聊一聊计算机视觉中常用的注意力机制以及Pytorch代码实现

注意力机制（Attention）是深度学习中常用的tricks，可以在模型原有的基础上直接插入，进一步增强你模型的性能。注意力机制起初是作为自然语言处理中的工作Attention Is All You Need被大家所熟知，从而也引发了一系列的XX is All You Need的论文命题，SENET-Squeeze-and-Excitation Networks是注意力机制在计算机视觉中应用的早期工作之一，并获得了2017年imagenet，同时也是最后一届Imagenet比赛的冠军，后面就又出现了各种各样的注意力机制，应用在计算机视觉的任务中，今天我们就来一起聊一聊计算机视觉中常用的注意力机制以及他们对应的Pytorch代码实现，另外我还使用这些注意力机制做了一些目标检测的实验，实验效果我也一并放在博客中，大家可以一起对自己感兴趣的部分讨论讨论。

新出的手把手教程，感兴趣的兄弟们快去自己动手试试看！

手把手教你使用YOLOV5训练自己的目标检测模型-口罩检测-视频教程_dejahu的博客-CSDN博客

这里是我数据集的基本情况，这里我使用的是交通标志检测的数据集

CocoDataset Train dataset with number of images 2226, and instance counts: 
+------------+-------+-----------+-------+-----------+-------+-----------------------------+-------+---------------------+-------+
| category   | count | category  | count | category  | count | category                    | count | category            | count |
+------------+-------+-----------+-------+-----------+-------+-----------------------------+-------+---------------------+-------+
| 0 [red_tl] | 1465  | 1 [arr_s] | 1133  | 2 [arr_l] | 638   | 3 [no_driving_mark_allsort] | 622   | 4 [no_parking_mark] | 1142  |
+------------+-------+-----------+-------+-----------+-------+-----------------------------+-------+---------------------+-------+

baseline选择的是fasterrcnn，实验的结果如下：

 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.341
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.502
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.400
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.115
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.473
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.655
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.417
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.417
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.417
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.156
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.570
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.726

如果大家遇到论文下载比较慢

推荐使用中科院的 arxiv 镜像: http://xxx.itp.ac.cn, 国内网络能流畅访问
简单直接的方法是, 把要访问 arxiv 链接中的域名从 https://arxiv.org 换成 http://xxx.itp.ac.cn , 比如:

从 https://arxiv.org/abs/1901.07249 改为 http://xxx.itp.ac.cn/abs/1901.07249

1. SeNet: Squeeze-and-Excitation Attention

论文地址：https://arxiv.org/abs/1709.01507

网络结构

对通道做注意力机制，通过全连接层对每个通道进行加权。
Pytorch代码

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

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)

if name == 'main':
input = torch.randn(50, 512, 7, 7)
se = SEAttention(channel=512, reduction=8)
output = se(input)
print(output.shape)


* 实验结果

  ```bash
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.338
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.511
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.375
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.126
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.458
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.696
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.411
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.411
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.411
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.163
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.551
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.758

2. （有用）CBAM: Convolutional Block Attention Module

论文地址：CBAM: Convolutional Block Attention Module

网络结构

对通道方向上做注意力机制之后再对空间方向上做注意力机制
Pytorch代码

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

class ChannelAttention(nn.Module):
def init(self, channel, reduction=16):
super().init()
self.maxpool = nn.AdaptiveMaxPool2d(1)
self.avgpool = nn.AdaptiveAvgPool2d(1)
self.se = nn.Sequential(
nn.Conv2d(channel, channel // reduction, 1, bias=False),
nn.ReLU(),
nn.Conv2d(channel // reduction, channel, 1, bias=False)
)
self.sigmoid = nn.Sigmoid()

  def forward(self, x):
      max_result = self.maxpool(x)
      avg_result = self.avgpool(x)
      max_out = self.se(max_result)
      avg_out = self.se(avg_result)
      output = self.sigmoid(max_out + avg_out)
      return output

class SpatialAttention(nn.Module):
def init(self, kernel_size=7):
super().init()
self.conv = nn.Conv2d(2, 1, kernel_size=kernel_size, padding=kernel_size // 2)
self.sigmoid = nn.Sigmoid()

  def forward(self, x):
      max_result, _ = torch.max(x, dim=1, keepdim=True)
      avg_result = torch.mean(x, dim=1, keepdim=True)
      result = torch.cat([max_result, avg_result], 1)
      output = self.conv(result)
      output = self.sigmoid(output)
      return output

class CBAMBlock(nn.Module):

  def __init__(self, channel=512, reduction=16, kernel_size=49):
      super().__init__()
      self.ca = ChannelAttention(channel=channel, reduction=reduction)
      self.sa = SpatialAttention(kernel_size=kernel_size)

  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()
      residual = x
      out = x * self.ca(x)
      out = out * self.sa(out)
      return out + residual

if name == 'main':
input = torch.randn(50, 512, 7, 7)
kernel_size = input.shape[2]
cbam = CBAMBlock(channel=512, reduction=16, kernel_size=kernel_size)
output = cbam(input)
print(output.shape)


* 实验结果

