将yolov5中的PANet层改为BiFPN

本文以YOLOv5-6.1版本为例

1.添加

[En]

1. 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

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:

将yolov5中的PANet层改为BiFPN
# 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

BiFPN_Add2BiFPN_Add3函数中定义的 w参数,加入g1

将yolov5中的PANet层改为BiFPN
 # 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)

然后导入这两个包。

[En]

Then import these two packages.

将yolov5中的PANet层改为BiFPN

1.合并

[En]

1. 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

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:

将yolov5中的PANet层改为BiFPN
# 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,工作就完成了。

[En]

At this point, the work is done.

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

Original: https://blog.csdn.net/m0_56247038/article/details/124891449
Author: 有温度的AI
Title: 将yolov5中的PANet层改为BiFPN

原创文章受到原创版权保护。转载请注明出处:https://www.johngo689.com/6251/

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