1.Triplet注意力介绍
论文：https://arxiv.org/pdf/2010.03045.pdf
本文提出了可以有效解决跨维度交互的triplet attention。相较于以往的注意力方法，主要有两个优点：
1.可以忽略的计算开销
2.强调了多维交互而不降低维度的重要性，因此消除了通道和权重之间的间接对应。
传统的计算通道注意力的方法为了计算这些通道的权值，输入张量在空间上通过全局平均池化分解为一个像素。这导致了空间信息的大量丢失，因此在单像素通道上计算注意力时，通道维数和空间维数之间的相互依赖性也不存在。后面提出基于Spatial和Channel的CBAM模型缓解了空间相互依赖的问题，但是通道注意和空间注意是分离的，计算是相互独立的。基于建立空间注意力的方法，本文提出了跨维度交互作用(cross dimension interaction)的概念，通过捕捉空间维度和输入张量通道维度之间的交互作用，解决了这一问题。
2.Triplet加入Yolov5
2.1Triple加入common.py
代码语言：javascript复制###################### TripletAttention #### start by AI&CV ###############################
class BasicConv(nn.Module): #https://arxiv.org/pdf/2010.03045.pdf
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, relu=True,
bn=True, bias=False):
super(BasicConv, self).__init__()
self.out_channels = out_planes
self.conv = nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=groups, bias=bias)
self.bn = nn.BatchNorm2d(out_planes, eps=1e-5, momentum=0.01, affine=True) if bn else None
self.relu = nn.ReLU() if relu else None
def forward(self, x):
x = self.conv(x)
if self.bn is not None:
x = self.bn(x)
if self.relu is not None:
x = self.relu(x)
return x
class ZPool(nn.Module):
def forward(self, x):
return torch.cat((torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)), dim=1)
class AttentionGate(nn.Module):
def __init__(self):
super(AttentionGate, self).__init__()
kernel_size = 7
self.compress = ZPool()
self.conv = 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.conv(x_compress)
scale = torch.sigmoid_(x_out)
return x * scale
class TripletAttention(nn.Module):
def __init__(self, no_spatial=False):
super(TripletAttention, self).__init__()
self.cw = AttentionGate()
self.hc = AttentionGate()
self.no_spatial = no_spatial
if not no_spatial:
self.hw = AttentionGate()
def forward(self, x):
x_perm1 = x.permute(0, 2, 1, 3).contiguous()
x_out1 = self.cw(x_perm1)
x_out11 = x_out1.permute(0, 2, 1, 3).contiguous()
x_perm2 = x.permute(0, 3, 2, 1).contiguous()
x_out2 = self.hc(x_perm2)
x_out21 = x_out2.permute(0, 3, 2, 1).contiguous()
if not self.no_spatial:
x_out = self.hw(x)
x_out = 1 / 3 * (x_out x_out11 x_out21)
else:
x_out = 1 / 2 * (x_out11 x_out21)
return x_out
###################### TripletAttention #### END by AI&CV ###############################by CSDN AI小怪兽 https://blog.csdn.net/m0_63774211/article/details/130386790
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