8dce4fb6d91c6ee2101c3fba2e337aa3f4335f0c,gluon/models/darknet.py,DarkNet,__init__,#DarkNet#Any#Any#Any#Any#Any#Any#,70

Before Change

        with self.name_scope():
            self.features = nn.HybridSequential(prefix="")
            for i in range(len(channels)):
                stage = nn.HybridSequential(prefix="")
                channels_per_stage = channels[i]
                for j in range(len(channels_per_stage)):

After Change

            self.features = nn.HybridSequential(prefix="")
            for i, channels_per_stage in enumerate(channels):
                stage = nn.HybridSequential(prefix="stage{}_".format(i + 1))
                with stage.name_scope():
                    for j, out_channels in enumerate(channels_per_stage):
                        stage.add(dark_convYxY(
                            in_channels=in_channels,
                            out_channels=out_channels,
                            pointwise=(len(channels_per_stage) > 1) and not(((j + 1) % 2 == 1) ^ odd_pointwise)))
                        in_channels = out_channels
                    if i != len(channels) - 1:
                        stage.add(nn.MaxPool2D(
                            pool_size=2,
                            strides=2))
                self.features.add(stage)

            self.output = nn.HybridSequential(prefix="")
            self.output.add(nn.Conv2D(

Instances