b89b592181e06850fa6eae6be04c2f8ec3b7fdaf,gluon/models/preresnet.py,PreResNet,__init__,#PreResNet#Any#Any#Any#Any#Any#Any#Any#,386

Before Change

                 **kwargs):
        super(PreResNet, self).__init__(**kwargs)

        with self.name_scope():
            self.features = nn.HybridSequential(prefix="")
            self.features.add(PreResInitBlock(
                in_channels=in_channels,
                out_channels=init_block_channels,
                bn_use_global_stats=bn_use_global_stats))
            in_channels = init_block_channels
            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):
                        strides = 2 if (j == 0) and (i != 0) else 1
                        stage.add(PreResUnit(
                            in_channels=in_channels,
                            out_channels=out_channels,
                            strides=strides,
                            bn_use_global_stats=bn_use_global_stats,
                            bottleneck=bottleneck,
                            conv1_stride=conv1_stride))
                        in_channels = out_channels
                self.features.add(stage)
            self.features.add(PreResActivation(
                in_channels=in_channels,
                bn_use_global_stats=bn_use_global_stats))
            self.features.add(nn.AvgPool2D(
                pool_size=7,
                strides=1))

            self.output = nn.HybridSequential(prefix="")
            self.output.add(nn.Flatten())
            self.output.add(nn.Dense(
                units=classes,
                in_units=in_channels))

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

After Change

    out_channels : int
        Number of output channels.
    bn_use_global_stats : bool
        Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
    
    def __init__(self,
                 in_channels,
                 out_channels,
                 bn_use_global_stats,
                 **kwargs):
        super(PreResInitBlock, self).__init__(**kwargs)
        with self.name_scope():
            self.conv = nn.Conv2D(
                channels=out_channels,
                kernel_size=7,
                strides=2,
                padding=3,
                use_bias=False,
                in_channels=in_channels)
            self.bn = nn.BatchNorm(
                in_channels=out_channels,
                use_global_stats=bn_use_global_stats)
            self.activ = nn.Activation("relu")
            self.pool = nn.MaxPool2D(
                pool_size=3,
                strides=2,
                padding=1)

    def hybrid_forward(self, F, x):
        x = self.conv(x)
        x = self.bn(x)
        x = self.activ(x)
        x = self.pool(x)
        return x


class PreResActivation(HybridBlock):
    
    PreResNet pure pre-activation block without convolution layer. It"s used by itself as the final block.

    Parameters:
    ----------
    in_channels : int
        Number of input channels.
    bn_use_global_stats : bool
        Whether global moving statistics is used instead of local batch-norm for BatchNorm layers.
    
    def __init__(self,
                 in_channels,
                 bn_use_global_stats,
                 **kwargs):
        super(PreResActivation, self).__init__(**kwargs)
        with self.name_scope():
            self.bn = nn.BatchNorm(
                in_channels=in_channels,

Instances