目录

摘要

1、通道注意力机制和空间注意力机制

2、SE-Net: Squeeze-and-Excitation Networks

SE模块的实现

SE的另一种实现方式

3、轻量模块ECANet（通道注意力超强改进）

4、Coordinate Attention

摘要

计算机视觉（computer vision）中的注意力机制（attention）的基本思想就是想让系统学会注意力——能够忽略无关信息而关注重点信息。

注意力机制按照关注的域来分:

空间域(spatial domain)
通道域(channel domain)
层域(layer domain)
混合域(mixed domain)
时间域(time domain)：还有另一种比较特殊的强注意力实现的注意力域，时间域(time domain)，但是因为强注意力是使用reinforcement learning来实现的，训练起来有所不同

1、通道注意力机制和空间注意力机制

Convolutional Block Attention Module (CBAM) 表示卷积模块的注意力机制模块。是一种结合了空间（spatial）和通道（channel）的注意力机制模块。相比于senet只关注通道（channel）的注意力机制可以取得更好的效果。

通道注意力：将输入的featuremap，分别经过基于width和height的global max pooling 和global average pooling，然后分别经过MLP。将MLP输出的特征进行基于elementwise的加和操作，再经过sigmoid激活操作，生成最终的channel attention featuremap。将该channel attention featuremap和input featuremap做elementwise乘法操作，生成Spatial attention模块需要的输入特征。

空间注意力：将Channel attention模块输出的特征图作为本模块的输入特征图。首先做一个基于channel的global max pooling 和global average pooling，然后将这2个结果基于channel 做concat操作。然后经过一个卷积操作，降维为1个channel。再经过sigmoid生成spatial attention feature。最后将该feature和该模块的输入feature做乘法，得到最终生成的特征。

代码如下：

      import torch.nn as nn
      import math
      try:
         from torch.hub import load_state_dict_from_url
      except ImportError:
         from torch.utils.model_zoo import load_url as load_state_dict_from_url
      import torch
      #通道注意力机制
      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 // 16, 1, bias=False)
              self.relu1 = nn.ReLU()
              self.fc2   = nn.Conv2d(in_planes // 16, 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)
      #空间注意力机制
      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)
              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)
  
 

