PyTorch实现的ResNeXt

PyTorch实现的ResNeXt

Aggregated Residual Transformations for Deep Neural Networks PDF: ​​​PyTorch代码: ​​ResNeXtBlock(nn.Module): def __init__(self,in_places,places, stride=1,downsampling=False, expansion = 2, cardinality=32): super(ResNeXtBlock,self).__init__() self.expansion = expansion self.downsampling = downsampling self.bottleneck = nn.Sequential( nn.Conv2d(in_channels=in_places, out_channels=places, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(places), nn.ReLU(inplace=True), nn.Conv2d(in_channels=places, out_channels=places, kernel_size=3, stride=stride, padding=1, bias=False, groups=cardinality), nn.BatchNorm2d(places), nn.ReLU(inplace=True), nn.Conv2d(in_channels=places, out_channels=places * self.expansion, kernel_size=1, stride=1, bias=False), nn.BatchNorm2d(places * self.expansion), ) if self.downsampling: self.downsample = nn.Sequential( nn.Conv2d(in_channels=in_places, out_channels=places * self.expansion, kernel_size=1, stride=stride,bias=False), nn.BatchNorm2d(places * self.expansion) ) self.relu = nn.ReLU(inplace=True) def forward(self, x): residual = x out = self.bottleneck(x) if self.downsampling: residual = self.downsample(x) out += residual out = self.relu(out) return

可以直接将VGG 和 ResNet中的Block换做 ResNeXt block

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