Source code for niftynet.layer.squeeze_excitation
# -*- coding: utf-8 -*-
from __future__ import absolute_import, print_function
import tensorflow as tf
from niftynet.layer.base_layer import Layer
from niftynet.layer.fully_connected import FullyConnectedLayer
from niftynet.layer.convolution import ConvolutionalLayer
from niftynet.utilities.util_common import look_up_operations
SUPPORTED_OP = set(['AVG', 'MAX'])
[docs]class ChannelSELayer(Layer):
"""
Re-implementation of Squeeze-and-Excitation (SE) block described in::
Hu et al., Squeeze-and-Excitation Networks, arXiv:1709.01507
"""
def __init__(self,
func='AVG',
reduction_ratio=16,
name='channel_squeeze_excitation'):
self.func = func.upper()
self.reduction_ratio = reduction_ratio
super(ChannelSELayer, self).__init__(name=name)
look_up_operations(self.func, SUPPORTED_OP)
[docs] def layer_op(self, input_tensor):
# spatial squeeze
input_rank = len(input_tensor.shape)
reduce_indices = list(range(input_rank))[1:-1]
if self.func == 'AVG':
squeeze_tensor = tf.reduce_mean(input_tensor, axis=reduce_indices)
elif self.func == 'MAX':
squeeze_tensor = tf.reduce_max(input_tensor, axis=reduce_indices)
else:
raise NotImplementedError("pooling function not supported")
# channel excitation
num_channels = int(squeeze_tensor.shape[-1])
reduction_ratio = self.reduction_ratio
if num_channels % reduction_ratio != 0:
raise ValueError(
"reduction ratio incompatible with "
"number of input tensor channels")
num_channels_reduced = num_channels / reduction_ratio
fc1 = FullyConnectedLayer(num_channels_reduced,
with_bias=False,
feature_normalization=None,
acti_func='relu',
name='se_fc_1')
fc2 = FullyConnectedLayer(num_channels,
with_bias=False,
feature_normalization=None,
acti_func='sigmoid',
name='se_fc_2')
fc_out_1 = fc1(squeeze_tensor)
fc_out_2 = fc2(fc_out_1)
while len(fc_out_2.shape) < input_rank:
fc_out_2 = tf.expand_dims(fc_out_2, axis=1)
output_tensor = tf.multiply(input_tensor, fc_out_2)
return output_tensor
[docs]class SpatialSELayer(Layer):
"""
Re-implementation of SE block -- squeezing spatially
and exciting channel-wise described in::
Roy et al., Concurrent Spatial and Channel Squeeze & Excitation
in Fully Convolutional Networks, arXiv:1803.02579
"""
def __init__(self,
name='spatial_squeeze_excitation'):
super(SpatialSELayer, self).__init__(name=name)
[docs] def layer_op(self, input_tensor):
# channel squeeze
conv = ConvolutionalLayer(n_output_chns=1,
kernel_size=1,
feature_normalization=None,
acti_func='sigmoid',
name="se_conv")
squeeze_tensor = conv(input_tensor)
# spatial excitation
output_tensor = tf.multiply(input_tensor, squeeze_tensor)
return output_tensor
[docs]class ChannelSpatialSELayer(Layer):
"""
Re-implementation of concurrent spatial and channel
squeeze & excitation::
Roy et al., Concurrent Spatial and Channel Squeeze & Excitation
in Fully Convolutional Networks, arXiv:1803.02579
"""
def __init__(self,
func='AVG',
reduction_ratio=16,
name='channel_spatial_squeeze_excitation'):
self.func = func.upper()
self.reduction_ratio = reduction_ratio
super(ChannelSpatialSELayer, self).__init__(name=name)
look_up_operations(self.func, SUPPORTED_OP)
[docs] def layer_op(self, input_tensor):
cSE = ChannelSELayer(func=self.func,
reduction_ratio=self.reduction_ratio,
name='cSE')
sSE = SpatialSELayer(name='sSE')
output_tensor = tf.add(cSE(input_tensor), sSE(input_tensor))
return output_tensor