  ```bash
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.364
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.544
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.425
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.137
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.499
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.674
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.439
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.439
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.439
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.185
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.590
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.755

3. BAM: Bottleneck Attention Module

论文地址：https://arxiv.org/pdf/1807.06514.pdf

网络结构
Pytorch代码

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

class Flatten(nn.Module):
def forward(self, x):
return x.view(x.shape[0], -1)

class ChannelAttention(nn.Module):
def init(self, channel, reduction=16, num_layers=3):
super().init()
self.avgpool = nn.AdaptiveAvgPool2d(1)
gate_channels = [channel]
gate_channels += [channel // reduction] * num_layers
gate_channels += [channel]

      self.ca = nn.Sequential()
      self.ca.add_module('flatten', Flatten())
      for i in range(len(gate_channels) - 2):
          self.ca.add_module('fc%d' % i, nn.Linear(gate_channels[i], gate_channels[i + 1]))
          self.ca.add_module('bn%d' % i, nn.BatchNorm1d(gate_channels[i + 1]))
          self.ca.add_module('relu%d' % i, nn.ReLU())
      self.ca.add_module('last_fc', nn.Linear(gate_channels[-2], gate_channels[-1]))

  def forward(self, x):
      res = self.avgpool(x)
      res = self.ca(res)
      res = res.unsqueeze(-1).unsqueeze(-1).expand_as(x)
      return res

class SpatialAttention(nn.Module):
def init(self, channel, reduction=16, num_layers=3, dia_val=2):
super().init()
self.sa = nn.Sequential()
self.sa.add_module('conv_reduce1',
nn.Conv2d(kernel_size=1, in_channels=channel, out_channels=channel // reduction))
self.sa.add_module('bn_reduce1', nn.BatchNorm2d(channel // reduction))
self.sa.add_module('relu_reduce1', nn.ReLU())
for i in range(num_layers):
self.sa.addmodule('conv%d' % i, nn.Conv2d(kernel_size=3, in_channels=channel // reduction,
out_channels=channel // reduction, padding=1, dilation=dia_val))
self.sa.addmodule('bn%d' % i, nn.BatchNorm2d(channel // reduction))
self.sa.addmodule('relu%d' % i, nn.ReLU())
self.sa.add_module('last_conv', nn.Conv2d(channel // reduction, 1, kernel_size=1))

  def forward(self, x):
      res = self.sa(x)
      res = res.expand_as(x)
      return res

class BAMBlock(nn.Module):

  def __init__(self, channel=512, reduction=16, dia_val=2):
      super().__init__()
      self.ca = ChannelAttention(channel=channel, reduction=reduction)
      self.sa = SpatialAttention(channel=channel, reduction=reduction, dia_val=dia_val)
      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)

  def forward(self, x):
      b, c, _, _ = x.size()
      sa_out = self.sa(x)
      ca_out = self.ca(x)
      weight = self.sigmoid(sa_out + ca_out)
      out = (1 + weight) * x
      return out

if name == 'main':
input = torch.randn(50, 512, 7, 7)
bam = BAMBlock(channel=512, reduction=16, dia_val=2)
output = bam(input)
print(output.shape)




* 实验结果

无


## 4. （有用）ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks

> 论文地址：https://arxiv.org/pdf/1910.03151.pdf

* 网络结构

  ![image-20211217105517345](https://i-blog.csdnimg.cn/blog_migrate/70c7234711b60959b0aa394f7bdb08a8.png)

