今天小编给大家分享一下怎么将yolov5中的PANet层改为BiFPN的相关知识点，内容详细，逻辑清晰，相信大部分人都还太了解这方面的知识，所以分享这篇文章给大家参考一下，希望大家阅读完这篇文章后有所收获，下面我们一起来了解一下吧。
一、Add
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 # Parametersnc: 80 # number of classesdepth_multiple: 0.33 # model depth multiplewidth_multiple: 0.50 # layer channel multipleanchors: - [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 backbonebackbone: # [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 headhead: [[-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) ]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])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
二、Concat
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 # Parametersnc: 80 # number of classesdepth_multiple: 0.33 # model depth multiplewidth_multiple: 0.50 # layer channel multipleanchors: - [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 backbonebackbone: # [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 headhead: [[-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) ]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)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)以上就是“怎么将yolov5中的PANet层改为BiFPN”这篇文章的所有内容，感谢各位的阅读！相信大家阅读完这篇文章都有很大的收获，小编每天都会为大家更新不同的知识，如果还想学习更多的知识，请关注编程网行业资讯频道。
