本文章纯属记录学习使用，我也不太明白是否为小目标检测层，不对的地方还希望一块交流

yolov5初始模型在特征融合时只对P3、P4、P5、三个特征层进行了融合，添加小目标检测层的目的是把P2（也就是yaml文件中第二个conv层得到的特征图）也加入到特征融合中。

P2位于低特征层，具有较强的位置信息，语义特征信息较弱，常用来进行小目标检测，这篇博客我觉得写的很好高低特征层。

但并不是你觉得你所要检测的是小目标，就需要加入小目标检测层，当添加小目标检测层有时会适得其反，并不会有所改进。看论文对于小目标的定义为：小于32*32像素的目标为小目标。

~~（yaml能跑通但是有些不合理，等有时间会修改）~~

添加BiFPN

第一步：在common.py 文件下添加下列代码

# BiFPN
# 两个特征图add操作
class BiFPN_Add2(nn.Module):
    def __init__(self, c1, c2):
        super(BiFPN_Add2, self).__init__()
        # 设置可学习参数 nn.Parameter的作用是：将一个不可训练的类型Tensor转换成可以训练的类型parameter
        # 并且会向宿主模型注册该参数 成为其一部分 即model.parameters()会包含这个parameter
        # 从而在参数优化的时候可以自动一起优化
        self.w = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.epsilon = 0.0001
        self.conv = nn.Conv2d(c1, c2, kernel_size=1, stride=1, padding=0)
        self.silu = nn.SiLU()
    def forward(self, x):
        w = self.w
        weight = w / (torch.sum(w, dim=0) + self.epsilon)
        return self.conv(self.silu(weight[0] * x[0] + weight[1] * x[1]))
# 三个特征图add操作
class BiFPN_Add3(nn.Module):
    def __init__(self, c1, c2):
        super(BiFPN_Add3, self).__init__()
        self.w = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True)
        self.epsilon = 0.0001
        self.conv = nn.Conv2d(c1, c2, kernel_size=1, stride=1, padding=0)
        self.silu = nn.SiLU()
    def forward(self, x):
        w = self.w
        weight = w / (torch.sum(w, dim=0) + self.epsilon)
        # Fast normalized fusion
        return self.conv(self.silu(weight[0] * x[0] + weight[1] * x[1] + weight[2] * x[2]))

第二步：修改 yolo.py

        elif m in [BiFPN_Add2, BiFPN_Add3]:
            c2 = max([ch[x] for x in f])

第三步：修改 train.py

看下边

~~该部分代码还请去看迪导博客，他提供很多改进方法，值得学习，强烈推荐！~~

Yolov5如何更换BiFPN?

6.2版本的train.py代码有些变化，需要进入smart_optomizer这个函数中加入train.py这部分代码

~~6.2版本会出现报错，后续会解决~~

~~2023.3.6 最近在忙并没进行解决，报错的原因大概就是执行顺序的问题~~

~~解决思路:把smart_optimizer复制到train.py里，然后加上面红框的代码就可以（如果还报错请留言我再解决）~~

2023.3.13 解决报错

只需要把smart_optimizer这个函数放在train.py就行，位置随便

         # BiFPN_Concat
        elif isinstance(v, BiFPN_Add2) and hasattr(v, 'w') and isinstance(v.w, nn.Parameter):
            g[1].append(v.w)
        elif isinstance(v, BiFPN_Add3) and hasattr(v, 'w') and isinstance(v.w, nn.Parameter):
            g[1].append(v.w)

导入BiFPN_Add2，BiFPN_Add3

from models.common import BiFPN_Add3, BiFPN_Add2

记得要把这个删除，不然会报错

运行成功

接下来便是修改后的yaml文件

# Parameters
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors: 3  # AutoAnchor evolves 3 anchors per P output layer
# YOLOv5 v6.0 backbone
# Adding connection in architecture between backbone and multi-stage head
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]
# YOLOv5 v6.0 head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, BiFPN_Add2, [256,256]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, BiFPN_Add2, [128,128]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 2], 1, BiFPN_Add2, [64,64]],  # cat backbone P2
   [-1, 3, C3, [128, False]],  # 21 (P2/4-tiny)
   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 17, 4], 1, BiFPN_Add3, [128,128]],
   [-1, 3, C3, [256, False]],  # 24 (P4/16-medium)
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 13, 6], 1, BiFPN_Add3, [256,256]],
   [-1, 3, C3, [512, False]],  # 27 (P4/16-medium)
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1,  BiFPN_Add2, [256,256]],
   [-1, 3, C3, [1024, False]],  # 30 (P5/32-large)
   [[21, 24, 27, 30], 1, Detect, [nc, anchors]],  # Detect(P2, P3, P4, P5)
  ]

经过修改后的四检测头结合BiFPN（若有错误还请大佬指点一下）

# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# Parameters  (P2, P3, P4, P5)都输出，宽深与large版本相同，相当于比large版本能检测更小物体
nc: 1  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors: 4  # AutoAnchor evolves 3 anchors per P output layer
# YOLOv5 v6.0 backbone
backbone:
  # [from, number, module, args]
  [[-1, 1, Conv, [64, 6, 2, 2]],  # 0-P1/2
   [-1, 1, Conv, [128, 3, 2]],  # 1-P2/4
   [-1, 3, C3, [128]],
   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
   [-1, 6, C3, [256]],
   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
   [-1, 9, C3, [512]],
   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
   [-1, 3, C3, [1024]],
   [-1, 1, SPPF, [1024, 5]],  # 9
  ]
# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 6], 1, BiFPN_Add2, [256,256]],  # cat backbone P4
   [-1, 3, C3, [512, False]],  # 13
   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 4], 1, BiFPN_Add2, [128,128]],  # cat backbone P3
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
   [-1, 1, Conv, [128, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
   [[-1, 2], 1, BiFPN_Add2, [64,64]],  # cat backbone P2
   [-1, 1, C3, [128, False]],  # 21 (P2/4-xsmall)
   [-1, 1, Conv, [128, 3, 2]],
   [-1, 1, Conv, [256, 1, 1]],
   [[-1, 17, 4], 1, BiFPN_Add3, [128,128]],  # cat head P3
   [-1, 3, C3, [256, False]],  # 25 (P3/8-small)
   [-1, 1, Conv, [256, 3, 2]],
   [-1, 1, Conv, [512, 1, 1]],
   [[-1, 13, 6], 1, BiFPN_Add3, [256,256]],  # cat head P4
   [-1, 3, C3, [512, False]],  # 29 (P4/16-medium)
   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, BiFPN_Add2, [256,256]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 32 (P5/32-large)
   [[21, 25, 29, 32], 1, Detect, [nc, anchors]],  # Detect(P2, P3, P4, P5)
  ]

题外话

这个博客中的BiFPN代码有两个版本，个人感觉第二个版本更合理些，因为不会出现我用1*1conv更换通道使得BiFPN_Add3结合成功，可以尝试下

标签 if, Python, 目标检测

yoloV5更换BiFPN结合小目标检测层

添加BiFPN

2023.3.13 解决报错