1.BAM介绍

论文：https://arxiv.org/pdf/1807.06514.pdf

摘要：提出了一种简单有效的注意力模块，称为瓶颈注意力模块（BAM），可以与任何前馈卷积神经网络集成。我们的模块沿着两条独立的路径，通道和空间，推断出一张注意力图。我们将我们的模块放置在模型的每个瓶颈处，在那里会发生特征图的下采样。我们的模块用许多参数在瓶颈处构建了分层注意力，并且它可以以端到端的方式与任何前馈模型联合训练。我们通过在CIFAR-100、ImageNet-1K、VOC 2007和MS COCO基准上进行大量实验来验证我们的BAM。我们的实验表明，各种模型在分类和检测性能上都有持续的改进，证明了BAM的广泛适用性。

作者将BAM放在了Resnet网络中每个stage之间。有趣的是，通过可视化我们可以看到多层BAMs形成了一个分层的注意力机制，这有点像人类的感知机制。BAM在每个stage之间消除了像背景语义特征这样的低层次特征，然后逐渐聚焦于高级的语义–明确的目标。

作者提出了新的Attention模型——瓶颈注意模块，通过分离的两个路径channel和spatial得到attention map，减少计算开销和参数开销。

实验

BAM可以在大规模数据集中的各种模型上有很好的泛化能力，同时参数和计算的开销可以忽略不计，这表明提出的模块BAM可以有效地提高网络容量。另一个值得注意的是，改进的性能来自于只在网络中放置三个模块。

BAM提高了所有具有两个骨干网络的强大基线的准确性.BAM的准确率提高是以可忽略不计的参数开销实现的，这表明提高不是由于天真的容量增加，而是由于我们有效的特征细化。

2.BAM引入到yolov8

2.1 加入`modules.py`中：

###################### BAM  attention  ####     START   by  AI&CV  ###############################

import torch
from torch import nn
import torch.nn.functional as F


class ChannelGate(nn.Module):
    def __init__(self, channel, reduction=16):
        super().__init__()
        self.avgpool = nn.AdaptiveAvgPool2d(1)
        self.mlp = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel)
        )
        self.bn = nn.BatchNorm1d(channel)

    def forward(self, x):
        b, c, h, w = x.shape
        y = self.avgpool(x).view(b, c)
        y = self.mlp(y)
        y = self.bn(y).view(b, c, 1, 1)
        return y.expand_as(x)


class SpatialGate(nn.Module):
    def __init__(self, channel, reduction=16, kernel_size=3, dilation_val=4):
        super().__init__()
        self.conv1 = nn.Conv2d(channel, channel // reduction, kernel_size=1)
        self.conv2 = nn.Sequential(
            nn.Conv2d(channel // reduction, channel // reduction, kernel_size, padding=dilation_val,
                      dilation=dilation_val),
            nn.BatchNorm2d(channel // reduction),
            nn.ReLU(inplace=True),
            nn.Conv2d(channel // reduction, channel // reduction, kernel_size, padding=dilation_val,
                      dilation=dilation_val),
            nn.BatchNorm2d(channel // reduction),
            nn.ReLU(inplace=True)
        )
        self.conv3 = nn.Conv2d(channel // reduction, 1, kernel_size=1)
        self.bn = nn.BatchNorm2d(1)

    def forward(self, x):
        b, c, h, w = x.shape
        y = self.conv1(x)
        y = self.conv2(y)
        y = self.conv3(y)
        y = self.bn(y)
        return y.expand_as(x)


class BAM(nn.Module):
    def __init__(self, channel):
        super(BAM, self).__init__()
        self.channel_attn = ChannelGate(channel)
        self.spatial_attn = SpatialGate(channel)

    def forward(self, x):
        attn = F.sigmoid(self.channel_attn(x) + self.spatial_attn(x))
        return x + x * attn

###################### BAM  attention  ####     END   by  AI&CV  ###############################

2.2 加入`tasks.py`中：

def parse_model(d, ch, verbose=True):  # model_dict, input_channels(3)

添加以下内容

        elif m is BAM:
            c1, c2 = ch[f], args[0]
            if c2 != nc:
                c2 = make_divisible(min(c2, max_channels) * width, 8)
            args = [c1, *args[1:]]

2.3 yolov8_BAM.yaml

仅供参考，加入网络位置不同在不同数据集表现不一致是正常现场

# 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: 2  # 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)
  - [-1, 1, BAM, [1024]]  # 24 

  - [[15, 18, 22], 1, Detect, [nc]]  # Detect(P3, P4, P5)

3.YOLOv8魔术师专栏介绍