self.dropblock_prob=dropblock_probifself.rectify:from rfconvimportRFConv2d self.conv=RFConv2d(in_channels,channels*radix,kernel_size,stride,padding,dilation,groups=groups*radix,bias=bias,average_mode=rectify_avg,**kwargs)else:self.conv=Conv2d(in_channels,channels*radix,kernel_size,stride,padding,di...
self.dropblock_prob=dropblock_probifself.rectify:fromrfconvimportRFConv2d self.conv= RFConv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation, groups=groups*radix, bias=bias, average_mode=rectify_avg, **kwargs)else: self.conv= Conv2d(in_channels, channels*radix, kernel_si...
self.relu= ReLU(inplace=True) self.fc1= Conv2d(channels, inter_channels, 1, groups=self.cardinality) self.bn1=norm_layer(inter_channels) self.fc2= Conv2d(inter_channels, channels*radix, 1, groups=self.cardinality)ifdropblock_prob > 0.0: self.dropblock= DropBlock2D(dropblock_prob, 3)deffor...
out=self.conv1(x)out=self.conv2(out)out=self.conv3(out)out=self.bn(out)out+=identity out=self.relu(out)returnoutclassResNeSt(nn.Module):def__init__(self,num_classes):super(ResNeSt,self).__init__()self.stem=nn.Sequential(nn.Conv2d(3,64,kernel_size=7,stride=2,padding,nn.BatchNorm...
(ngf)self.im=nn.Conv2d(ngf,3,1)defforward(self,input):img,mask=input[:,0:3,:,:],input[:,3:4,:,:]# U-Net generator with skip connections from encoder to decoderx,d1=self.down1(input)# 128,256x,d2=self.down2(x)# 64,128x,d3=self.down3(x)# 32,64x,d4=self.down4(...
第一个贡献点:提出了split-attention blocks构造的ResNeSt,与现有的ResNet变体相比,不需要增加额外的计算量。而且ResNeSt可以作为其它任务的骨架。 第二个贡献点:图像分类和迁移学习应用的大规模基准。 利用ResNeSt主干的模型能够在几个任务上达到最先进的性能,即:图像分类,对象检测,实例分割和语义分割。 与通过神经架构...
ResNet-D的技巧:替换7x7卷积为3x3;shortcut stride=2时在1x1 conv前加avg pool避免信息丢失 Label Smoothing、MixUp、DropBlock等 四、评价 本文属于将Attention机制用在CNN上的又一大尝试,在SENet和SKNet基础上将attention进一步推广到group(cardinality)维度。