涨点技巧：注意力机制

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涨点技巧：注意力机制

2023-03-24 09:56| 来源: 网络整理| 查看: 265

1.计算机视觉中的注意力机制

一般来说，注意力机制通常被分为以下基本四大类：

通道注意力 Channel Attention

空间注意力机制 Spatial Attention

时间注意力机制 Temporal Attention

分支注意力机制 Branch Attention

1.1.CBAM：通道注意力和空间注意力的集成者

轻量级的卷积注意力模块，它结合了通道和空间的注意力机制模块

论文题目：《CBAM: Convolutional Block Attention Module》论文地址： https://arxiv.org/pdf/1807.06521.pdf

上图可以看到，CBAM包含CAM（Channel Attention Module）和SAM（Spartial Attention Module）两个子模块，分别进行通道和空间上的Attention。这样不只能够节约参数和计算力，并且保证了其能够做为即插即用的模块集成到现有的网络架构中去。

1.2 GAM：Global Attention Mechanism

超越CBAM，全新注意力GAM：不计成本提高精度！论文题目：Global Attention Mechanism: Retain Information to Enhance Channel-Spatial Interactions 论文地址：https://paperswithcode.com/paper/global-attention-mechanism-retain-information

从整体上可以看出，GAM和CBAM注意力机制还是比较相似的，同样是使用了通道注意力机制和空间注意力机制。但是不同的是对通道注意力和空间注意力的处理。

1.3 ResBlock_CBAM

CBAM结构其实就是将通道注意力信息核空间注意力信息在一个block结构中进行运用。

在resnet中实现cbam：即在原始block和残差结构连接前，依次通过channel attention和spatial attention即可。

1.4性能评价

2.Yolov5加入CBAM、GAM 2.1 CBAM加入common.py中 class ChannelAttentionModule(nn.Module): def __init__(self, c1, reduction=16,light=False): super(ChannelAttentionModule, self).__init__() mid_channel = c1 // reduction self.light=light self.avg_pool = nn.AdaptiveAvgPool2d(1) if self.light: self.max_pool = nn.AdaptiveMaxPool2d(1) self.shared_MLP = nn.Sequential( nn.Linear(in_features=c1, out_features=mid_channel), nn.LeakyReLU(0.1, inplace=True), nn.Linear(in_features=mid_channel, out_features=c1) ) else: self.shared_MLP = nn.Conv2d(c1, c1, 1, 1, 0, bias=True) self.act = nn.Sigmoid() def forward(self, x): if self.light: avgout = self.shared_MLP(self.avg_pool(x).view(x.size(0),-1)).unsqueeze(2).unsqueeze(3) maxout = self.shared_MLP(self.max_pool(x).view(x.size(0),-1)).unsqueeze(2).unsqueeze(3) fc_out=(avgout + maxout) else: fc_out=(self.shared_MLP(self.avg_pool(x))) return x * self.act(fc_out) class SpatialAttentionModule(nn.Module): ##update:coding-style FOR LIGHTING def __init__(self, kernel_size=7): super().__init__() assert kernel_size in (3, 7), 'kernel size must be 3 or 7' padding = 3 if kernel_size == 7 else 1 self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False) self.act = nn.Sigmoid() def forward(self, x): return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1))) class CBAM(nn.Module): def __init__(self, c1,c2,k=7): super().__init__() self.channel_attention = ChannelAttentionModule(c1) self.spatial_attention = SpatialAttentionModule(k) def forward(self, x): return self.spatial_attention(self.channel_attention(x)) 2.2 GAM加入common.py中 def channel_shuffle(x, groups=2): ##shuffle channel #RESHAPE----->transpose------->Flatten B, C, H, W = x.size() out = x.view(B, groups, C // groups, H, W).permute(0, 2, 1, 3, 4).contiguous() out=out.view(B, C, H, W) return out class GAM_Attention(nn.Module): #https://paperswithcode.com/paper/global-attention-mechanism-retain-information def __init__(self, c1, c2, group=True,rate=4): super(GAM_Attention, self).__init__() self.channel_attention = nn.Sequential( nn.Linear(c1, int(c1 / rate)), nn.ReLU(inplace=True), nn.Linear(int(c1 / rate), c1) ) self.spatial_attention = nn.Sequential( nn.Conv2d(c1, c1//rate, kernel_size=7, padding=3,groups=rate)if group else nn.Conv2d(c1, int(c1 / rate), kernel_size=7, padding=3), nn.BatchNorm2d(int(c1 /rate)), nn.ReLU(inplace=True), nn.Conv2d(c1//rate, c2, kernel_size=7, padding=3,groups=rate) if group else nn.Conv2d(int(c1 / rate), c2, kernel_size=7, padding=3), nn.BatchNorm2d(c2) ) def forward(self, x): b, c, h, w = x.shape x_permute = x.permute(0, 2, 3, 1).view(b, -1, c) x_att_permute = self.channel_attention(x_permute).view(b, h, w, c) x_channel_att = x_att_permute.permute(0, 3, 1, 2) # x_channel_att=channel_shuffle(x_channel_att,4) #last shuffle x = x * x_channel_att x_spatial_att = self.spatial_attention(x).sigmoid() x_spatial_att=channel_shuffle(x_spatial_att,4) #last shuffle out = x * x_spatial_att #out=channel_shuffle(out,4) #last shuffle return out

