，空间注意力SAM(Spatial Attention Module)，通道注意力模块CAM(Channel Attention Module）汇总空间和通道两方面的注意力信息，并进行综合，获得更全面可靠的注意力信息。

CAM：1.特征图H*W*C 经过 MaxPool+AvgPool 得到1*1*C特征图；2.输入两层MLP神经网络：第一层神经元个数C/r（r为缩放率），激活函数ReLU，第二层神经元个数C，激活函数sigmoid。3.element-wise的加和操作，再经过sigmoid激活操作，生成最终的通道注意力特征。

           SAM：1.空间注意力模块将通道注意力模块输出的特征图F作为本模块的输入特征图，特征图H*W*C 经过基于通道的 MaxPool+AvgPool 得到2个H*W*1的特征图，基于通道进行拼接；2.经过一个7*7卷积操作，降维为H*W*1，再经过sigmoid生成空间注意力特征。3.最后将该向量和该模块的输入特征图做乘操作，得到最终生成的特征。

代码实现：先通道后空间效果好  

class ChannelAttention(nn.Module): def __init__(self, in_planes, ratio=16): super(ChannelAttention, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc1 = nn.Conv2d(in_planes, in_planes // ratio, 1, bias=False) self.relu1 = nn.ReLU() self.fc2 = nn.Conv2d(in_planes // ratio, in_planes, 1, bias=False) self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = self.fc2(self.relu1(self.fc1(self.avg_pool(x)))) max_out = self.fc2(self.relu1(self.fc1(self.max_pool(x)))) out = avg_out + max_out return self.sigmoid(out)class SpatialAttention(nn.Module): def __init__(self, kernel_size=7): super(SpatialAttention, self).__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) # 7,3 3,1 self.sigmoid = nn.Sigmoid() def forward(self, x): avg_out = torch.mean(x, dim=1, keepdim=True) max_out, _ = torch.max(x, dim=1, keepdim=True) x = torch.cat([avg_out, max_out], dim=1) x = self.conv1(x) return self.sigmoid(x)class CBAM(nn.Module): def __init__(self, in_planes, ratio=16, kernel_size=7): super(CBAM, self).__init__() self.ca = ChannelAttention(in_planes, ratio) self.sa = SpatialAttention(kernel_size) def forward(self, x): out = x * self.ca(x) result = out * self.sa(out) return result

4.双注意力网络DA-Net(CVPR2019)：类似于CBAM,综合考虑通道和空间两路的注意力信息。但是获取注意力信息的方式不同，CBAM串行获取两路注意力信息的，而DA-Net是采用并行两路获取注意力信息的。

