ea5306a4e8fc9700022f93c3731fb1711e248a8e,gluoncv/model_zoo/resnext.py,Block,__init__,#Block#Any#Any#Any#Any#Any#Any#Any#Any#Any#,60

Before Change

        group_width = cardinality * D

        self.body = nn.HybridSequential(prefix="")
        self.body.add(nn.Conv2D(group_width, kernel_size=1, use_bias=False))
        self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
        self.body.add(nn.Activation("relu"))
        self.body.add(nn.Conv2D(group_width, kernel_size=3, strides=stride, padding=1,

After Change

        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    

    def __init__(self, channels, cardinality, bottleneck_width, stride,
                 downsample=False, last_gamma=False, use_se=False, avg_down=False,
                 norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
        super(Block, self).__init__(**kwargs)
        D = int(math.floor(channels * (bottleneck_width / 64)))

Instances