修改yaml文件（以yolov5s为例）
本文以 yolov5s.yaml为例进行修改，修改模型配置文件时要注意以下几点：
- 这里的yaml文件只修改了一处，也就是将19层的Concat换成了BiFPN_Add，要想修改其他层的Concat，可以类比进行修改
- BiFPN_Add本质是add操作，不是concat操作，因此，BiFPN_Add的各个输入层要求大小完全一致（通道数、feature map大小等），因此，这里要修改之前的参数[-1, 13, 6]，来满足这个要求：
- -1层就是上一层的输出，原来上一层的输出channel数为256，这里改成512
- 13层就是这里
[-1, 3, C3, [512, False]], # 13 - 这样修改后，BiFPN_Add各个输入大小都是
[bs,256,40,40] - 最后BiFPN_Add后面的参数层设置为
[256, 256]也就是输入输出channel数都是256
nc: 80
depth_multiple: 0.33
width_multiple: 0.50
anchors:
- [10,13, 16,30, 33,23]
- [30,61, 62,45, 59,119]
- [116,90, 156,198, 373,326]
backbone:
[[-1, 1, Conv, [64, 6, 2, 2]],
[-1, 1, Conv, [128, 3, 2]],
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]],
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]],
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]],
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]],
]
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]],
[-1, 3, C3, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]],
[-1, 3, C3, [256, False]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, 13, 6], 1, BiFPN_Add3, [256, 256]],
[-1, 3, C3, [512, False]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]],
[-1, 3, C3, [1024, False]],
[[17, 20, 23], 1, Detect, [nc, anchors]],
]
nc: 80
depth_multiple: 0.33
width_multiple: 0.50
anchors:
- [10,13, 16,30, 33,23]
- [30,61, 62,45, 59,119]
- [116,90, 156,198, 373,326]
backbone:
[[-1, 1, Conv, [64, 6, 2, 2]],
[-1, 1, Conv, [128, 3, 2]],
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]],
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]],
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]],
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]],
]
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, BiFPN_Add2, [256, 256]],
[-1, 3, C3, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, BiFPN_Add2, [128, 128]],
[-1, 3, C3, [256, False]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, 13, 6], 1, BiFPN_Add3, [256, 256]],
[-1, 3, C3, [512, False]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, BiFPN_Add2, [256, 256]],
[-1, 3, C3, [1024, False]],
[[17, 20, 23], 1, Detect, [nc, anchors]],
]
from n params module arguments
0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2]
1 -1 1 18560 models.common.Conv [32, 64, 3, 2]
2 -1 1 18816 models.common.C3 [64, 64, 1]
3 -1 1 73984 models.common.Conv [64, 128, 3, 2]
4 -1 2 115712 models.common.C3 [128, 128, 2]
5 -1 1 295424 models.common.Conv [128, 256, 3, 2]
6 -1 3 625152 models.common.C3 [256, 256, 3]
7 -1 1 1180672 models.common.Conv [256, 512, 3, 2]
8 -1 1 1182720 models.common.C3 [512, 512, 1]
9 -1 1 656896 models.common.SPPF [512, 512, 5]
10 -1 1 131584 models.common.Conv [512, 256, 1, 1]
11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
12 [-1, 6] 1 65794 models.common.BiFPN_Add2 [256, 256]
13 -1 1 296448 models.common.C3 [256, 256, 1, False]
14 -1 1 33024 models.common.Conv [256, 128, 1, 1]
15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
16 [-1, 4] 1 16514 models.common.BiFPN_Add2 [128, 128]
17 -1 1 74496 models.common.C3 [128, 128, 1, False]
18 -1 1 295424 models.common.Conv [128, 256, 3, 2]
19 [-1, 13, 6] 1 65795 models.common.BiFPN_Add3 [256, 256]
20 -1 1 296448 models.common.C3 [256, 256, 1, False]
21 -1 1 590336 models.common.Conv [256, 256, 3, 2]
22 [-1, 10] 1 65794 models.common.BiFPN_Add2 [256, 256]
23 -1 1 1051648 models.common.C3 [256, 512, 1, False]
24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
Model Summary: 278 layers, 7384006 parameters, 7384006 gradients, 17.2 GFLOPs
修改common.py
- 复制粘贴一下代码：
class BiFPN_Add2(nn.Module):
def __init__(self, c1, c2):
super(BiFPN_Add2, self).__init__()
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]))
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)
return self.conv(self.silu(weight[0] * x[0] + weight[1] * x[1] + weight[2] * x[2]))
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：
elif m is Concat:
c2 = sum(ch[x] for x in f)
elif m in [BiFPN_Add2, BiFPN_Add3]:
c2 = max([ch[x] for x in f])
修改train.py
- 将
BiFPN_Add2和BiFPN_Add3函数中定义的w参数，加入g1
g0, g1, g2 = [], [], []
for v in model.modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
g2.append(v.bias)
if isinstance(v, nn.BatchNorm2d):
g0.append(v.weight)
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
g1.append(v.weight)
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)
References
Original: https://blog.csdn.net/weixin_43799388/article/details/124091648
Author: 嗜睡的篠龙
Title: 【YOLOv5-6.x】设置可学习权重结合BiFPN（Add操作）
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