论文题目:Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles
论文:https://arxiv.org/abs/2206.02424
代码:https://github.com/AlanLi1997/Slim-neck-by-GSConv
直接步入正题~~~
目标:为YOLOv5模型构建一个简单高效的Neck模块。考虑了卷积方法、特征融合结构、计算效率、计算成本效益等诸多因素。
一、GSConv
class GSConv(nn.Module):
# GSConv https://github.com/AlanLi1997/slim-neck-by-gsconv
def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
super().__init__()
c_ = c2 // 2
self.cv1 = Conv(c1, c_, k, s, None, g, act)
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
def forward(self, x):
x1 = self.cv1(x)
x2 = torch.cat((x1, self.cv2(x1)), 1)
# shuffle
b, n, h, w = x2.data.size()
b_n = b * n // 2
y = x2.reshape(b_n, 2, h * w)
y = y.permute(1, 0, 2)
y = y.reshape(2, -1, n // 2, h, w)
return torch.cat((y[0], y[1]), 1)
将YOLOv5s.yaml的Neck模块中的Conv换成GSConv
1、将GSConv代码加入common.py文件中
2、找到yolo.py文件里的parse_model函数,将类名加入进去
3、修改配置文件,将YOLOv5s.yaml的Neck模块中的Conv换成GSConv
~~~此处有一个疑问,官方给出的GSConv代码中为什么没用DWConv呢?希望知道的朋友在评论区指点一下~~~
二、GSConv+Slim Neck
1、GSBottleneck
class GSBottleneck(nn.Module):
# GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv
def __init__(self, c1, c2, k=3, s=1):
super().__init__()
c_ = c2 // 2
# for lighting
self.conv_lighting = nn.Sequential(
GSConv(c1, c_, 1, 1),
GSConv(c_, c2, 1, 1, act=False))
# for receptive field
self.conv = nn.Sequential(
GSConv(c1, c_, 3, 1),
GSConv(c_, c2, 3, 1, act=False))
self.shortcut = Conv(c1, c2, 3, 1, act=False)
def forward(self, x):
return self.conv_lighting(x) + self.shortcut(x)
2、VoVGSCSP
class VoVGSCSP(nn.Module):
# VoV-GSCSP https://github.com/AlanLi1997/slim-neck-by-gsconv
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__()
c_ = int(c2 * e)
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(2 * c_, c2, 1)
self.m = nn.Sequential(*(GSBottleneck(c_, c_) for _ in range(n)))
def forward(self, x):
x1 = self.cv1(x)
return self.cv2(torch.cat((self.m(x1), x1), dim=1))
将YOLOv5s.yaml的Neck模块中的Conv换成GSConv,C3模块换为VoVGSCSP模块
1、将以下代码加入common.py文件中
class GSConv(nn.Module):
# GSConv https://github.com/AlanLi1997/slim-neck-by-gsconv
def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
super().__init__()
c_ = c2 // 2
self.cv1 = Conv(c1, c_, k, s, None, g, act)
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
def forward(self, x):
x1 = self.cv1(x)
x2 = torch.cat((x1, self.cv2(x1)), 1)
# shuffle
b, n, h, w = x2.data.size()
b_n = b * n // 2
y = x2.reshape(b_n, 2, h * w)
y = y.permute(1, 0, 2)
y = y.reshape(2, -1, n // 2, h, w)
return torch.cat((y[0], y[1]), 1)
class GSBottleneck(nn.Module):
# GS Bottleneck https://github.com/AlanLi1997/slim-neck-by-gsconv
def __init__(self, c1, c2, k=3, s=1):
super().__init__()
c_ = c2 // 2
# for lighting
self.conv_lighting = nn.Sequential(
GSConv(c1, c_, 1, 1),
GSConv(c_, c2, 1, 1, act=False))
# for receptive field
self.conv = nn.Sequential(
GSConv(c1, c_, 3, 1),
GSConv(c_, c2, 3, 1, act=False))
self.shortcut = nn.Identity()
def forward(self, x):
return self.conv_lighting(x)
class VoVGSCSP(nn.Module):
# VoV-GSCSP https://github.com/AlanLi1997/slim-neck-by-gsconv
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__()
c_ = int(c2 * e)
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(2 * c_, c2, 1)
self.m = nn.Sequential(*(GSBottleneck(c_, c_) for _ in range(n)))
def forward(self, x):
x1 = self.cv1(x)
return self.cv2(torch.cat((self.m(x1), x1), dim=1))
2、找到yolo.py文件里的parse_model函数,将类名加入进去,注意有两处需要添加的地方
3、修改配置文件,将YOLOv5s.yaml的Neck模块中的Conv换成GSConv ,C3模块换为VoVGSCSP
Appendix
下图是原论文中给出的结构图,个人对照源码后觉得这里多画了一个GSConv模块(红色框里所示),如果有知道的大佬望在评论区指点一下。
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