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聊一聊计算机视觉中常用的注意力机制 附Pytorch代码实现

时间：2023-01-01 11:07:39浏览次数：66  
标签：nn area self IoU Pytorch 聊一聊 视觉 Average channel


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-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。

聊一聊计算机视觉中常用的注意力机制 附Pytorch代码实现_人工智能

  • Pytorch代码
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
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
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
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
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.add_module('conv_%d' % i, nn.Conv2d(kernel_size=3, in_channels=channel // reduction,
                                                        out_channels=channel // reduction, padding=1, dilation=dia_val))
            self.sa.add_module('bn_%d' % i, nn.BatchNorm2d(channel // reduction))
            self.sa.add_module('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
  • Pytorch代码
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
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
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
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
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
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
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
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
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

更多注意力

参考：https://github.com/xmu-xiaoma666/External-Attention-pytorch

另外还有一些用在语义分割上面的结构，这里就不测试了，大家可以自行下去测试

双路注意力机制-DANET

论文标题：Fu_Dual_Attention_Network_for_Scene_Segmentation

论文地址：https://openaccess.thecvf.com/content_CVPR_2019/papers/Fu_Dual_Attention_Network_for_Scene_Segmentation_CVPR_2019_paper.pdf

时间：2019

相当于之前是并行的结构，现在改成了串行的结构然后做特征的concat

聊一聊计算机视觉中常用的注意力机制 附Pytorch代码实现_人工智能_02

聊一聊计算机视觉中常用的注意力机制 附Pytorch代码实现_注意力机制_03

位置注意力-CCNET

在上面的danet上改的，主要是解决计算量的问题, 通过十字交叉的结构来解决

论文标题：CCNet: Criss-Cross Attention for Semantic Segmentation

论文地址：https://openaccess.thecvf.com/content_ICCV_2019/papers/Huang_CCNet_Criss-Cross_Attention_for_Semantic_Segmentation_ICCV_2019_paper.pdf

时：2019

聊一聊计算机视觉中常用的注意力机制 附Pytorch代码实现_深度学习_04

找到我

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标签：nn,area,self,IoU,Pytorch,聊一聊,视觉,Average,channel
From： https://blog.51cto.com/u_15754889/5982778

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