2318052dc79966bf36675606b7d992a347418292,gluoncv/model_zoo/senet.py,SENet,__init__,#SENet#,114

Before Change

            self.features.add(nn.BatchNorm())
            self.features.add(nn.Activation("relu"))
            self.features.add(nn.Conv2D(channels * 2, 3, 1, 1, use_bias=False))
            self.features.add(nn.BatchNorm())
            self.features.add(nn.Activation("relu"))
            self.features.add(nn.MaxPool2D(3, 2, ceil_mode=True))

            for i, num_layer in enumerate(layers):
                stride = 1 if i == 0 else 2
                self.features.add(self._make_layer(channels, num_layer, stride, i+1))
                channels *= 2
            self.features.add(nn.GlobalAvgPool2D())
            self.features.add(nn.Dropout(0.2))

After Change

        with self.name_scope():
            self.features = nn.HybridSequential(prefix="")
            self.features.add(nn.Conv2D(channels, 3, 2, 1, use_bias=False))
            self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
            self.features.add(nn.Activation("relu"))
            self.features.add(nn.Conv2D(channels, 3, 1, 1, use_bias=False))
            self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
            self.features.add(nn.Activation("relu"))
            self.features.add(nn.Conv2D(channels * 2, 3, 1, 1, use_bias=False))
            self.features.add(norm_layer(**({} if norm_kwargs is None else norm_kwargs)))
            self.features.add(nn.Activation("relu"))
            self.features.add(nn.MaxPool2D(3, 2, ceil_mode=True))

            for i, num_layer in enumerate(layers):
                stride = 1 if i == 0 else 2
                self.features.add(self._make_layer(channels, num_layer, stride, i+1,
                                                   norm_layer=norm_layer, norm_kwargs=norm_kwargs))
                channels *= 2
            self.features.add(nn.GlobalAvgPool2D())
            self.features.add(nn.Dropout(0.2))

Instances