卷积篇 | YOLOv8改进之C2f模块融合SCConv | 即插即用的空间和通道维度重构卷积

🚀2.网络结构

本文的改进是基于YOLOv8，关于其网络结构具体如下图所示：

YOLOv8官方仓库地址：

GitHub - ultralytics/ultralytics: NEW - YOLOv8 🚀 in PyTorch > ONNX > OpenVINO > CoreML > TFLite

本文所作出的改进是在主干网络中的C2f模块融合SCConv，改进后的网络结构图具体如下图所示：

🚀3.添加步骤

针对本文的改进，具体步骤如下所示：👇

步骤1：block.py文件修改

步骤2：__init__.py文件修改

步骤3：tasks.py文件修改

步骤4：创建自定义yaml文件

步骤5：新建train.py文件

步骤6：模型训练测试

🚀4.改进方法

🍀🍀步骤1：block.py文件修改

在源码中找到block.py文件，具体位置是ultralytics/nn/modules/block.py，然后将SCConv模块代码添加到block.py文件末尾位置。

SCConv模块代码：

# C2f模块融合SCConv
# By CSDN 小哥谈
class GroupBatchnorm2d(nn.Module):def __init__(self, c_num: int,group_num: int = 16,eps: float = 1e-10):super(GroupBatchnorm2d, self).__init__()assert c_num >= group_numself.group_num = group_numself.gamma = nn.Parameter(torch.randn(c_num, 1, 1))self.beta = nn.Parameter(torch.zeros(c_num, 1, 1))self.eps = epsdef forward(self, x):N, C, H, W = x.size()x = x.view(N, self.group_num, -1)mean = x.mean(dim=2, keepdim=True)std = x.std(dim=2, keepdim=True)x = (x - mean) / (std + self.eps)x = x.view(N, C, H, W)return x * self.gamma + self.betaclass SRU(nn.Module):def __init__(self,oup_channels: int,group_num: int = 16,gate_treshold: float = 0.5):super().__init__()self.gn = GroupBatchnorm2d(oup_channels, group_num=group_num)self.gate_treshold = gate_tresholdself.sigomid = nn.Sigmoid()def forward(self, x):gn_x = self.gn(x)w_gamma = self.gn.gamma / sum(self.gn.gamma)reweigts = self.sigomid(gn_x * w_gamma)# Gateinfo_mask = reweigts >= self.gate_tresholdnoninfo_mask = reweigts < self.gate_tresholdx_1 = info_mask * xx_2 = noninfo_mask * xx = self.reconstruct(x_1, x_2)return xdef reconstruct(self, x_1, x_2):x_11, x_12 = torch.split(x_1, x_1.size(1) // 2, dim=1)x_21, x_22 = torch.split(x_2, x_2.size(1) // 2, dim=1)return torch.cat([x_11 + x_22, x_12 + x_21], dim=1)class CRU(nn.Module):'''alpha: 0<alpha<1'''def __init__(self,op_channel: int,alpha: float = 1 / 2,squeeze_radio: int = 2,group_size: int = 2,group_kernel_size: int = 3,):super().__init__()self.up_channel = up_channel = int(alpha * op_channel)self.low_channel = low_channel = op_channel - up_channelself.squeeze1 = nn.Conv2d(up_channel, up_channel // squeeze_radio, kernel_size=1, bias=False)self.squeeze2 = nn.Conv2d(low_channel, low_channel // squeeze_radio, kernel_size=1, bias=False)# upself.GWC = nn.Conv2d(up_channel // squeeze_radio, op_channel, kernel_size=group_kernel_size, stride=1,padding=group_kernel_size // 2, groups=group_size)self.PWC1 = nn.Conv2d(up_channel // squeeze_radio, op_channel, kernel_size=1, bias=False)# lowself.PWC2 = nn.Conv2d(low_channel // squeeze_radio, op_channel - low_channel // squeeze_radio, kernel_size=1,bias=False)self.advavg = nn.AdaptiveAvgPool2d(1)def forward(self, x):# Splitup, low = torch.split(x, [self.up_channel, self.low_channel], dim=1)up, low = self.squeeze1(up), self.squeeze2(low)# TransformY1 = self.GWC(up) + self.PWC1(up)Y2 = torch.cat([self.PWC2(low), low], dim=1)# Fuseout = torch.cat([Y1, Y2], dim=1)out = F.softmax(self.advavg(out), dim=1) * outout1, out2 = torch.split(out, out.size(1) // 2, dim=1)return out1 + out2class SCConv(nn.Module):# https://github.com/cheng-haha/ScConv/blob/main/ScConv.pydef __init__(self,op_channel: int,group_num: int = 16,gate_treshold: float = 0.5,alpha: float = 1 / 2,squeeze_radio: int = 2,group_size: int = 2,group_kernel_size: int = 3,):super().__init__()self.SRU = SRU(op_channel,group_num=group_num,gate_treshold=gate_treshold)self.CRU = CRU(op_channel,alpha=alpha,squeeze_radio=squeeze_radio,group_size=group_size,group_kernel_size=group_kernel_size)def forward(self, x):x = self.SRU(x)x = self.CRU(x)return xclass Bottleneck_SCConv(Bottleneck):def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):super().__init__(c1, c2, shortcut, g, k, e)c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, k[0], 1)self.cv2 = SCConv(c2)class C2f_SCConv(C2f):def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):super().__init__(c1, c2, n, shortcut, g, e)self.m = nn.ModuleList(Bottleneck_SCConv(self.c, self.c, shortcut, g, k=(3, 3), e=1.0) for _ in range(n))