* Pytorch代码

  ```python
  import numpy as np
  import torch
  from torch import nn
  from torch.nn import init
  from collections import OrderedDict


  class ECAAttention(nn.Module):

      def __init__(self, kernel_size=3):
          super().__init__()
          self.gap = nn.AdaptiveAvgPool2d(1)
          self.conv = nn.Conv1d(1, 1, kernel_size=kernel_size, padding=(kernel_size - 1) // 2)
          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)

      def forward(self, x):
          y = self.gap(x)  # bs,c,1,1
          y = y.squeeze(-1).permute(0, 2, 1)  # bs,1,c
          y = self.conv(y)  # bs,1,c
          y = self.sigmoid(y)  # bs,1,c
          y = y.permute(0, 2, 1).unsqueeze(-1)  # bs,c,1,1
          return x * y.expand_as(x)


  if __name__ == '__main__':
      input = torch.randn(50, 512, 7, 7)
      eca = ECAAttention(kernel_size=3)
      output = eca(input)
      print(output.shape)

实验结果

2021-12-17 12:18:08,911 - mmdet - INFO - 
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.360
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.545
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.414
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.141
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.489
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.676
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.432
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.432
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.432
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.184
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.576
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.748

5. SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS

论文地址：https://arxiv.org/pdf/2102.00240.pdf

网络结构
Pytorch代码

```python
import numpy as np
import torch
from torch import nn
from torch.nn import init
from torch.nn.parameter import Parameter

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

if name == 'main':
input = torch.randn(50, 512, 7, 7)
se = ShuffleAttention(channel=512, G=8)
output = se(input)
print(output.shape)


