def get_active_subnet()

in models/modules/dynamic_layers.py [0:0]

• 34 lines of code
• 4 McCabe index (conditional complexity)

    def get_active_subnet(self, in_channel, preserve_weight=True):
        middle_channel = make_divisible(round(in_channel * self.active_expand_ratio), 8)
        channels_per_group = self.depth_conv.conv.channels_per_group

        # build the new layer
        sub_layer = MBInvertedConvLayer(
            in_channel, self.active_out_channel, self.active_kernel_size, self.stride, self.active_expand_ratio,
            act_func=self.act_func, mid_channels=middle_channel, use_se=self.use_se, channels_per_group=channels_per_group
        )
        sub_layer = sub_layer.to(get_net_device(self))

        if not preserve_weight:
            return sub_layer

        # copy weight from current layer
        if sub_layer.inverted_bottleneck is not None:
            sub_layer.inverted_bottleneck.conv.weight.data.copy_(
                self.inverted_bottleneck.conv.conv.weight.data[:middle_channel, :in_channel, :, :]
            )
            copy_bn(sub_layer.inverted_bottleneck.bn, self.inverted_bottleneck.bn.bn)

        sub_layer.depth_conv.conv.weight.data.copy_(
            self.depth_conv.conv.get_active_filter(middle_channel, self.active_kernel_size).data
        )
        copy_bn(sub_layer.depth_conv.bn, self.depth_conv.bn.bn)

        if self.use_se:
            se_mid = make_divisible(middle_channel // SELayer.REDUCTION, divisor=8)
            sub_layer.depth_conv.se.fc.reduce.weight.data.copy_(
                self.depth_conv.se.fc.reduce.weight.data[:se_mid, :middle_channel, :, :]
            )
            sub_layer.depth_conv.se.fc.reduce.bias.data.copy_(self.depth_conv.se.fc.reduce.bias.data[:se_mid])

            sub_layer.depth_conv.se.fc.expand.weight.data.copy_(
                self.depth_conv.se.fc.expand.weight.data[:middle_channel, :se_mid, :, :]
            )
            sub_layer.depth_conv.se.fc.expand.bias.data.copy_(self.depth_conv.se.fc.expand.bias.data[:middle_channel])

        sub_layer.point_linear.conv.weight.data.copy_(
            self.point_linear.conv.conv.weight.data[:self.active_out_channel, :middle_channel, :, :]
        )
        copy_bn(sub_layer.point_linear.bn, self.point_linear.bn.bn)

        return sub_layer