updated squeeze and excitation layer name from SELayer to ChannelSELayer in line with layer module
@@ -10,7 +10,7 @@ from niftynet.layer.bn import BNLayer
from niftynet.layer.fully_connected import FCLayer
from niftynet.layer.base_layer import TrainableLayer
from niftynet.layer.convolution import ConvolutionalLayer
-from niftynet.layer.squeeze_excitation_layer import SELayer
+from niftynet.layer.squeeze_excitation_layer import ChannelSELayer
from niftynet.network.base_net import BaseNet
SE_ResNetDesc = namedtuple('SE_ResNetDesc', ['bn', 'fc', 'conv1', 'blocks'])
@@ -98,7 +98,7 @@ class BottleneckBlock(TrainableLayer):
def layer_op(self, images, is_training=True):
layers = self.create(images.shape[-1])
- se=SELayer()
+ se=ChannelSELayer()
if self.n_output_chns == images.shape[-1]:
out=layers.conv[0](images, is_training)
out=layers.conv[1](out, is_training)