* 实验结果

  ```bash
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.350
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.523
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.401
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.123
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.479
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.662
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.424
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.424
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.424
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.160
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.576
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.733

6. Polarized Self-Attention: Towards High-quality Pixel-wise Regression

论文地址：https://arxiv.org/abs/2107.00782

网络结构
Pytorch代码

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

class ParallelPolarizedSelfAttention(nn.Module):

  def __init__(self, channel=512):
      super().__init__()
      self.ch_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
      self.ch_wq = nn.Conv2d(channel, 1, kernel_size=(1, 1))
      self.softmax_channel = nn.Softmax(1)
      self.softmax_spatial = nn.Softmax(-1)
      self.ch_wz = nn.Conv2d(channel // 2, channel, kernel_size=(1, 1))
      self.ln = nn.LayerNorm(channel)
      self.sigmoid = nn.Sigmoid()
      self.sp_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
      self.sp_wq = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
      self.agp = nn.AdaptiveAvgPool2d((1, 1))

  def forward(self, x):
      b, c, h, w = x.size()

      # Channel-only Self-Attention
      channel_wv = self.ch_wv(x)  # bs,c//2,h,w
      channel_wq = self.ch_wq(x)  # bs,1,h,w
      channel_wv = channel_wv.reshape(b, c // 2, -1)  # bs,c//2,h*w
      channel_wq = channel_wq.reshape(b, -1, 1)  # bs,h*w,1
      channel_wq = self.softmax_channel(channel_wq)
      channel_wz = torch.matmul(channel_wv, channel_wq).unsqueeze(-1)  # bs,c//2,1,1
      channel_weight = self.sigmoid(self.ln(self.ch_wz(channel_wz).reshape(b, c, 1).permute(0, 2, 1))).permute(0, 2,
                                                                                                               1).reshape(
          b, c, 1, 1)  # bs,c,1,1
      channel_out = channel_weight * x

      # Spatial-only Self-Attention
      spatial_wv = self.sp_wv(x)  # bs,c//2,h,w
      spatial_wq = self.sp_wq(x)  # bs,c//2,h,w
      spatial_wq = self.agp(spatial_wq)  # bs,c//2,1,1
      spatial_wv = spatial_wv.reshape(b, c // 2, -1)  # bs,c//2,h*w
      spatial_wq = spatial_wq.permute(0, 2, 3, 1).reshape(b, 1, c // 2)  # bs,1,c//2
      spatial_wq = self.softmax_spatial(spatial_wq)
      spatial_wz = torch.matmul(spatial_wq, spatial_wv)  # bs,1,h*w
      spatial_weight = self.sigmoid(spatial_wz.reshape(b, 1, h, w))  # bs,1,h,w
      spatial_out = spatial_weight * x
      out = spatial_out + channel_out
      return out

class SequentialPolarizedSelfAttention(nn.Module):

  def __init__(self, channel=512):
      super().__init__()
      self.ch_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
      self.ch_wq = nn.Conv2d(channel, 1, kernel_size=(1, 1))
      self.softmax_channel = nn.Softmax(1)
      self.softmax_spatial = nn.Softmax(-1)
      self.ch_wz = nn.Conv2d(channel // 2, channel, kernel_size=(1, 1))
      self.ln = nn.LayerNorm(channel)
      self.sigmoid = nn.Sigmoid()
      self.sp_wv = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
      self.sp_wq = nn.Conv2d(channel, channel // 2, kernel_size=(1, 1))
      self.agp = nn.AdaptiveAvgPool2d((1, 1))

  def forward(self, x):
      b, c, h, w = x.size()

      # Channel-only Self-Attention
      channel_wv = self.ch_wv(x)  # bs,c//2,h,w
      channel_wq = self.ch_wq(x)  # bs,1,h,w
      channel_wv = channel_wv.reshape(b, c // 2, -1)  # bs,c//2,h*w
      channel_wq = channel_wq.reshape(b, -1, 1)  # bs,h*w,1
      channel_wq = self.softmax_channel(channel_wq)
      channel_wz = torch.matmul(channel_wv, channel_wq).unsqueeze(-1)  # bs,c//2,1,1
      channel_weight = self.sigmoid(self.ln(self.ch_wz(channel_wz).reshape(b, c, 1).permute(0, 2, 1))).permute(0, 2,
                                                                                                               1).reshape(
          b, c, 1, 1)  # bs,c,1,1
      channel_out = channel_weight * x

      # Spatial-only Self-Attention
      spatial_wv = self.sp_wv(channel_out)  # bs,c//2,h,w
      spatial_wq = self.sp_wq(channel_out)  # bs,c//2,h,w
      spatial_wq = self.agp(spatial_wq)  # bs,c//2,1,1
      spatial_wv = spatial_wv.reshape(b, c // 2, -1)  # bs,c//2,h*w
      spatial_wq = spatial_wq.permute(0, 2, 3, 1).reshape(b, 1, c // 2)  # bs,1,c//2
      spatial_wq = self.softmax_spatial(spatial_wq)
      spatial_wz = torch.matmul(spatial_wq, spatial_wv)  # bs,1,h*w
      spatial_weight = self.sigmoid(spatial_wz.reshape(b, 1, h, w))  # bs,1,h,w
      spatial_out = spatial_weight * channel_out
      return spatial_out

if name == 'main':
input = torch.randn(1, 512, 7, 7)
psa = SequentialPolarizedSelfAttention(channel=512)
output = psa(input)
print(output.shape)


* 实验结果

  ```bash
  2021-12-16 20:30:36,981 - mmdet - INFO - 
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.346
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.522
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.385
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.123
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.474
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.676
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.422
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.422
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.422
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.170
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.570
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.743

7. Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

论文地址：https://arxiv.org/pdf/1905.09646.pdf

网络结构

主要是用在语义分割上，所以在检测上的效果一般，没有带来多少提升
Pytorch代码

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

class SpatialGroupEnhance(nn.Module):

  def __init__(self, groups):
      super().__init__()
      self.groups = groups
      self.avg_pool = nn.AdaptiveAvgPool2d(1)
      self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
      self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))
      self.sig = nn.Sigmoid()
      self.init_weights()

  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, h, w = x.shape
      x = x.view(b * self.groups, -1, h, w)  # bs*g,dim//g,h,w
      xn = x * self.avg_pool(x)  # bs*g,dim//g,h,w
      xn = xn.sum(dim=1, keepdim=True)  # bs*g,1,h,w
      t = xn.view(b * self.groups, -1)  # bs*g,h*w

      t = t - t.mean(dim=1, keepdim=True)  # bs*g,h*w
      std = t.std(dim=1, keepdim=True) + 1e-5
      t = t / std  # bs*g,h*w
      t = t.view(b, self.groups, h, w)  # bs,g,h*w

      t = t * self.weight + self.bias  # bs,g,h*w
      t = t.view(b * self.groups, 1, h, w)  # bs*g,1,h*w
      x = x * self.sig(t)
      x = x.view(b, c, h, w)

      return x

if name == 'main':
input = torch.randn(50, 512, 7, 7)
sge = SpatialGroupEnhance(groups=8)
output = sge(input)
print(output.shape)




