一、本文介绍
作为入门性第一篇,这里介绍了CBAM注意力在YOLOv8中的使用。包含CBAM原理分析,CBAM的代码、CBAM的使用方法、以及添加以后的yaml文件及运行记录。
二、CBAM原理分析
CBAM官方论文地址:CBAM论文
CBAM的pytorch版代码:CBAM的pytorch版代码
CBAM:卷积块注意力模块,由通道注意力和空间注意力组成。其中通道注意力机制主要检测目标的内容信息,空间注意力主要检测目标位置信息。模块先应用通道注意力,再利用空间注意力;其原理结构如下图所示。
相关代码:
在YOLOv8中,作者已经集成了cbam注意力的代码,仅未应用。
class ChannelAttention(nn.Module):
"""Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
def __init__(self, channels: int) -> None:
"""Initializes the class and sets the basic configurations and instance variables required."""
super().__init__()
self.pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)
self.act = nn.Sigmoid()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Applies forward pass using activation on convolutions of the input, optionally using batch normalization."""
return x * self.act(self.fc(self.pool(x)))
class SpatialAttention(nn.Module):
"""Spatial-attention module."""
def __init__(self, kernel_size=7):
"""Initialize Spatial-attention module with kernel size argument."""
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):
"""Apply channel and spatial attention on input for feature recalibration."""
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):
"""Convolutional Block Attention Module."""
def __init__(self, c1, kernel_size=7):
"""Initialize CBAM with given input channel (c1) and kernel size."""
super().__init__()
self.channel_attention = ChannelAttention(c1)
self.spatial_attention = SpatialAttention(kernel_size)
def forward(self, x):
"""Applies the forward pass through C1 module."""
return self.spatial_attention(self.channel_attention(x))
四、YOLOv8中CBAM使用方法
YOLOv8中CBAM模块,作者存于ultralytics/nn/modules/conv.py中。
我们使用CBAM模块,仅需在ultralytics/nn/tasks.py进行CBAM注意力机制的注册,以及在YOLOv8的yaml配置文件中添加CBAM即可。
首先打开task.py文件,按住Ctrl+F,输入parse_model进行搜索。找到parse_model函数。在其最后一个else前面添加以下注册代码:
elif m in {CBAM,MHSA,SEAttention,ECA,ShuffleAttention,ECA_SA,SE_SA,SA_ECA,SA_ShuffleAttention,CBAM_base,PECA_SA,CPAM, CPAM_SA, MSCA_SA, SKAttention, DoubleAttention,PCPAM_SA,SA_CPAM,CoordAtt}:#自己加的注意力模块
c1, c2 = ch[f], args[0]
if c2 != nc:
c2 = make_divisible(min(c2, max_channels) * width, 8)
args = [c1, *args[1:]]
然后,就是新建一个名为YOLOv8_CBAM.yaml的配置文件:(路径:ultralytics/cfg/models/v8/YOLOv8_CBAM.yaml)
# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call CPAM-yolov8.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024] # YOLOv8n summary: 225 layers, 3157200 parameters, 3157184 gradients, 8.9 GFLOPs
s: [0.33, 0.50, 1024] # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients, 28.8 GFLOPs
m: [0.67, 0.75, 768] # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients, 79.3 GFLOPs
l: [1.00, 1.00, 512] # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPs
x: [1.00, 1.25, 512] # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs
# YOLOv8.0n backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, CBAM, [1024,7]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv8.0n head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 13], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 18 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 10], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 21 (P5/32-large)
- [[16, 19, 22], 1, Detect, [nc]] # Detect(P3, P4, P5)
其中参数中nc,由自己的数据集决定。本文测试,采用的coco8数据集,有80个类别。
在根目录新建一个train.py文件,内容如下
from ultralytics import YOLO
# 加载一个模型
model = YOLO('ultralytics/cfg/models/v8/YOLOv8_CBAM.yaml') # 从YAML建立一个新模型
# 训练模型
results = model.train(data='ultralytics/cfg/datasets/coco8.yaml', epochs=1,imgsz=640,optimizer="SGD")
训练输出:
五、总结
以上就是CBAM的原理及使用方式,但具体CBAM注意力机制的具体位置放哪里,效果更好。需要根据不同的数据集做相应的实验验证。希望本文能够帮助你入门YOLO中注意力机制的使用。