使用举例，在Resnet网络中添加注意力机制

      class ResNet(nn.Module):
         def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
       groups=1, width_per_group=64, replace_stride_with_dilation=None,
       norm_layer=None):
             super(ResNet, self).__init__()
             if norm_layer is None:
                  norm_layer = nn.BatchNorm2d
              self._norm_layer = norm_layer
              self.inplanes = 64
              self.dilation = 1
             if replace_stride_with_dilation is None:
                 # each element in the tuple indicates if we should replace
                 # the 2x2 stride with a dilated convolution instead
                  replace_stride_with_dilation = [False, False, False]
             if len(replace_stride_with_dilation) != 3:
                 raise ValueError("replace_stride_with_dilation should be None "
                                  "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
              self.groups = groups
              self.base_width = width_per_group
              self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                     bias=False)
              self.bn1 = norm_layer(self.inplanes)
              self.relu = nn.ReLU(inplace=True)
             # 网络的第一层加入注意力机制
              self.ca = ChannelAttention(self.inplanes)
              self.sa = SpatialAttention()
              self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
              self.layer1 = self._make_layer(block, 64, layers[0])
              self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                             dilate=replace_stride_with_dilation[0])
              self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                             dilate=replace_stride_with_dilation[1])
              self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                             dilate=replace_stride_with_dilation[2])
             # 网络的卷积层的最后一层加入注意力机制
              self.ca1 = ChannelAttention(self.inplanes)
              self.sa1 = SpatialAttention()
              self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
              self.fc = nn.Linear(512 * block.expansion, num_classes)
             for m in self.modules():
                 if isinstance(m, nn.Conv2d):
                      nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                 elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                      nn.init.constant_(m.weight, 1)
                      nn.init.constant_(m.bias, 0)
             # Zero-initialize the last BN in each residual branch,
             # so that the residual branch starts with zeros, and each residual block behaves like an identity.
             # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
             if zero_init_residual:
                 for m in self.modules():
                     if isinstance(m, Bottleneck):
                          nn.init.constant_(m.bn3.weight, 0)
                     elif isinstance(m, BasicBlock):
                          nn.init.constant_(m.bn2.weight, 0)
         def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
              norm_layer = self._norm_layer
              downsample = None
              previous_dilation = self.dilation
             if dilate:
                  self.dilation *= stride
                  stride = 1
             if stride != 1 or self.inplanes != planes * block.expansion:
                  downsample = nn.Sequential(
                      conv1x1(self.inplanes, planes * block.expansion, stride),
                      norm_layer(planes * block.expansion),
                  )
              layers = []
              layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                                  self.base_width, previous_dilation, norm_layer))
              self.inplanes = planes * block.expansion
             for _ in range(1, blocks):
                  layers.append(block(self.inplanes, planes, groups=self.groups,
                                      base_width=self.base_width, dilation=self.dilation,
                                      norm_layer=norm_layer))
             return nn.Sequential(*layers)
         def forward(self, x):
              x = self.conv1(x)
              x = self.bn1(x)
              x = self.relu(x)
              x = self.ca(x) * x
              x = self.sa(x) * x
              x = self.maxpool(x)
              x = self.layer1(x)
              x = self.layer2(x)
              x = self.layer3(x)
              x = self.layer4(x)
              x = self.ca1(x) * x
              x = self.sa1(x) * x
              x = self.avgpool(x)
              x = x.reshape(x.size(0), -1)
              x = self.fc(x)
             return x
  
 

注意点：因为不能改变ResNet的网络结构，所以CBAM不能加在block里面，因为加进去网络结构发生了变化，所以不能用预训练参数。加在最后一层卷积和第一层卷积不改变网络，可以用预训练参数。

添加位置：

      # 网络的第一层加入注意力机制
       self.ca = ChannelAttention(self.inplanes)
       self.sa = SpatialAttention()
  
 

和

      # 网络的卷积层的最后一层加入注意力机制
      self.ca1 = ChannelAttention(self.inplanes)
      self.sa1 = SpatialAttention()
  
 

forWord部分代码

      x = self.ca(x) * x
      x = self.sa(x) * x
      x = self.maxpool(x)
      x = self.layer1(x)
      x = self.layer2(x)
      x = self.layer3(x)
      x = self.layer4(x)
      x = self.ca1(x) * x
      x = self.sa1(x) * x
  
 

2、SE-Net: Squeeze-and-Excitation Networks

论文链接：https://arxiv.org/abs/1709.01507
代码地址：https://github.com/hujie-frank/SENet
PyTorch代码地址：https://github.com/miraclewkf/SENet-PyTorch

SE-Net赢得了最后一届ImageNet 2017竞赛分类任务的冠军，其基本原理是对于每个输出channel，预测一个常数权重，对每个channel加权一下。结构如下图：

第一步每个通道H*W个数全局平均池化得到一个标量，称之为Squeeze，然后两个FC得到01之间的一个权重值，对原始的每个HxW的每个元素乘以对应通道的权重，得到新的feature map，称之为Excitation。任意的原始网络结构，都可以通过这个Squeeze-Excitation的方式进行feature recalibration，如下图。

具体实现上就是一个Global Average Pooling-FC-ReLU-FC-Sigmoid，第一层的FC会把通道降下来，然后第二层FC再把通道升上去，得到和通道数相同的C个权重，每个权重用于给对应的一个通道进行加权。上图中的r就是缩减系数，实验确定选取16，可以得到较好的性能并且计算量相对较小。SENet的核心思想在于通过网络根据loss去学习特征权重，使得有效的feature map权重大，无效或效果小的feature map权重小的方式训练模型达到更好的结果。