PAM 位置注意力具体步骤

 CAM通道注意力具体步骤

 代码实现class PAM_Module(Module): def __init__(self, in_dim): super(PAM_Module, self).__init__() self.chanel_in = in_dim # 先经过3个卷积层生成3个新特征图B C D （尺寸不变） self.query_conv = Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1) self.key_conv = Conv2d(in_channels=in_dim, out_channels=in_dim//8, kernel_size=1) self.value_conv = Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1) self.gamma = Parameter(torch.zeros(1)) # α尺度系数初始化为0，并逐渐地学习分配到更大的权重 self.softmax = Softmax(dim=-1) # 对每一行进行softmax def forward(self, x): m_batchsize, C, height, width = x.size() proj_query = self.query_conv(x).view(m_batchsize, -1, width*height).permute(0, 2, 1) # C -> (N,C,HW) proj_key = self.key_conv(x).view(m_batchsize, -1, width*height) # BC，空间注意图 -> (N,HW,HW) energy = torch.bmm(proj_query, proj_key) # S = softmax(BC) -> (N,HW,HW) attention = self.softmax(energy) # D -> (N,C,HW) proj_value = self.value_conv(x).view(m_batchsize, -1, width*height) # DS -> (N,C,HW) out = torch.bmm(proj_value, attention.permute(0, 2, 1)) # torch.bmm表示批次矩阵乘法 # output -> (N,C,H,W) out = out.view(m_batchsize, C, height, width) out = self.gamma*out + x return outclass CAM_Module(Module): """ Channel attention module""" def __init__(self, in_dim): super(CAM_Module, self).__init__() self.chanel_in = in_dim self.gamma = Parameter(torch.zeros(1)) # β尺度系数初始化为0，并逐渐地学习分配到更大的权重 self.softmax = Softmax(dim=-1) # 对每一行进行softmax def forward(self,x): m_batchsize, C, height, width = x.size() # A -> (N,C,HW) proj_query = x.view(m_batchsize, C, -1) # A -> (N,HW,C) proj_key = x.view(m_batchsize, C, -1).permute(0, 2, 1) # 矩阵乘积，通道注意图：X -> (N,C,C) energy = torch.bmm(proj_query, proj_key) # 这里实现了softmax用最后一维的最大值减去了原始数据，获得了一个不是太大的值 # 沿着最后一维的C选择最大值，keepdim保证输出和输入形状一致，除了指定的dim维度大小为1 # expand_as表示以复制的形式扩展到energy的尺寸 energy_new = torch.max(energy, -1, keepdim=True)[0].expand_as(energy)-energy attention = self.softmax(energy_new) # A -> (N,C,HW) proj_value = x.view(m_batchsize, C, -1) # XA -> （N,C,HW） out = torch.bmm(attention, proj_value) # output -> (N,C,H,W) out = out.view(m_batchsize, C, height, width) out = self.gamma*out + x return outclass DANetHead(nn.Module): def __init__(self, in_channels, out_channels, norm_layer): super(DANetHead, self).__init__() inter_channels = in_channels // 4 # in_channels=2018，通道数缩减为512 self.conv5a = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) self.conv5c = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) self.sa = PAM_Module(inter_channels) # 空间注意力模块 self.sc = CAM_Module(inter_channels) # 通道注意力模块 self.conv51 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) self.conv52 = nn.Sequential(nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.ReLU()) # nn.Dropout2d(p,inplace)：p表示将元素置0的概率；inplace若设置为True，会在原地执行操作。 self.conv6 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(512, out_channels, 1)) # 输出通道数为类别的数目 self.conv7 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(512, out_channels, 1)) self.conv8 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(512, out_channels, 1)) def forward(self, x): # 经过一个1×1卷积降维后，再送入空间注意力模块 feat1 = self.conv5a(x) sa_feat = self.sa(feat1) # 先经过一个卷积后，再使用有dropout的1×1卷积输出指定的通道数 sa_conv = self.conv51(sa_feat) sa_output = self.conv6(sa_conv) # 经过一个1×1卷积降维后，再送入通道注意力模块 feat2 = self.conv5c(x) sc_feat = self.sc(feat2) # 先经过一个卷积后，再使用有dropout的1×1卷积输出指定的通道数 sc_conv = self.conv52(sc_feat) sc_output = self.conv7(sc_conv) feat_sum = sa_conv+sc_conv # 两个注意力模块结果相加 sasc_output = self.conv8(feat_sum) # 最后再送入1个有dropout的1×1卷积中 output = [sasc_output] output.append(sa_output) output.append(sc_output) return tuple(output) # 输出模块融合后的结果，以及两个模块各自的结果 优点：DANet使用注意力模块，可以更有效地捕获全局依赖关系和长程上下文信息，在场景分割中学到更好的特征表示。位置注意模块能够捕捉到清晰的语义相似性和长程关系。当通道注意模块增强后，特定语义的响应是明显的。缺点：矩阵计算使得算法的计算复杂度较高？模型的鲁棒性？缺少和DeepLab v3+的比较（可能没有DeepLab v3+的效果好）？

5.金字塔特征注意力网络（Pyramid FeatureAttention Network(2019CVPR)SE仅仅考虑了通道注意力，忽略了空间注意力。2.BAM和CBAM考虑了通道注意力和空间注意力，但仍存在两个最重要的缺点：(1)没有捕获不同尺度的空间信息来丰富特征空间。(2)空间注意力仅仅考虑了局部区域的信息，而无法建立远距离的依赖。3.后续出现的PyConv，Res2Net和HS-ResNet都用于解决CBAM的这两个缺点，但计算量太大。

