将yolov5中的PANet层改为BiFPN

本文以YOLOv5-6.1版本为例

一、Add

1.在common.py后加入如下代码

# 结合BiFPN 设置可学习参数 学习不同分支的权重
# 两个分支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]))


# 三个分支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)  # 将权重进行归一化
        # Fast normalized fusion
        return self.conv(self.silu(weight[0] * x[0] + weight[1] * x[1] + weight[2] * x[2]))

2.yolov5s.yaml进行修改

# YOLOv5  by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# 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 BiFPN 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, [512, 3, 2]],  # 为了BiFPN正确add，调整channel数
   [[-1, 13, 6], 1, BiFPN_Add3, [256, 256]],  # cat P4 <--- BiFPN change 注意v5s通道数是默认参数的一半
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, BiFPN_Add2, [256, 256]],  # cat head P5
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

3.修改yolo.py，在parse_model函数中找到elif m is Concat:语句，在其后面加上BiFPN_Add相关语句：

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

4.修改train.py，向优化器中添加BiFPN的权重参数

将BiFPN_Add2和BiFPN_Add3函数中定义的w参数，加入g1

 # BiFPN_Concat
        elif isinstance(v, BiFPN_Add2) and hasattr(v, \'w\') and isinstance(v.w, nn.Parameter):
            g1.append(v.w)
        elif isinstance(v, BiFPN_Add3) and hasattr(v, \'w\') and isinstance(v.w, nn.Parameter):
            g1.append(v.w)

然后导入一下这两个包

一、Concat

1.在common.py后加入如下代码

# 结合BiFPN 设置可学习参数 学习不同分支的权重
# 两个分支concat操作
class BiFPN_Concat2(nn.Module):
    def __init__(self, dimension=1):
        super(BiFPN_Concat2, self).__init__()
        self.d = dimension
        self.w = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True)
        self.epsilon = 0.0001

    def forward(self, x):
        w = self.w
        weight = w / (torch.sum(w, dim=0) + self.epsilon)  # 将权重进行归一化
        # Fast normalized fusion
        x = [weight[0] * x[0], weight[1] * x[1]]
        return torch.cat(x, self.d)


# 三个分支concat操作
class BiFPN_Concat3(nn.Module):
    def __init__(self, dimension=1):
        super(BiFPN_Concat3, self).__init__()
        self.d = dimension
        # 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(3, dtype=torch.float32), requires_grad=True)
        self.epsilon = 0.0001

    def forward(self, x):
        w = self.w
        weight = w / (torch.sum(w, dim=0) + self.epsilon)  # 将权重进行归一化
        # Fast normalized fusion
        x = [weight[0] * x[0], weight[1] * x[1], weight[2] * x[2]]
        return torch.cat(x, self.d)

2.yolov5s.yaml进行修改 

# YOLOv5  by Ultralytics, GPL-3.0 license

# Parameters
nc: 80  # number of classes
depth_multiple: 0.33  # model depth multiple
width_multiple: 0.50  # layer channel multiple
anchors:
  - [10,13, 16,30, 33,23]  # P3/8
  - [30,61, 62,45, 59,119]  # P4/16
  - [116,90, 156,198, 373,326]  # P5/32

# 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 BiFPN head
head:
  [[-1, 1, Conv, [512, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, \'nearest\']],
   [[-1, 6], 1, BiFPN_Concat2, [1]],  # cat backbone P4 <--- BiFPN change
   [-1, 3, C3, [512, False]],  # 13

   [-1, 1, Conv, [256, 1, 1]],
   [-1, 1, nn.Upsample, [None, 2, \'nearest\']],
   [[-1, 4], 1, BiFPN_Concat2, [1]],  # cat backbone P3 <--- BiFPN change
   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)

   [-1, 1, Conv, [256, 3, 2]],
   [[-1, 14, 6], 1, BiFPN_Concat3, [1]],  # cat P4 <--- BiFPN change
   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)

   [-1, 1, Conv, [512, 3, 2]],
   [[-1, 10], 1, BiFPN_Concat2, [1]],  # cat head P5 <--- BiFPN change
   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)

   [[17, 20, 23], 1, Detect, [nc, anchors]],  # Detect(P3, P4, P5)
  ]

3.修改yolo.py，在parse_model函数中找到elif m is Concat:语句，在其后面加上BiFPN_Concat相关语句： 

# 添加bifpn_concat结构
elif m in [Concat, BiFPN_Concat2, BiFPN_Concat3]:
    c2 = sum(ch[x] for x in f)

4.修改train.py，向优化器中添加BiFPN的权重参数

添加复方式同上（Add）

# BiFPN_Concat
        elif isinstance(v, BiFPN_Concat2) and hasattr(v, \'w\') and isinstance(v.w, nn.Parameter):
            g1.append(v.w)
        elif isinstance(v, BiFPN_Concat3) and hasattr(v, \'w\') and isinstance(v.w, nn.Parameter):
            g1.append(v.w)

至此，大功告成~~~

reference：【YOLOv5-6.x】设置可学习权重结合BiFPN（Add操作）_嗜睡的篠龙的博客-CSDN博客
【YOLOv5-6.x】设置可学习权重结合BiFPN（Concat操作）_嗜睡的篠龙的博客-CSDN博客_bifpn代码