YOLOV8改进：如何增加注意力模块？（以CBAM模块为例）

前言

因为毕设用到了YOLO，鉴于最近V8刚出，因此考虑将注意力机制加入到v8中。

YOLOV8

代码地址：YOLOV8官方代码

使用pip安装或者clone到本地，在此不多赘述了。下面以使用pip安装ultralytics包为例介绍。
进入ultralytics文件夹

nn文件夹

再进入nn文件夹。

-- modules.py：在里面存放着各种常用的模块，如：Conv，DWConv，ConvTranspose，TransformerLayer，Bottleneck等
-- tasks.py: 在里面导入了modules中的基本模块组建model，根据不同的下游任务组建不同的model。

modules.py

在该文件中，我们可以写入自己的注意力模块，或者使用V8已经提供的CBAM模块（见代码的CBAM类）

"""
通道注意力模型: 通道维度不变，压缩空间维度。该模块关注输入图片中有意义的信息。
1）假设输入的数据大小是(b,c,w,h)
2）通过自适应平均池化使得输出的大小变为(b,c,1,1)
3）通过2d卷积和sigmod激活函数后，大小是(b,c,1,1)
4）将上一步输出的结果和输入的数据相乘，输出数据大小是(b,c,w,h)。
"""
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:
        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:
        return x * self.act(self.fc(self.pool(x)))

"""
空间注意力模块：空间维度不变，压缩通道维度。该模块关注的是目标的位置信息。
1） 假设输入的数据x是(b,c,w,h)，并进行两路处理。
2）其中一路在通道维度上进行求平均值，得到的大小是(b,1,w,h)；另外一路也在通道维度上进行求最大值，得到的大小是(b,1,w,h)。
3） 然后对上述步骤的两路输出进行连接，输出的大小是(b,2,w,h)
4）经过一个二维卷积网络，把输出通道变为1，输出大小是(b,1,w,h)
4）将上一步输出的结果和输入的数据x相乘，最终输出数据大小是(b,c,w,h)。
"""
class SpatialAttention(nn.Module):
    # Spatial-attention module
    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):
    # Convolutional Block Attention Module
    def __init__(self, c1, kernel_size=7):  # ch_in, kernels
        super().__init__()
        self.channel_attention = ChannelAttention(c1)
        self.spatial_attention = SpatialAttention(kernel_size)
        
        

    def forward(self, x):
        return self.spatial_attention(self.channel_attention(x))

如果使用V8的CBAM模块，则不需要更改modules.py的内容。如果使用自己的注意力模块，只需要在该文件后面添加对应的代码即可。

task.py

在该文件中，通过import modules.py文件中的模块来构建模型。
在文件开头导入需要的模块，可以看到modules中的很多模块在v8中并没有用到。我们在最后添加对应的CBAM模块。

from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
                                    Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
                                    GhostBottleneck, GhostConv, Segment, CBAM)

之后修改对应的parse_model方法（对应428行）
添加分支elif m is CBAM:，具体代码如下：

def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)
    # Parse a YOLO model.yaml dictionary
    if verbose:
        LOGGER.info(f"\n{'':>3}{'from':>20}{'n':>3}{'params':>10}  {'module':<45}{'arguments':<30}")
    nc, gd, gw, act = d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
    if act:
        Conv.default_act = eval(act)  # redefine default activation, i.e. Conv.default_act = nn.SiLU()
        if verbose:
            LOGGER.info(f"{colorstr('activation:')} {act}")  # print
    ch = [ch]
    layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
        m = eval(m) if isinstance(m, str) else m  # eval strings
        for j, a in enumerate(args):
            # TODO: re-implement with eval() removal if possible
            # args[j] = (locals()[a] if a in locals() else ast.literal_eval(a)) if isinstance(a, str) else a
            with contextlib.suppress(NameError):
                args[j] = eval(a) if isinstance(a, str) else a  # eval strings

        n = n_ = max(round(n * gd), 1) if n > 1 else n  # depth gain
        if m in (Classify, Conv, ConvTranspose, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, Focus,
                 BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x):
            c1, c2 = ch[f], args[0]
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(c2 * gw, 8)

            args = [c1, c2, *args[1:]]
            if m in (BottleneckCSP, C1, C2, C2f, C3, C3TR, C3Ghost, C3x):
                args.insert(2, n)  # number of repeats
                n = 1
        elif m is nn.BatchNorm2d:
            args = [ch[f]]
        elif m is Concat:
            c2 = sum(ch[x] for x in f)
        elif m in (Detect, Segment):
            args.append([ch[x] for x in f])
            if m is Segment:
                args[2] = make_divisible(args[2] * gw, 8)
        elif m is CBAM:
            """
            ch[f]:上一层的
            args[0]:第0个参数
            c1:输入通道数
            c2:输出通道数
            """
            c1, c2 = ch[f], args[0]
            # print("ch[f]:",ch[f])
            # print("args[0]:",args[0])
            # print("args:",args)
            # print("c1:",c1)
            # print("c2:",c2)
            if c2 != nc:  # if c2 not equal to number of classes (i.e. for Classify() output)
                c2 = make_divisible(c2 * gw, 8)
            args = [c1,*args[1:]]
        else:
            c2 = ch[f]
        
        m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args)  # module
        t = str(m)[8:-2].replace('__main__.', '')  # module type
        m.np = sum(x.numel() for x in m_.parameters())  # number params
        m_.i, m_.f, m_.type = i, f, t  # attach index, 'from' index, type
        if verbose:
            LOGGER.info(f'{i:>3}{str(f):>20}{n_:>3}{m.np:10.0f}  {t:<45}{str(args):<30}')  # print
        save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
        layers.append(m_)
        if i == 0:
            ch = []
        ch.append(c2)
    return nn.Sequential(*layers), sorted(save)