* 实验结果

  ```bash
  2021-12-16 21:39:42,785 - mmdet - INFO - 
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.342
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.516
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.381
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.117
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.474
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.652
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.415
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.415
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.415
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.155
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.565
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.718

8. Coordinate Attention for Efficient Mobile Network Design

论文地址：https://arxiv.org/abs/2103.02907

网络结构

主要应用在轻量级网络上，在resnet系列上效果不好。
Pytorch代码

```python
import torch
import torch.nn as nn
import torch.nn.functional as F

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 CoordAtt(nn.Module):
def init(self, inp, oup, reduction=32):
super(CoordAtt, self).init()
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))

      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()
      x_h = self.pool_h(x)
      x_w = self.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


* 实验结果

  ```bash
  2021-12-16 19:04:16,776 - mmdet - INFO - 
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.340
   Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.516
   Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.386
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.127
   Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.457
   Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.632
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.408
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.408
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.408
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.162
   Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.546
   Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.716

9. Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

论文地址： https://arxiv.org/abs/2112.05561

网络结构

计算量特别大，效果一般

Pytorch代码

class GAM_Attention(nn.Module):
    def __init__(self, in_channels, out_channels, rate=4):
        super(GAM_Attention, self).__init__()

        self.channel_attention = nn.Sequential(
            nn.Linear(in_channels, int(in_channels / rate)),
            nn.ReLU(inplace=True),
            nn.Linear(int(in_channels / rate), in_channels)
        )

        self.spatial_attention = nn.Sequential(
            nn.Conv2d(in_channels, int(in_channels / rate), kernel_size=7, padding=3),
            nn.BatchNorm2d(int(in_channels / rate)),
            nn.ReLU(inplace=True),
            nn.Conv2d(int(in_channels / rate), out_channels, kernel_size=7, padding=3),
            nn.BatchNorm2d(out_channels)
        )

    def forward(self, x):
        # print(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()
        out = x * x_spatial_att
        # print(out)

        return out

实验结果

2021-12-16 16:14:20,693 - mmdet - INFO - 
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.350
 Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.530
 Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.399
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.131
 Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.481
 Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.683
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.424
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.424
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.424
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.171
 Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.575
 Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.745

找到我

你可以通过这些方式来寻找我。

B站：肆十二-

CSDN：肆十二

知乎：肆十二

微博：肆十二-

现在关注以后就是老朋友喽！

聊一聊计算机视觉中常用的注意力机制以及Pytorch代码实现

聊一聊计算机视觉中常用的注意力机制以及Pytorch代码实现

1. SeNet: Squeeze-and-Excitation Attention

2. （有用）CBAM: Convolutional Block Attention Module

3. BAM: Bottleneck Attention Module

5. SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS

6. Polarized Self-Attention: Towards High-quality Pixel-wise Regression

7. Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

8. Coordinate Attention for Efficient Mobile Network Design

9. Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

更多注意力

双路注意力机制-DANET

位置注意力-CCNET

找到我

热门文章

最新文章

相关课程

相关电子书

相关实验场景

推荐镜像

热门

活动广场

任务中心

开发者评测

高校计划

乘风者计划

训练营

阿里云MVP

话题

直播

下载

镜像站

技术资料

插件

聊一聊计算机视觉中常用的注意力机制以及Pytorch代码实现

聊一聊计算机视觉中常用的注意力机制以及Pytorch代码实现

1. SeNet: Squeeze-and-Excitation Attention

2. （有用）CBAM: Convolutional Block Attention Module

3. BAM: Bottleneck Attention Module

5. SA-NET: SHUFFLE ATTENTION FOR DEEP CONVOLUTIONAL NEURAL NETWORKS

6. Polarized Self-Attention: Towards High-quality Pixel-wise Regression

7. Spatial Group-wise Enhance: Improving Semantic Feature Learning in Convolutional Networks

8. Coordinate Attention for Efficient Mobile Network Design

9. Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions

更多注意力

双路注意力机制-DANET

位置注意力-CCNET

找到我

热门文章

最新文章

相关课程

相关电子书

相关实验场景

推荐镜像