2318052dc79966bf36675606b7d992a347418292,gluoncv/model_zoo/resnet.py,BottleneckV1,__init__,#BottleneckV1#Any#Any#Any#Any#Any#Any#,133

Before Change

        super(BottleneckV1, self).__init__(**kwargs)
        self.body = nn.HybridSequential(prefix="")
        self.body.add(nn.Conv2D(channels//4, kernel_size=1, strides=stride))
        self.body.add(nn.BatchNorm())
        self.body.add(nn.Activation("relu"))
        self.body.add(_conv3x3(channels//4, 1, channels//4))
        self.body.add(nn.BatchNorm())
        self.body.add(nn.Activation("relu"))
        self.body.add(nn.Conv2D(channels, kernel_size=1, strides=1))

        if use_se:
            self.se = nn.HybridSequential(prefix="")
            self.se.add(nn.Dense(channels // 4, use_bias=False))
            self.se.add(nn.Activation("relu"))
            self.se.add(nn.Dense(channels * 4, use_bias=False))
            self.se.add(nn.Activation("sigmoid"))
        else:
            self.se = None

        if not last_gamma:
            self.body.add(nn.BatchNorm())
        else:
            self.body.add(nn.BatchNorm(gamma_initializer="zeros"))

        if downsample:
            self.downsample = nn.HybridSequential(prefix="")
            self.downsample.add(nn.Conv2D(channels, kernel_size=1, strides=stride,
                                          use_bias=False, in_channels=in_channels))

After Change

        Number of input channels. Default is 0, to infer from the graph.
    last_gamma : bool, default False
        Whether to initialize the gamma of the last BatchNorm layer in each bottleneck to zero.
    use_se : bool, default False
        Whether to use Squeeze-and-Excitation module
    norm_layer : object
        Normalization layer used (default: :class:`mxnet.gluon.nn.BatchNorm`)
        Can be :class:`mxnet.gluon.nn.BatchNorm` or :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    norm_kwargs : dict
        Additional `norm_layer` arguments, for example `num_devices=4`
        for :class:`mxnet.gluon.contrib.nn.SyncBatchNorm`.
    
    def __init__(self, channels, stride, downsample=False, in_channels=0,
                 last_gamma=False, use_se=False, norm_layer=BatchNorm, norm_kwargs=None, **kwargs):
        super(BottleneckV1, self).__init__(**kwargs)
        self.body = nn.HybridSequential(prefix="")
        self.body.add(nn.Conv2D(channels//4, kernel_size=1, strides=stride))
        self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
        self.body.add(nn.Activation("relu"))
        self.body.add(_conv3x3(channels//4, 1, channels//4))
        self.body.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))

Instances