SE模块的实现

这里给出PyTorch版本的实现（参考senet.pytorch）:

      class SELayer(nn.Module):
         def __init__(self, channel, reduction=16):
             super(SELayer, self).__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 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)
  
 

将SE模块用在Resnet网络，只需要将SE模块加入到残差单元（应用在残差学习那一部分）就可以：

      class SEBottleneck(nn.Module):
              expansion = 4
             def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
                 super(SEBottleneck, self).__init__()
                  self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
                  self.bn1 = nn.BatchNorm2d(planes)
                  self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                         padding=1, bias=False)
                  self.bn2 = nn.BatchNorm2d(planes)
                  self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
                  self.bn3 = nn.BatchNorm2d(planes * 4)
                  self.relu = nn.ReLU(inplace=True)
                  self.se = SELayer(planes * 4, reduction)
                  self.downsample = downsample
                  self.stride = stride
             def forward(self, x):
                  residual = x
                  out = self.conv1(x)
                  out = self.bn1(out)
                  out = self.relu(out)
                  out = self.conv2(out)
                  out = self.bn2(out)
                  out = self.relu(out)
                  out = self.conv3(out)
                  out = self.bn3(out)
                  out = self.se(out)
                 if self.downsample is not None:
                      residual = self.downsample(x)
                  out += residual
                  out = self.relu(out)
                 return out
  
 

SE的另一种实现方式

该方式使用卷积替代全连接。

      class SEBlock(nn.Module):
          def __init__(self, input_channels, internal_neurons):
              super(SEBlock, self).__init__()
              self.down = nn.Conv2d(in_channels=input_channels, out_channels=internal_neurons, kernel_size=1, stride=1,
                                    bias=True, padding_mode='same')
              self.up = nn.Conv2d(in_channels=internal_neurons, out_channels=input_channels, kernel_size=1, stride=1,
                                  bias=True, padding_mode='same')
          def forward(self, inputs):
              x = F.avg_pool2d(inputs, kernel_size=inputs.size(3))
              x = self.down(x)
              x = F.leaky_relu(x)
              x = self.up(x)
              x = F.sigmoid(x)
              x = x.repeat(1, 1, inputs.size(2), inputs.size(3))
              return inputs * x
  
 

3、轻量模块ECANet（通道注意力超强改进）

论文链接：https://arxiv.org/abs/1910.03151

代码地址：https://github.com/BangguWu/ECANet

论文翻译：https://wanghao.blog.csdn.net/article/details/113073026

ECANet主要对SENet模块进行了一些改进，提出了一种不降维的局部跨信道交互策略（ECA模块）和自适应选择一维卷积核大小的方法，从而实现了性能上的提优。

ECANet的实现：

      class eca_layer(nn.Module):
         """Constructs a ECA module.
       Args:
       channel: Number of channels of the input feature map
       k_size: Adaptive selection of kernel size
       """
         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)
  
 

