空间通道重组卷积SCConv
空间通道重组卷积SCConv，全称Spatial and Channel Reconstruction Convolution，CPR2023年提出，可以即插即用，能够在减少参数的同时提升性能的模块。其核心思想是希望能够实现减少特征冗余从而提高算法的效率。一般压缩模型的方法分为三种，分别是network pruning, weight quantization, low-rank factorization以及knowledge distillation，虽然这些方法能够达到减少参数的效果，但是往往都会导致模型性能的衰减。另一种方法就是在构建模型时利用特殊的模块或操作减少模型参数，获得轻量级的网络模型，这种方法能够在保证性能的同时达到参数减少的效果。
原文地址：SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy
作者提出的SCConv包含两部分，分别是Spatial Reconstruction Unit (SRU)和Channel Reconstruction Unit (CRU)，下面是SSConv的总体结构。
可以看出，SCConv模块设计，对于输入的特征图先利用1x1的卷积改变为适合的通道数，之后便分别是SRU和CRU两个模块对于特征图进行处理，最后在通过1x1的卷积将特征通道数恢复并进行残差操作。
代码实现如下：
import torch
import torch.nn.functional as F
import torch.nn as nn 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):def __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 xif __name__ == '__main__':x = torch.randn(1,32,16,16)model = ScConv(32)print(model(x).shape)