注意传入的参数为上一层输出，要注意CBAM模块的参数和传入参数的对应。读者可以自行print比较。

models文件夹

返回上一级目录，进入models文件夹。
可以看到该文件夹中还有v5、v3对应的模型配置文件，所以也可以使用该包进行v5和v3的训练。
进入v8文件夹

打开对应的yolov8.yaml，如下所示。该文件是V8对应的配置文件，里面包括了类别数，模型大小（n,s,m,l,x），backbone和head。

# Ultralytics YOLO , GPL-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 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, 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, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [1024]]  # 21 (P5/32-large)

  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

我们复制一份，以yolov8x为例，并改名为myyolo.yaml

# Ultralytics YOLO , GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 1.00  # scales module repeats
width_multiple: 1.25  # scales convolution channels

# YOLOv8.0x 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, 3, CBAM, [128,7]]
  - [-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, [512, 3, 2]]  # 7-P5/32
  - [-1, 3, C2f, [512, True]]
  - [-1, 1, SPPF, [512, 5]]  # 9
  - [-1, 3, CBAM, [512,7]]

# YOLOv8.0x 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, 12], 1, Concat, [1]]  # cat head P4
  - [-1, 3, C2f, [512]]  # 18 (P4/16-medium)

  - [-1, 1, Conv, [512, 3, 2]]
  - [[-1, 9], 1, Concat, [1]]  # cat head P5
  - [-1, 3, C2f, [512]]  # 21 (P5/32-large)

  - [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

我们在SPPF模块后添加一层CBAM模块，参数为[512,7]，7为SpatialAttention对应的卷积核大小，值可为3或7，其他会报错。
添加完后使用对应的yaml配置文件训练即可。

yolo task=detect mode=train model=myyolo.yaml data=datasets/data/MOT20Det/VOC2007/mot20.yaml batch=32 epochs=80 imgsz=640 workers=16 device=\'0,1,2,3\'

值得注意的是，如果添加了多层CBAM模块，可能会导致各个模块对应的层数改变，因此需要同时修改head中各个layer from对应的层数。

初始YOLOV8X默认的层数如下

# 默认
#   0                  -1  1      2320  ultralytics.nn.modules.Conv                  [3, 80, 3, 2]                 
#   1                  -1  1    115520  ultralytics.nn.modules.Conv                  [80, 160, 3, 2]               
#   2                  -1  3    436800  ultralytics.nn.modules.C2f                   [160, 160, 3, True]           
#   3                  -1  1    461440  ultralytics.nn.modules.Conv                  [160, 320, 3, 2]              
#   4                  -1  6   3281920  ultralytics.nn.modules.C2f                   [320, 320, 6, True]           
#   5                  -1  1   1844480  ultralytics.nn.modules.Conv                  [320, 640, 3, 2]              
#   6                  -1  6  13117440  ultralytics.nn.modules.C2f                   [640, 640, 6, True]           
#   7                  -1  1   3687680  ultralytics.nn.modules.Conv                  [640, 640, 3, 2]              
#   8                  -1  3   6969600  ultralytics.nn.modules.C2f                   [640, 640, 3, True]           
#   9                  -1  1   1025920  ultralytics.nn.modules.SPPF                  [640, 640, 5]                 
#  10                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
#  11             [-1, 6]  1         0  ultralytics.nn.modules.Concat                [1]                           
#  12                  -1  3   7379200  ultralytics.nn.modules.C2f                   [1280, 640, 3]                
#  13                  -1  1         0  torch.nn.modules.upsampling.Upsample         [None, 2, 'nearest']          
#  14             [-1, 4]  1         0  ultralytics.nn.modules.Concat                [1]                           
#  15                  -1  3   1948800  ultralytics.nn.modules.C2f                   [960, 320, 3]                 
#  16                  -1  1    922240  ultralytics.nn.modules.Conv                  [320, 320, 3, 2]              
#  17            [-1, 12]  1         0  ultralytics.nn.modules.Concat                [1]                           
#  18                  -1  3   7174400  ultralytics.nn.modules.C2f                   [960, 640, 3]                 
#  19                  -1  1   3687680  ultralytics.nn.modules.Conv                  [640, 640, 3, 2]              
#  20             [-1, 9]  1         0  ultralytics.nn.modules.Concat                [1]                           
#  21                  -1  3   7379200  ultralytics.nn.modules.C2f                   [1280, 640, 3]                
#  22        [15, 18, 21]  1   8795008  ultralytics.nn.modules.Detect                [80, [320, 640, 640]] 

增加对应的模块后，之后的层数的layer+1，因此需要适当更改，不然会报concat维度不匹配的错误，如下

RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 16 but got size 32 for tensor number 1 in the list.

总结

添加注意力模块只需要3步
1、在对应的modules.py中添加需要的模块
2、在task.py中引入modules.py中的模块，并进行适当的参数匹配
3、修改对应的models文件夹中的yaml文件，并注意层数问题。
之后就可以进行正常训练了