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深度学习Tricks，第一时间送达
目录
（一）前沿介绍
论文题目：《CBAM: Convolutional Block Attention Module》
论文地址： https://arxiv.org/pdf/1807.06521.pdf
CBAM注意力结构基本原理：从上图明显可以看到， CBAM一共包含2个独立的子模块， 通道注意力模块（Channel Attention Module，CAM) 和空间注意力模块（Spartial Attention Module，SAM) ，分别进行通道与空间维度上的注意力特征融合。 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。实验证明，将CBAM注意力模块嵌入到YOLOv7网络中，有利于解决原始网络无注意力偏好的问题。
（二）YOLOv5/YOLOv7改进之结合CBAM
改进方法基本和YOLOv5一样，分三步走：
1.配置common.py文件
加入CBAM代码
-
class
ChannelAttention(nn.Module):
-
def
__init__(
self, in_planes, ratio=16):
-
super(ChannelAttention, self).__init__()
-
self.avg_pool = nn.AdaptiveAvgPool2d(
1)
-
self.max_pool = nn.AdaptiveMaxPool2d(
1)
-
self.f1 = nn.Conv2d(in_planes, in_planes // ratio,
1, bias=
False)
-
self.relu = nn.ReLU()
-
self.f2 = nn.Conv2d(in_planes // ratio, in_planes,
1, bias=
False)
-
self.sigmoid = nn.Sigmoid()
-
-
def
forward(
self, x):
-
avg_out = self.f2(self.relu(self.f1(self.avg_pool(x))))
-
max_out = self.f2(self.relu(self.f1(self.max_pool(x))))
-
out = self.sigmoid(avg_out + max_out)
-
return out
-
-
-
class
SpatialAttention(nn.Module):
-
def
__init__(
self, kernel_size=7):
-
super(SpatialAttention, self).__init__()
-
assert kernel_size
in (
3,
7),
'kernel size must be 3 or 7'
-
padding =
3
if kernel_size ==
7
else
1
-
self.conv = nn.Conv2d(
2,
1, kernel_size, padding=padding, bias=
False)
-
self.sigmoid = nn.Sigmoid()
-
-
def
forward(
self, x):
-
avg_out = torch.mean(x, dim=
1, keepdim=
True)
-
max_out, _ = torch.
max(x, dim=
1, keepdim=
True)
-
x = torch.cat([avg_out, max_out], dim=
1)
-
x = self.conv(x)
-
return self.sigmoid(x)
-
-
-
class
CBAM(nn.Module):
-
# Standard convolution
-
def
__init__(
self, c1, c2, k=1, s=1, p=None, g=1, act=True):
# ch_in, ch_out, kernel, stride, padding, groups
-
super(CBAM, self).__init__()
-
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=
False)
-
self.bn = nn.BatchNorm2d(c2)
-
self.act = nn.Hardswish()
if act
else nn.Identity()
-
self.ca = ChannelAttention(c2)
-
self.sa = SpatialAttention()
2.配置yolo.py文件
加入CBAM模块
3.配置yolov7_CBAM.yaml文件
添加方法灵活多变，Backbone或者Neck都可，例子如下：
-
#
anchors
-
anchors:
-
-
[12,16, 19,36, 40,28] #
P3/
8
-
-
[36,75, 76,55, 72,146] #
P4/
16
-
-
[142,110, 192,243, 459,401] #
P5/
32
-
-
#
yolov7
backbone
-
backbone:
-
#
[from, number, module, args]
-
[[-1, 1, Conv, [32, 3, 1]], #
0
-
-
[-1, 1, CBAM, [64, 3, 2]], #
1
-P1/
2
-
[-1, 1, Conv, [64, 3, 1]],
-
-
[-1, 1, CBAM, [128, 3, 2]], #
3
-P2/
4
-
[-1, 1, Conv, [64, 1, 1]],
-
[-2, 1, Conv, [64, 1, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[[-1, -3, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [256, 1, 1]], #
11
-
-
[-1, 1, MP, []],
-
[-1, 1, Conv, [128, 1, 1]],
-
[-3, 1, Conv, [128, 1, 1]],
-
[-1, 1, Conv, [128, 3, 2]],
-
[[-1, -3],
1,
Concat,
