专栏介绍：YOLOv9改进系列 | 包含深度学习最新创新，主力高效涨点！！！
一、改进点介绍
SCConv是一种即插即用的空间和通道重建卷积。
RepNCSPELAN4是YOLOv9中的特征提取模块，类似YOLOv5和v8中的C2f与C3模块。
二、RepNCSPELAN4_SCConv模块详解
2.1 模块简介
RepNCSPELAN4_SCConv的主要思想： 使用SCConv替换RepNCSPELAN4中的Conv模块。
三、 RepNCSPELAN4_SCConv模块使用教程
3.1 RepNCSPELAN4_SCConv模块的代码
class RepConvN_SC(RepConvN):
"""RepConv is a basic rep-style block, including training and deploy status
This code is based on https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py
"""
default_act = nn.SiLU() # default activation
def __init__(self, c1, c2, k=3, s=1, p=1, g=1, d=1, act=True, bn=False, deploy=False):
super().__init__(c1, c2, k, s, p, g, d, act, bn, deploy)
assert k == 3 and p == 1
self.g = g
self.c1 = c1
self.c2 = c2
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
self.bn = None
self.conv1 = SCConv(c1, c2, k, s, p=p, g=g)
self.conv2 = Conv(c1, c2, 1, s, p=(p - k // 2), g=g, act=False)
class RepNBottleneck_SC(RepNBottleneck):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5): # ch_in, ch_out, shortcut, kernels, groups, expand
super().__init__( c1, c2, shortcut, g, k, e)
c_ = int(c2 * e) # hidden channels
self.cv1 = RepConvN_SC(c1, c_, k[0], 1)
self.cv2 = SCConv(c_, c2, k[1], s=1, g=g)
self.add = shortcut and c1 == c2
class RepNCSP_SCConv(RepNCSP):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_)
self.cv2 = Conv(c1, c_)
self.cv3 = Conv(2 * c_, c2) # optional act=FReLU(c2)
self.m = nn.Sequential(*(RepNBottleneck_SC(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
class RepNCSPELAN4SCConv1(RepNCSPELAN4):
# csp-elan
def __init__(self, c1, c2, c3, c4, c5=1): # ch_in, ch_out, number, shortcut, groups, expansion
super().__init__(c1, c2, c3, c4, c5)
self.cv1 = Conv(c1, c3, k=1, s=1)
self.cv2 = nn.Sequential(RepNCSP_SCConv(c3 // 2, c4, c5), SCConv(c4, c4, 3, 1))
self.cv3 = nn.Sequential(RepNCSP_SCConv(c4, c4, c5), SCConv(c4, c4, 3, 1))
self.cv4 = Conv(c3 + (2 * c4), c2, 1, 1)
class SCConv(nn.Module):
"""https://github.com/MCG-NKU/SCNet/blob/master/scnet.py"""
def __init__(self, inplanes, planes,k=3, s=1, p=1, dilation=1, g=1, pooling_r=4):
super(SCConv, self).__init__()
self.k2 = nn.Sequential(
nn.AvgPool2d(kernel_size=pooling_r, stride=pooling_r),
Conv(inplanes, planes, k=k, s=s, p=p, d=dilation, g=g, act=False))
self.k3 = Conv(inplanes, planes, k=k, s=s, p=p, d=dilation, g=g, act=False)
self.k4 = Conv(inplanes, planes, k=k, s=s, p=p, d=dilation, g=g, act=False)
def forward(self, x):
identity = x
out = torch.sigmoid(torch.add(identity, F.interpolate(self.k2(x), identity.size()[2:]))) # sigmoid(identity + k2)
out = torch.mul(self.k3(x), out) # k3 * sigmoid(identity + k2)
out = self.k4(out) # k4
return out3.2 在YOlO v9中的添加教程
阅读YOLOv9添加模块教程或使用下文操作
1. 将YOLOv9工程中models下common.py文件中的最下行（否则可能因类继承报错）增加模块的代码。
2. 将YOLOv9工程中models下yolo.py文件中的第681行（可能因版本变化而变化）增加以下代码。
RepNCSPELAN4, SPPELAN, RepNCSPELAN4SCConv1}:3.3 运行配置文件
# YOLOv9
# Powered bu https://blog.csdn.net/StopAndGoyyy
# parameters
nc: 80 # number of classes
#depth_multiple: 0.33 # model depth multiple
depth_multiple: 1 # model depth multiple
#width_multiple: 0.25 # layer channel multiple
width_multiple: 1 # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()
# anchors
anchors: 3
# YOLOv9 backbone
backbone:
[
[-1, 1, Silence, []],
# conv down
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4SCConv1, [256, 128, 64, 1]], # 3
# avg-conv down
[-1, 1, ADown, [256]], # 4-P3/8
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 5
# avg-conv down
[-1, 1, ADown, [512]], # 6-P4/16
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 7
# avg-conv down
[-1, 1, ADown, [512]], # 8-P5/32
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 9
]
# YOLOv9 head
head:
[
# elan-spp block
[-1, 1, SPPELAN, [512, 256]], # 10
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 7], 1, Concat, [1]], # cat backbone P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 13
# up-concat merge
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 5], 1, Concat, [1]], # cat backbone P3
# elan-2 block
[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]], # 16 (P3/8-small)
# avg-conv-down merge
[-1, 1, ADown, [256]],
[[-1, 13], 1, Concat, [1]], # cat head P4
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 19 (P4/16-medium)
# avg-conv-down merge
[-1, 1, ADown, [512]],
[[-1, 10], 1, Concat, [1]], # cat head P5
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 22 (P5/32-large)
# multi-level reversible auxiliary branch
# routing
[5, 1, CBLinear, [[256]]], # 23
[7, 1, CBLinear, [[256, 512]]], # 24
[9, 1, CBLinear, [[256, 512, 512]]], # 25
# conv down
[0, 1, Conv, [64, 3, 2]], # 26-P1/2
# conv down
[-1, 1, Conv, [128, 3, 2]], # 27-P2/4
# elan-1 block
[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]], # 28
# avg-conv down fuse
[-1, 1, ADown, [256]], # 29-P3/8
[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]], # 31
# avg-conv down fuse
[-1, 1, ADown, [512]], # 32-P4/16
[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 34
# avg-conv down fuse
[-1, 1, ADown, [512]], # 35-P5/32
[[25, -1], 1, CBFuse, [[2]]], # 36
# elan-2 block
[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]], # 37
# detection head
# detect
[[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]], # DualDDetect(A3, A4, A5, P3, P4, P5)
]
3.4 训练过程
欢迎关注!