再然后，在block.py文件最上方下图所示位置加入C2f_SCConv。

🍀🍀步骤2：__init__.py文件修改

在源码中找到__init__.py文件，具体位置是ultralytics/nn/modules/__init__.py。

修改1：加入C2f_SCConv，具体如下图所示：

修改2：加入C2f_SCConv，具体如下图所示：

🍀🍀步骤3：tasks.py文件修改

在源码中找到tasks.py文件，具体位置是ultralytics/nn/tasks.py。

然后找到parse_model函数（736行左右），在下图中所示位置添加C2f_SCConv。

最后，在本文件中导入该模块，具体代码如下：

from ultralytics.nn.modules.block import C2f_SCConv

🍀🍀步骤4：创建自定义yaml文件

在源码ultralytics/cfg/models/v8目录下创建yaml文件，并命名为：yolov8_SCConv.yaml。具体如下图所示：

yolov8_SCConv.yaml文件完整代码如下所示：

# Ultralytics YOLO 🚀, AGPL-3.0 license
# YOLOv8 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect# Parameters
nc: 80  # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'# [depth, width, max_channels]n: [0.33, 0.25, 1024]  # YOLOv8n summary: 225 layers,  3157200 parameters,  3157184 gradients,   8.9 GFLOPss: [0.33, 0.50, 1024]  # YOLOv8s summary: 225 layers, 11166560 parameters, 11166544 gradients,  28.8 GFLOPsm: [0.67, 0.75, 768]   # YOLOv8m summary: 295 layers, 25902640 parameters, 25902624 gradients,  79.3 GFLOPsl: [1.00, 1.00, 512]   # YOLOv8l summary: 365 layers, 43691520 parameters, 43691504 gradients, 165.7 GFLOPsx: [1.00, 1.25, 512]   # YOLOv8x summary: 365 layers, 68229648 parameters, 68229632 gradients, 258.5 GFLOPs# YOLOv8.0n backbone
backbone:# [from, repeats, module, args]- [-1, 1, Conv, [64, 3, 2]]  # 0-P1/2- [-1, 1, Conv, [128, 3, 2]]  # 1-P2/4- [-1, 3, C2f_SCConv, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f_SCConv, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f_SCConv, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f_SCConv, [1024, True]]- [-1, 1, SPPF, [1024, 5]]  # 9# YOLOv8.0n head
head:- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 6], 1, Concat, [1]]  # cat backbone P4- [-1, 3, C2f, [512]]  # 12- [-1, 1, nn.Upsample, [None, 2, 'nearest']]- [[-1, 4], 1, Concat, [1]]  # cat backbone P3- [-1, 3, C2f, [256]]  # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f, [512]]  # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f, [1024]]  # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)

🍀🍀步骤5：新建train.py文件

在源码根目录下新建train.py文件，文件完整代码如下所示：

from ultralytics import YOLO# Load a model
model = YOLO(r'C:\Users\Lenovo\PycharmProjects\ultralytics-main\ultralytics\cfg\models\v8\yolov8_SCConv.yaml')  # build a new model from YAML
model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
model = YOLO(r'C:\Users\Lenovo\PycharmProjects\ultralytics-main\ultralytics\cfg\models\v8\yolov8_SCConv.yaml').load('yolov8n.pt')  # build from YAML and transfer weights# Train the model
model.train(data=r'C:\Users\Lenovo\PycharmProjects\ultralytics-main\ultralytics\cfg\datasets\helmet.yaml', epochs=100,imgsz=640)

注意：一定要用绝对路径，以防发生报错。

🍀🍀步骤6：模型训练测试

在train.py文件，点击“运行”，在作者自制的安全帽佩戴检测数据集上，模型可以正常训练。

模型训练过程： 

模型训练结果：