ECANet在模型中的调用

      channelNum=64
      class CRBlock(nn.Module):
         def __init__(self):
             super(CRBlock, self).__init__()
              self.convban = nn.Sequential(OrderedDict([
                  ("conv3x3_bn", ConvBN(channelNum, channelNum, 3)),
              ]))
              self.path1 = Encoder_conv(channelNum, 2)
              self.path2 = nn.Sequential(OrderedDict([
                  ('conv1x5', ConvBN(channelNum, channelNum, [1, 3])),
                  ('conv5x1', ConvBN(channelNum, channelNum, 3)),
                  ('ac', ACBlock(channelNum, channelNum, kernel_size=3)),
                  ('eca', eca_layer(channelNum, 3)),
                 # ('ac', ACBlock(channelNum, channelNum, kernel_size=3)),
              ]))
              self.path2 = nn.Sequential(OrderedDict([
                  ('conv1x5', ConvBN(channelNum, channelNum, [1, 5])),
                  ('conv5x1', ConvBN(channelNum, channelNum, [5, 1])),
                  ("conv9x1_bn", ConvBN(channelNum, channelNum, 1)),
                  ('eca', eca_layer(channelNum, 3)),
              ]))
              self.encoder_conv = Encoder_conv(channelNum * 4)
              self.encoder_conv1 = ConvBN(channelNum * 4, channelNum, 1)
              self.identity = nn.Identity()
              self.relu = Mish()
              self.ca1 = eca_layer(channelNum * 4, 3)
             # self.ca2 = eca_layer(channelNum*4, 1)
         def forward(self, x):
              identity = self.identity(x)
              x = self.convban(x)
              out1 = self.path1(x)
              out2 = self.path2(x)
              out3 = self.path2(x)
              out = torch.cat((out1, out2, out3, x), dim=1)
              out = self.relu(out)
              out = self.encoder_conv(out)
              out = self.ca1(out)
              out = self.encoder_conv1(out)
              out = self.relu(out + identity)
             return out
  
 

4、Coordinate Attention

论文：https://arxiv.org/abs/2103.02907

代码链接：https://github.com/Andrew-Qibin/CoordAttention

Coordinate Attention通过精确的位置信息对通道关系和长期依赖性进行编码，具体操作分为Coordinate信息嵌入和Coordinate Attention生成2个步骤。

网络结构如下图：

详见：https://mp.weixin.qq.com/s/zQoo_IRfqVa2WzFOU_de1Q

Coordinate Attention的pytorch实现。

      import torch
      from torch import nn
      class CA_Block(nn.Module):
         def __init__(self, channel, h, w, reduction=16):
             super(CA_Block, self).__init__()
              self.h = h
              self.w = w
              self.avg_pool_x = nn.AdaptiveAvgPool2d((h, 1))
              self.avg_pool_y = nn.AdaptiveAvgPool2d((1, w))
              self.conv_1x1 = nn.Conv2d(in_channels=channel, out_channels=channel//reduction, kernel_size=1, stride=1, bias=False)
              self.relu = nn.ReLU()
              self.bn = nn.BatchNorm2d(channel//reduction)
              self.F_h = nn.Conv2d(in_channels=channel//reduction, out_channels=channel, kernel_size=1, stride=1, bias=False)
              self.F_w = nn.Conv2d(in_channels=channel//reduction, out_channels=channel, kernel_size=1, stride=1, bias=False)
              self.sigmoid_h = nn.Sigmoid()
              self.sigmoid_w = nn.Sigmoid()
         def forward(self, x):
              x_h = self.avg_pool_x(x).permute(0, 1, 3, 2)
              x_w = self.avg_pool_y(x)
              x_cat_conv_relu = self.relu(self.conv_1x1(torch.cat((x_h, x_w), 3)))
              x_cat_conv_split_h, x_cat_conv_split_w = x_cat_conv_relu.split([self.h, self.w], 3)
              s_h = self.sigmoid_h(self.F_h(x_cat_conv_split_h.permute(0, 1, 3, 2)))
              s_w = self.sigmoid_w(self.F_w(x_cat_conv_split_w))
              out = x * s_h.expand_as(x) * s_w.expand_as(x)
             return out
      if __name__ == '__main__':
          x = torch.randn(1, 16, 128, 64)    # b, c, h, w
          ca_model = CA_Block(channel=16, h=128, w=64)
          y = ca_model(x)
         print(y.shape)
  
 

参考文章：

https://zhuanlan.zhihu.com/p/99261200?from=singlemessage

https://blog.csdn.net/chanbo8205/article/details/110880273

文章来源: wanghao.blog.csdn.net，作者：AI浩，版权归原作者所有，如需转载，请联系作者。

原文链接：wanghao.blog.csdn.net/article/details/114449042