PSA模块

代码实现class SEWeightModule(nn.Module): def __init__(self, channels=64, reduction=16): super(SEWeightModule, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, stride=1, padding=0) self.relu = nn.ReLU(inplace=True) self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, stride=1, padding=0) self.sigmoid = nn.Sigmoid() def forward(self, x): x = self.avg_pool(x) x = self.fc1(x) x = self.relu(x) x = self.fc2(x) x = self.sigmoid(x) return xclass PAM_Module(nn.Module): # Position attention module def __init__(self, in_dim=64): super(PAM_Module, self).__init__() self.chanel_in = in_dim self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 2, kernel_size=1) self.key_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim // 2, kernel_size=1) self.value_conv = nn.Conv2d(in_channels=in_dim, out_channels=in_dim, kernel_size=1) self.gamma = nn.Parameter(torch.zeros(1)) self.softmax = nn.Softmax(dim=-1) def forward(self, x): # inputs : # x : input feature maps( B, C, H, W) # returns : # out : attention value + input feature # attention: B*(H*W)*(H*W) # [105, 16, 19, 19] m_batchsize, C, height, width = x.size() proj_query = self.query_conv(x).view(m_batchsize, -1, width * height).permute(0, 2, 1) proj_key = self.key_conv(x).view(m_batchsize, -1, width * height) energy = torch.bmm(proj_query, proj_key) attention = self.softmax(energy) proj_value = self.value_conv(x).view(m_batchsize, -1, width * height) out = torch.bmm(proj_value, attention.permute(0, 2, 1)) out = out.view(m_batchsize, C, height, width) out = self.gamma * out + x return out# 通道注意力class CAM_Scale(nn.Module): def __init__(self , channels=64 , ratio = 16): super(CAM_Scale, self).__init__() self.avg_pool = nn.AdaptiveAvgPool2d(1) # 通道数不变，H*W*C变为1*1*64 self.max_pool = nn.AdaptiveMaxPool2d(1) self.fc_layers=nn.Sequential( nn.Conv2d(channels , channels // ratio , 1 , bias = False), nn.LeakyReLU(), nn.Conv2d(channels//ratio , channels ,1, bias = False) ) self.sigmoid = nn.Sigmoid() def forward(self , x): avg_out = self.fc2(self.leakyrelu(self.fc1(self.avg_pool(x)))) #两层神经网络共享 max_out = self.fc2(self.leakyrelu(self.fc1(self.max_pool(x)))) out = self.sigmoid(avg_out + max_out) return x*outclass DANetHead(nn.Module): def __init__(self, in_channels=64, out_channels=64): super(DANetHead, self).__init__() inter_channels = in_channels // 4 self.conv5a = nn.Sequential( nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), nn.BatchNorm2d(inter_channels), nn.LeakyReLU()) self.conv5c = nn.Sequential( nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), nn.BatchNorm2d(inter_channels), nn.leakyReLU()) self.sa = PAM_Module(inter_channels) self.sc = CAM_Module(inter_channels) self.conv51 = nn.Sequential( nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False), nn.BatchNorm2d(inter_channels), nn.LeakyReLU()) self.conv52 = nn.Sequential( nn.Conv2d(inter_channels, inter_channels, 3, padding=1, bias=False), nn.BatchNorm2d(inter_channels), nn.LeakyReLU()) self.conv8 = nn.Sequential(nn.Dropout2d(0.1, False), nn.Conv2d(inter_channels, out_channels, 1)) # se self.se = SEWeightModule(out_channels) def forward(self, x): feat1 = self.conv5a(x) sa_feat = self.sa(feat1) sa_conv = self.conv51(sa_feat) feat2 = self.conv5c(x) sc_feat = self.sc(feat2) sc_conv = self.conv52(sc_feat) feat_sum = sa_conv + sc_conv sasc_output = self.conv8(feat_sum) # se sasc_output = self.se(sasc_output) return sasc_output# Pyramid Split Dual Attentionclass PSDAModule(nn.Module): def __init__(self, inplans=64, planes=64, conv_kernels=[3, 5, 7, 9], stride=1, conv_groups=[1, 4, 8, 16]): super(PSDAModule, self).__init__() self.conv_1 = nn.Conv2d(inplans, planes // 4, kernel_size=conv_kernels[0], padding=conv_kernels[0] // 2, stride=stride, groups=conv_groups[0]) self.conv_2 = nn.Conv2d(inplans, planes // 4, kernel_size=conv_kernels[1], padding=conv_kernels[1] // 2, stride=stride, groups=conv_groups[1]) self.conv_3 = nn.Conv2d(inplans, planes // 4, kernel_size=conv_kernels[2], padding=conv_kernels[2] // 2, stride=stride, groups=conv_groups[2]) self.conv_4 = nn.Conv2d(inplans, planes // 4, kernel_size=conv_kernels[3], padding=conv_kernels[3] // 2, stride=stride, groups=conv_groups[3]) self.dan = DANetHead(planes // 4, planes // 4) self.split_channel = planes // 4 self.softmax = nn.Softmax(dim=1) def forward(self, x): batch_size = x.shape[0] # 多尺度分组 x1 = self.conv_1(x) x2 = self.conv_2(x) x3 = self.conv_3(x) x4 = self.conv_4(x) feats = torch.cat((x1, x2, x3, x4), dim=1) # [105, 64, 41, 41] feats = feats.view(batch_size, 4, self.split_channel, feats.shape[2], feats.shape[3]) # [105, 4, 16, 41, 41] x1_dan = self.dan(x1) # [105, 16, 1, 1] x2_dan = self.dan(x2) x3_dan = self.dan(x3) x4_dan = self.dan(x4) x_se = torch.cat((x1_dan, x2_dan, x3_dan, x4_dan), dim=1) # [105, 64, 1, 1] attention_vectors = x_se.view(batch_size, 4, self.split_channel, 1, 1) attention_vectors = self.softmax(attention_vectors) feats_weight = feats * attention_vectors for i in range(4): x_se_weight_fp = feats_weight[:, i, :, :] if i == 0: out = x_se_weight_fp else: out = torch.cat((x_se_weight_fp, out), dim=1) return out