2318052dc79966bf36675606b7d992a347418292,gluoncv/model_zoo/se_resnet.py,SE_BasicBlockV2,__init__,#SE_BasicBlockV2#Any#Any#Any#Any#,175

Before Change

        self.se = nn.HybridSequential(prefix="")
        self.se.add(nn.Dense(channels//16, use_bias=False))
        self.se.add(nn.Activation("relu"))
        self.se.add(nn.Dense(channels, use_bias=False))
        self.se.add(nn.Activation("sigmoid"))

        if downsample:

After Change

                self.features.add(nn.MaxPool2D(3, 2, 1))

            in_channels = channels[0]
            for i, num_layer in enumerate(layers):
                stride = 1 if i == 0 else 2
                self.features.add(self._make_layer(block, num_layer, channels[i+1],
                                                   stride, i+1, in_channels=in_channels,
                                                   norm_layer=norm_layer, norm_kwargs=norm_kwargs))
                in_channels = channels[i+1]
            self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
            self.features.add(nn.Activation("relu"))
            self.features.add(nn.GlobalAvgPool2D())
            self.features.add(nn.Flatten())

            self.output = nn.Dense(classes, in_units=in_channels)

    def _make_layer(self, block, layers, channels, stride, stage_index, in_channels=0,
                    norm_layer=BatchNorm, norm_kwargs=None):
        layer = nn.HybridSequential(prefix="stage%d_"%stage_index)
        with layer.name_scope():
            layer.add(block(channels, stride, channels != in_channels, in_channels=in_channels,

Instances