一、GSConv论文理论
论文地址：[Slim-neck by GSConv: A better design paradigm of detector architectures for autonomous vehicles]

1.理论思想
本文引入了一种新方法 GSConv 来减轻模型的复杂度并保持准确性。GSConv 可以更好地平衡模型的准确性和速度。并且，提供了一种设计范式，Slim-Neck，以实现检测器更高的计算成本效益。
2.创新点
1)引入了一种新方法 GSConv 来代替 SC 操作。该方法使卷积计算的输出尽可能接近 SC，同时降低计算成本；
2)为自动驾驶汽车的检测器架构提供了一种新的设计范式，即带有标准 Backbone 的 Slim-Neck 设计；
操作过程：
1)输入进行一个普通卷积的下采样
2)对上一步的输出使用DWConv深度卷积
3）将两个conv的结果拼接起来
4）进行shuffle操作
二、代码部署
1.代码

---------------------------- GSConv ---------------------------------

class GSConv(nn.Module):
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
    # y = x2.reshape(x2.shape[0], 2, x2.shape[1] // 2, x2.shape[2], x2.shape[3])
    # y = y.permute(0, 2, 1, 3, 4)
    # return y.reshape(y.shape[0], -1, y.shape[3], y.shape[4])

    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 GSConvns(GSConv):
def init(self, c1, c2, k=1, s=1, g=1, act=True):
super().init(c1, c2, k=1, s=1, g=1, act=True)
c_ = c2 // 2
self.shuf = nn.Conv2d(c_ * 2, c2, 1, 1, 0, bias=False)

def forward(self, x):
    x1 = self.cv1(x)
    x2 = torch.cat((x1, self.cv2(x1)), 1)
    # normative-shuffle, TRT supported
    return nn.ReLU(self.shuf(x2))

class GSBottleneck(nn.Module):
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)

class DWConv(Conv):
# Depth-wise convolution
def init(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation
super().init(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)

class GSBottleneckC(GSBottleneck):
def init(self, c1, c2, k=3, s=1):
super().init(c1, c2, k, s)
self.shortcut = DWConv(c1, c2, 3, 1, act=False)

class VoVGSCSP(nn.Module):
# VoVGSCSP module with GSBottleneck
def init(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().init()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
# self.gc1 = GSConv(c_, c_, 1, 1)
# self.gc2 = GSConv(c_, c_, 1, 1)
self.gsb = GSBottleneck(c_, c_, 1, 1)
self.res = Conv(c_, c_, 3, 1, act=False)
self.cv3 = Conv(2 * c_, c2, 1) #

def forward(self, x):
    x1 = self.gsb(self.cv1(x))
    y = self.cv2(x)
    return self.cv3(torch.cat((y, x1), dim=1))

class VoVGSCSPC(nn.Module):
# cheap VoVGSCSP module with GSBottleneck
def init(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().init()
c_ = int(c2 * e) # hidden channels
self.gsb = GSBottleneckC(c_, c_, 1, 1)

---------------------------- end ---------------------------------

2.配置教程
（1）在models/cmmon.py中添加上述代码

nc: 1 # 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 head

head:
[[-1, 1, GSConv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, ‘nearest’]],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13

[-1, 1, GSConv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, ‘nearest’]],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)

[-1, 1, GSConv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)

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

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

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