SCConv-空间和通道重构卷积

SCConv（空间和通道重构卷积）的高效卷积模块，以减少卷积神经网络（CNN）中的空间和通道冗余。SCConv旨在通过优化特征提取过程，减少计算资源消耗并提高网络性能。该模块包括两个单元：
1.空间重构单元（SRU）：SRU通过分离和重构方法来减少空间冗余。
2.通道重构单元（CRU）：CRU采用分割-变换-融合策略来减少通道冗余。

论文地址：SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy

YOLOv8添加SCConv卷积

SCConv代码实现

import torch
import torch.nn.functional as F
import torch.nn as nnclass 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.weight = nn.Parameter(torch.randn(c_num, 1, 1))self.bias = 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.weight + self.biasclass SRU(nn.Module):def __init__(self,oup_channels: int,group_num: int = 16,gate_treshold: float = 0.5,torch_gn: bool = True):super().__init__()self.gn = nn.GroupNorm(num_channels=oup_channels, num_groups=group_num) if torch_gn else GroupBatchnorm2d(c_num=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.weight / sum(self.gn.weight)w_gamma = w_gamma.view(1, -1, 1, 1)reweigts = self.sigomid(gn_x * w_gamma)# Gatew1 = torch.where(reweigts > self.gate_treshold, torch.ones_like(reweigts), reweigts)  # 大于门限值的设为1，否则保留原值w2 = torch.where(reweigts > self.gate_treshold, torch.zeros_like(reweigts), reweigts)  # 大于门限值的设为0，否则保留原值x_1 = w1 * xx_2 = w2 * xy = self.reconstruct(x_1, x_2)return ydef 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):def __init__(self,op_channel: int,group_num: int = 4,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 xif __name__ == '__main__':x = torch.randn(1, 32, 16, 16)model = ScConv(32)print(model(x).shape)

SCConv嵌入时额外添加调用函数

YOLOv8中直接嵌入会报错而且参数对不上，所以需要额外定义一个函数作为中转

class SCConv_yolov8(nn.Module):def __init__(self, in_channels, out_channels, kernel_size=1, stride=1, g=1, dilation=1):super().__init__()self.conv = Conv(in_channels, out_channels, k=1)self.RFAConv = ScConv(out_channels)self.bn = nn.BatchNorm2d(out_channels)self.gelu = nn.GELU()def forward(self, x):x = self.conv(x)x = self.RFAConv(x)x = self.gelu(self.bn(x))return x

将SCConv嵌入C2f与Bottleneck模块

class Bottleneck_SCConv(nn.Module):"""Standard bottleneck."""def __init__(self, c1, c2, shortcut=True, g=1, k=(3, 3), e=0.5):"""Initializes a bottleneck module with given input/output channels, shortcut option, group, kernels, andexpansion."""super().__init__()c_ = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, c_, k[0], 1)self.cv2 = SCConv_yolov8(c_, c2, k[1], 1, g=g)self.add = shortcut and c1 == c2def forward(self, x):"""'forward()' applies the YOLO FPN to input data."""return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))class C2f_SCConv(nn.Module):"""Faster Implementation of CSP Bottleneck with 2 convolutions."""def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5):"""Initialize CSP bottleneck layer with two convolutions with arguments ch_in, ch_out, number, shortcut, groups,expansion."""super().__init__()self.c = int(c2 * e)  # hidden channelsself.cv1 = Conv(c1, 2 * self.c, 1, 1)self.cv2 = Conv((2 + n) * self.c, c2, 1)  # optional act=FReLU(c2)self.m = nn.ModuleList(Bottleneck_SCConv(self.c, self.c, shortcut, g, k=((3, 3), (3, 3)), e=1.0) for _ in range(n))def forward(self, x):"""Forward pass through C2f layer."""x = self.cv1(x)x = x.chunk(2, 1)y = list(x)# y = list(self.cv1(x).chunk(2, 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))def forward_split(self, x):"""Forward pass using split() instead of chunk()."""y = list(self.cv1(x).split((self.c, self.c), 1))y.extend(m(y[-1]) for m in self.m)return self.cv2(torch.cat(y, 1))

参考案例

# 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, [128, True]]- [-1, 1, Conv, [256, 3, 2]]  # 3-P3/8- [-1, 6, C2f, [256, True]]- [-1, 1, Conv, [512, 3, 2]]  # 5-P4/16- [-1, 6, C2f, [512, True]]- [-1, 1, Conv, [1024, 3, 2]]  # 7-P5/32- [-1, 3, C2f, [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_SCConv, [256]]  # 15 (P3/8-small)- [-1, 1, Conv, [256, 3, 2]]- [[-1, 12], 1, Concat, [1]]  # cat head P4- [-1, 3, C2f_SCConv, [512]]  # 18 (P4/16-medium)- [-1, 1, Conv, [512, 3, 2]]- [[-1, 9], 1, Concat, [1]]  # cat head P5- [-1, 3, C2f_SCConv, [1024]]  # 21 (P5/32-large)- [[15, 18, 21], 1, Detect, [nc]]  # Detect(P3, P4, P5)