yolov8新版注意力机制

### YOLOv8 新版 注意力机制 介绍 YOLOv8 是一种先进的实时对象检测模型，在其最新版本中引入了多种注意力机制来提升特征提取的效果。通过加入不同的注意力模块，可以显著提高模型对于复杂场景下的目标识别精度。 #### SEAttention (Squeeze-and-Excitation Attention) SEAttention是一种通道级别的注意力建模方法，它通过对不同卷积层输出的特征图施加权重调整，使得重要的特征得到强化而不重要的特征受到抑制。具体来说，该机制先计算全局平均池化后的向量表示，再经过两个全连接层映射成比例因子作用于原始输入上[^1]。 ```python import torch.nn as nn 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 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) ``` #### CBAM (Convolutional Block Attention Module) CBAM不仅考虑了空间维度上的重要性分布也关注到了各个channel之间的关联程度。此模块由两部分组成：Channel attention module 和 Spatial attention module 。前者负责捕捉跨channels的信息交互；后者则用于感知图像内局部区域的重要性差异[^2]。 ```python from functools import partial def conv(in_planes, out_planes, kernel_size=3, stride=1, padding=1, dilation=1, freeze_bn=False): if freeze_bn: return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), FrozenBatchNorm2d(out_planes), nn.ReLU(inplace=True)) else: return nn.Sequential( nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding, dilation=dilation, bias=True), nn.BatchNorm2d(out_planes), nn.ReLU(inplace=True)) class ChannelGate(nn.Module): def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max']): super(ChannelGate, self).__init__() self.gate_channels = gate_channels self.mlp = nn.Sequential( Flatten(), nn.Linear(gate_channels, gate_channels // reduction_ratio), nn.ReLU(), nn.Linear(gate_channels // reduction_ratio, gate_channels) ) self.pool_types = pool_types def forward(self, x): channel_att_sum = None for pool_type in self.pool_types: if pool_type=='avg': avg_pool = F.avg_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) channel_att_raw = self.mlp(avg_pool) elif pool_type=='max': max_pool = F.max_pool2d( x, (x.size(2), x.size(3)), stride=(x.size(2), x.size(3))) channel_att_raw = self.mlp(max_pool) if channel_att_sum is None: channel_att_sum = channel_att_raw else: channel_att_sum = channel_att_sum + channel_att_raw scale = torch.sigmoid( channel_att_sum ).unsqueeze(2).unsqueeze(3).expand_as(x) return x * scale class SpatialGate(nn.Module): def __init__(self): super(SpatialGate, self).__init__() kernel_size = 7 self.compress = ChannelPool() self.spatial = BasicConv(2, 1, kernel_size, stride=1, padding=(kernel_size-1) // 2, relu=False) def forward(self, x): x_compress = self.compress(x) x_out = self.spatial(x_compress) scale = torch.sigmoid(x_out) # broadcasting return x * scale class CBAM(nn.Module): def __init__(self, gate_channels, reduction_ratio=16, pool_types=['avg', 'max'], no_spatial=False): super(CBAM, self).__init__() self.ChannelGate = ChannelGate(gate_channels, reduction_ratio, pool_types) self.no_spatial=no_spatial if not no_spatial: self.SpatialGate = SpatialGate() def forward(self, x): x_out = self.ChannelGate(x) if not self.no_spatial: x_out = self.SpatialGate(x_out) return x_out ```