056338255ea968d28ab462295b308cf475adcdde,chainer_/models/dpn.py,DPN,__init__,#DPN#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#Any#,358

Before Change

                self.features.add(stage)
            self.features.add(DPNFinalBlock(channels=in_channels))

            self.output = nn.HybridSequential(prefix="")
            if for_training or not test_time_pool:
                self.output.add(nn.GlobalAvgPool2D())
                self.output.add(conv1x1(
                    in_channels=in_channels,
                    out_channels=classes,
                    use_bias=True))
                self.output.add(nn.Flatten())
            else:
                self.output.add(nn.AvgPool2D(
                    pool_size=7,
                    strides=1))
                self.output.add(conv1x1(
                    in_channels=in_channels,
                    out_channels=classes,
                    use_bias=True))
                self.output.add(GlobalAvgMaxPool2D())
                self.output.add(nn.Flatten())

    def hybrid_forward(self, F, x):
        x = self.features(x)
        x = self.output(x)

After Change

                        for j, out_channels in enumerate(channels_per_stage):
                            has_proj = (j == 0)
                            key_strides = 2 if (j == 0) and (i != 0) else 1
                            setattr(stage, "unit{}".format(j + 1), DPNUnit(
                                in_channels=in_channels,
                                mid_channels=r,
                                bw=bw,

Instances