[1]], #
16
-P3/
8
-
[-1, 1, Conv, [128, 1, 1]],
-
[-2, 1, Conv, [128, 1, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[[-1, -3, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [512, 1, 1]], #
24
-
-
[-1, 1, MP, []],
-
[-1, 1, Conv, [256, 1, 1]],
-
[-3, 1, Conv, [256, 1, 1]],
-
[-1, 1, Conv, [256, 3, 2]],
-
[[-1, -3],
1,
Concat,
[1]], #
29
-P4/
16
-
[-1, 1, Conv, [256, 1, 1]],
-
[-2, 1, Conv, [256, 1, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[[-1, -3, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [1024, 1, 1]], #
37
-
-
[-1, 1, MP, []],
-
[-1, 1, Conv, [512, 1, 1]],
-
[-3, 1, Conv, [512, 1, 1]],
-
[-1, 1, Conv, [512, 3, 2]],
-
[[-1, -3],
1,
Concat,
[1]], #
42
-P5/
32
-
[-1, 1, Conv, [256, 1, 1]],
-
[-2, 1, Conv, [256, 1, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[[-1, -3, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [1024, 1, 1]], #
50
-
]
-
-
#
yolov7
head
-
head:
-
[[-1, 1, SPPCSPC, [512]], #
51
-
-
[-1, 1, Conv, [256, 1, 1]],
-
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
-
[37, 1, Conv, [256, 1, 1]], #
route
backbone
P4
-
[[-1, -2],
1,
Concat,
[1]],
-
-
[-1, 1, Conv, [256, 1, 1]],
-
[-2, 1, Conv, [256, 1, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[[-1, -2, -3, -4, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [256, 1, 1]], #
63
-
-
[-1, 1, Conv, [128, 1, 1]],
-
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
-
[24, 1, Conv, [128, 1, 1]], #
route
backbone
P3
-
[[-1, -2],
1,
Concat,
[1]],
-
-
[-1, 1, Conv, [128, 1, 1]],
-
[-2, 1, Conv, [128, 1, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[-1, 1, Conv, [64, 3, 1]],
-
[[-1, -2, -3, -4, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [128, 1, 1]], #
75
-
-
[-1, 1, MP, []],
-
[-1, 1, Conv, [128, 1, 1]],
-
[-3, 1, Conv, [128, 1, 1]],
-
[-1, 1, Conv, [128, 3, 2]],
-
[[-1, -3, 63],
1,
Concat,
[1]],
-
-
[-1, 1, Conv, [256, 1, 1]],
-
[-2, 1, Conv, [256, 1, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[-1, 1, Conv, [128, 3, 1]],
-
[[-1, -2, -3, -4, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [256, 1, 1]], #
88
-
-
[-1, 1, MP, []],
-
[-1, 1, Conv, [256, 1, 1]],
-
[-3, 1, Conv, [256, 1, 1]],
-
[-1, 1, Conv, [256, 3, 2]],
-
[[-1, -3, 51],
1,
Concat,
[1]],
-
-
[-1, 1, Conv, [512, 1, 1]],
-
[-2, 1, Conv, [512, 1, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[-1, 1, Conv, [256, 3, 1]],
-
[[-1, -2, -3, -4, -5, -6],
1,
Concat,
[1]],
-
[-1, 1, Conv, [512, 1, 1]], #
101
-
-
[75, 1, RepConv, [256, 3, 1]],
-
[88, 1, RepConv, [512, 3, 1]],
-
[101, 1, RepConv, [1024, 3, 1]],
-
-
[[102,103,104],
1,
Detect,
[nc, anchors]], #
Detect(P3, P4, P5)
-
]
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转载：https://blog.csdn.net/m0_53578855/article/details/127585358
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