2318052dc79966bf36675606b7d992a347418292,gluoncv/model_zoo/resnext.py,Block,__init__,#Block#Any#Any#Any#Any#Any#Any#Any#,53

Before Change

        self.body = nn.HybridSequential(prefix="")
        self.body.add(nn.Conv2D(group_width, kernel_size=1, use_bias=False))
        self.body.add(nn.BatchNorm())
        self.body.add(nn.Activation("relu"))
        self.body.add(nn.Conv2D(group_width, kernel_size=3, strides=stride, padding=1,
                                groups=cardinality, use_bias=False))
        self.body.add(nn.BatchNorm())
        self.body.add(nn.Activation("relu"))
        self.body.add(nn.Conv2D(channels * 4, kernel_size=1, use_bias=False))
        if last_gamma:
            self.body.add(nn.BatchNorm())
        else:
            self.body.add(nn.BatchNorm(gamma_initializer="zeros"))

        if use_se:
            self.se = nn.HybridSequential(prefix="")
            self.se.add(nn.Conv2D(channels // 4, kernel_size=1, padding=0))
            self.se.add(nn.Activation("relu"))
            self.se.add(nn.Conv2D(channels * 4, kernel_size=1, padding=0))
            self.se.add(nn.Activation("sigmoid"))
        else:
            self.se = None

        if downsample:
            self.downsample = nn.HybridSequential(prefix="")
            self.downsample.add(nn.Conv2D(channels * 4, kernel_size=1, strides=stride,
                                          use_bias=False))
            self.downsample.add(nn.BatchNorm())
        else:
            self.downsample = None

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,
                 norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
        super(Block, self).__init__(**kwargs)
        D = int(math.floor(channels * (bottleneck_width / 64)))
        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,
                                groups=cardinality, use_bias=False))
        self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
        self.body.add(nn.Activation("relu"))

Instances