网络模块可以被解耦为两个独立的组件:SMixer和CMixer,用于空间上和通道上的信息传播, 其中,Norm是一个归一化层,例如,批处理归一化(BN)。请注意,SMixer可以是各种空间操作(例如,自注意力,卷积),而CMixer通常是通过 inverted bottleneck 中的通道...
Conv3x3:普通的3x3卷积 + 激活函数(SiLU)+ BN Fused-MBConv1,k3×3:1表示expansion ratio,k表示kernel_size。 Fused-MBConv在论文中有SE模块,源码中没有。估计是SAN搜索后发现效果不好,删了 当expansion ratio等于1时是没有expand conv的 当stride=1且输入输出Channels相等时才有shortcut连接,shortcut连接时才...
为增强训练稳定性和速度,在每次卷积后应用批归一化(BN)和sigmoid线性单元(SiLU)。 PConv的第一层进行并行卷积,计算公式如下: \begin{align*} &X_{1}^{(h', w', c')}=SiLU(BN(X_{P(0,1,0,3)}^{(h_{1}, w_{1}, c_{1})} \otimes W_{1}^{(1,3, c')})),\\ &X_{2}^{(h'...
self.conv=nn.Sequential(nn.Conv2d(inc,outc,kernel_size=(num_param,1),stride=(num_param,1),bias=bias),nn.BatchNorm2d(outc),nn.SiLU())# the conv adds theBNand SiLU to compare original ConvinYOLOv5.self.p_conv=nn.Conv2d(inc,2*num_param,kernel_size=3,padding=1,stride=stride)nn.i...
(k,p),groups=g,bias=False)self.bn=nn.BatchNorm2d(c2)self.act=nn.SiLU()ifactisTrueelse(actifisinstance(act,nn.Module)elsenn.Identity())defforward(self,x):returnself.act(self.bn(self.conv(x)))defforward_fuse(self,x):returnself.act(self.conv(x))classDWConv(Conv):# Depth-wise ...
就是pytorch 自带的conv + BN +SiLU,这里对应上面的配置文件的Conv 的 args 比如[64, 3, 2] 就是 conv2d 的c2=64、k=3、 s =2、c1 自动为上一层参数、p 为自动计算,真实需要计算scales 里面的with 和 max_channels 缩放系数。 这里连续使用两个3*3卷积stride为2的CBS模块直接横竖各降低了4倍分辨率(...
(c2)self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())def forward(self, x):return self.act(self.bn(self.conv(x)))def forward_fuse(self, x):return self.act(self.conv(x))class DWConv(Conv):# Depth-wise convolution classdef __init...
bias=bias)self.bn=nn.BatchNorm2d(out_channels,eps=0.001,momentum=0.03)self.act=get_activation(act,inplace=True)defforward(self,x):returnself.act(self.bn(self.conv(x)))deffuseforward(self,x):returnself.act(self.conv(x))classSiLU(nn.Module):@staticmethoddefforward(x):returnx*torch....
nn.SiLU())# the conv adds the BN and SiLU to compare original Conv in YOLOv5.self.p_conv = nn.Conv2d(inc, 2 * num_param, kernel_size=3, padding=1, stride=stride) nn.init.constant_(self.p_conv.weight, 0)# self.p_conv.register_full_backward_hook(self._set_lr)@staticmethod ...
SiLU()) # the conv adds the BN and SiLU to compare original Conv in YOLOv5. self.p_conv = nn.Conv2d(inc, 2 * num_param, kernel_size=3, padding=1, stride=stride) nn.init.constant_(self.p_conv.weight, 0) self.p_conv.register_full_backward_hook(self._set_lr) @staticmethod def...