2.4 GAM加入common.py中加入common.py中

class ResBlock_CBAM(nn.Module): def __init__(self, in_places, places, stride=1, downsampling=False, expansion=4): super(ResBlock_CBAM, self).__init__() self.expansion = expansion self.downsampling = downsampling self.bottleneck = nn.Sequential( nn.Conv2d(in_channels=in_places, out_channels=places, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(places), nn.LeakyReLU(0.1, inplace=True), nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False), nn.BatchNorm2d(places), nn.LeakyReLU(0.1, inplace=True), nn.Conv2d(in_channels=places, out_channels=places * self.expansion, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(places * self.expansion), ) self.cbam = CBAM(c1=places * self.expansion, c2=places * self.expansion, ) if self.downsampling: self.downsample = nn.Sequential( nn.Conv2d(in_channels=in_places, out_channels=places * self.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(places * self.expansion) ) self.relu = nn.ReLU(inplace=True) def forward(self, x): residual = x out = self.bottleneck(x) out = self.cbam(out) if self.downsampling: residual = self.downsample(x) out += residual out = self.relu(out) return out 2.3 CBAM、GAM加入yolo.py中 if m in { Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x, C2f,CBAM,ResBlock_CBAM,GAM_Attention}: 2.4 CBAM、GAM修改对应yaml 2.4.1 修改 yolov5s_cbam.yaml # parameters nc: 10 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: #- [5,6, 7,9, 12,10] # P2/4 - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # [c=channels,module,kernlsize,strides] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [c=3,64*0.5=32,3] [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, CBAM, [1024,7]], #9 [-1, 1, SPPF, [1024,5]], #10 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 14 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 18 (P3/8-small) [-1, 1, CBAM, [256,7]], #19 [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 22 (P4/16-medium) [256, 256, 1, False] [-1, 1, CBAM, [512,7]], [-1, 1, Conv, [512, 3, 2]], #[256, 256, 3, 2] [[-1, 11], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 25 (P5/32-large) [512, 512, 1, False] [-1, 1, CBAM, [1024,7]], [[19, 23, 27], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ] 2.4.2 修改 yolov5s_gam.yaml # parameters nc: 1 # number of classes depth_multiple: 0.33 # model depth multiple width_multiple: 0.50 # layer channel multiple # anchors anchors: #- [5,6, 7,9, 12,10] # P2/4 - [10,13, 16,30, 33,23] # P3/8 - [30,61, 62,45, 59,119] # P4/16 - [116,90, 156,198, 373,326] # P5/32 # YOLOv5 backbone backbone: # [from, number, module, args] # [c=channels,module,kernlsize,strides] [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 [c=3,64*0.5=32,3] [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [-1, 3, C3, [128]], [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [-1, 6, C3, [256]], [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [-1, 9, C3, [512]], [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [-1, 3, C3, [1024]], [-1, 1, GAM_Attention, [1024,1024]], #9 [-1, 1, SPPF, [1024,5]], #10 ] # YOLOv5 head head: [[-1, 1, Conv, [512, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 6], 1, Concat, [1]], # cat backbone P4 [-1, 3, C3, [512, False]], # 14 [-1, 1, Conv, [256, 1, 1]], [-1, 1, nn.Upsample, [None, 2, 'nearest']], [[-1, 4], 1, Concat, [1]], # cat backbone P3 [-1, 3, C3, [256, False]], # 18 (P3/8-small) [-1, 1, GAM_Attention, [256,256]], #19 [-1, 1, Conv, [256, 3, 2]], [[-1, 15], 1, Concat, [1]], # cat head P4 [-1, 3, C3, [512, False]], # 22 (P4/16-medium) [256, 256, 1, False] [-1, 1, GAM_Attention, [512,512]], [-1, 1, Conv, [512, 3, 2]], #[256, 256, 3, 2] [[-1, 11], 1, Concat, [1]], # cat head P5 [-1, 3, C3, [1024, False]], # 25 (P5/32-large) [512, 512, 1, False] [-1, 1, GAM_Attention, [1024,1024]], # [[19, 23, 27], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) ]

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涨点技巧：注意力机制

涨点技巧：注意力机制

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