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custom_layers.py
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custom_layers.py
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'''
This is a part of the supplementary material uploaded along with
the manuscript:
"Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks"
M. Anthimopoulos, S. Christodoulidis, L. Ebner, A. Christe and S. Mougiakakou
IEEE Journal of Biomedical and Health infomatics (2018)
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
For more information please read the README file. The files can also
be found at: https://github.com/intact-project/LungNet
'''
from keras.engine import Layer, InputSpec
from keras import regularizers
from keras import initializers
from keras import backend as K
class Softmax4D(Layer):
"""
This class was retrieved and modified from
https://github.com/keras-team/keras/tree/1.2.1
licensed under The MIT License (MIT) as defined
here https://github.com/keras-team/keras/blob/1.2.1/LICENSE
Softmax activation function.
# Arguments
x : Tensor.
axis: Integer, axis along which the softmax normalization is applied.
# Returns
Tensor, output of softmax transformation.
# Raises
ValueError: In case `dim(x) == 1`.
"""
def __init__(self, axis=-1,**kwargs):
self.axis=axis
super(Softmax4D, self).__init__(**kwargs)
def build(self,input_shape):
pass
def call(self, x, mask=None):
e = K.exp(x - K.max(x, axis=self.axis, keepdims=True))
s = K.sum(e, axis=self.axis, keepdims=True)
return K.clip(e / s, 10e-8, 1.0 - 10e-8)
def compute_output_shape(self, input_shape):
return input_shape
class BatchNormalization(Layer):
"""
This class was retrieved and modified from
https://github.com/keras-team/keras/tree/1.2.1
licensed under The MIT License (MIT) as defined
here https://github.com/keras-team/keras/blob/1.2.1/LICENSE
Batch normalization layer (Ioffe and Szegedy, 2014).
Normalize the activations of the previous layer at each batch,
i.e. applies a transformation that maintains the mean activation
close to 0 and the activation standard deviation close to 1.
# Arguments
epsilon: small float > 0. Fuzz parameter.
Theano expects epsilon >= 1e-5.
mode: integer, 0, 1 or 2.
- 0: feature-wise normalization.
Each feature map in the input will
be normalized separately. The axis on which
to normalize is specified by the `axis` argument.
Note that if the input is a 4D image tensor
using Theano conventions (samples, channels, rows, cols)
then you should set `axis` to `1` to normalize along
the channels axis.
During training we use per-batch statistics to normalize
the data, and during testing we use running averages
computed during the training phase.
- 1: sample-wise normalization. This mode assumes a 2D input.
- 2: feature-wise normalization, like mode 0, but
using per-batch statistics to normalize the data during both
testing and training.
axis: integer, axis along which to normalize in mode 0. For instance,
if your input tensor has shape (samples, channels, rows, cols),
set axis to 1 to normalize per feature map (channels axis).
momentum: momentum in the computation of the
exponential average of the mean and standard deviation
of the data, for feature-wise normalization.
weights: Initialization weights.
List of 2 Numpy arrays, with shapes:
`[(input_shape,), (input_shape,)]`
Note that the order of this list is [gamma, beta, mean, std]
beta_init: name of initialization function for shift parameter
(see [initializations](../initializations.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
gamma_init: name of initialization function for scale parameter (see
[initializations](../initializations.md)), or alternatively,
Theano/TensorFlow function to use for weights initialization.
This parameter is only relevant if you don't pass a `weights` argument.
gamma_regularizer: instance of [WeightRegularizer](../regularizers.md)
(eg. L1 or L2 regularization), applied to the gamma vector.
beta_regularizer: instance of [WeightRegularizer](../regularizers.md),
applied to the beta vector.
# Input shape
Arbitrary. Use the keyword argument `input_shape`
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
# Output shape
Same shape as input.
# References
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
"""
def __init__(self, epsilon=1e-3, mode=0, axis=-1, momentum=0.99,
weights=None, beta_init='zero', gamma_init='one',
gamma_regularizer=None, beta_regularizer=None, **kwargs):
self.supports_masking = True
self.beta_init = initializers.get(beta_init)
self.gamma_init = initializers.get(gamma_init)
self.epsilon = epsilon
self.mode = mode
self.axis = axis
self.momentum = momentum
self.gamma_regularizer = regularizers.get(gamma_regularizer)
self.beta_regularizer = regularizers.get(beta_regularizer)
self.initial_weights = weights
if self.mode == 0:
self.uses_learning_phase = True
super(BatchNormalization, self).__init__(**kwargs)
def build(self, input_shape):
self.input_spec = [InputSpec(shape=input_shape)]
shape = (input_shape[self.axis],)
self.gamma = self.add_weight(shape,
initializer=self.gamma_init,
regularizer=self.gamma_regularizer,
name='{}_gamma'.format(self.name))
self.beta = self.add_weight(shape,
initializer=self.beta_init,
regularizer=self.beta_regularizer,
name='{}_beta'.format(self.name))
self.running_mean = self.add_weight(shape, initializer='zero',
name='{}_running_mean'.format(self.name),
trainable=False)
self.running_std = self.add_weight(shape, initializer='one',
name='{}_running_std'.format(self.name),
trainable=False)
if self.initial_weights is not None:
self.set_weights(self.initial_weights)
del self.initial_weights
self.built = True
def call(self, x, mask=None):
if self.mode == 0 or self.mode == 2:
assert self.built, 'Layer must be built before being called'
input_shape = K.int_shape(x)
reduction_axes = list(range(len(input_shape)))
del reduction_axes[self.axis]
broadcast_shape = [1] * len(input_shape)
broadcast_shape[self.axis] = input_shape[self.axis]
x_normed, mean, std = K.normalize_batch_in_training(
x, self.gamma, self.beta, reduction_axes,
epsilon=self.epsilon)
if self.mode == 0:
self.add_update([K.moving_average_update(self.running_mean, mean, self.momentum),
K.moving_average_update(self.running_std, std, self.momentum)], x)
if sorted(reduction_axes) == range(K.ndim(x))[:-1]:
x_normed_running = K.batch_normalization(
x, self.running_mean, self.running_std,
self.beta, self.gamma,
epsilon=self.epsilon)
else:
# need broadcasting
broadcast_running_mean = K.reshape(self.running_mean, broadcast_shape)
broadcast_running_std = K.reshape(self.running_std, broadcast_shape)
broadcast_beta = K.reshape(self.beta, broadcast_shape)
broadcast_gamma = K.reshape(self.gamma, broadcast_shape)
x_normed_running = K.batch_normalization(
x, broadcast_running_mean, broadcast_running_std,
broadcast_beta, broadcast_gamma,
epsilon=self.epsilon)
# pick the normalized form of x corresponding to the training phase
x_normed = K.in_train_phase(x_normed, x_normed_running)
elif self.mode == 1:
# sample-wise normalization
m = K.mean(x, axis=-1, keepdims=True)
std = K.sqrt(K.var(x, axis=-1, keepdims=True) + self.epsilon)
x_normed = (x - m) / (std + self.epsilon)
x_normed = self.gamma * x_normed + self.beta
return x_normed
def get_config(self):
config = {
'epsilon': self.epsilon,
'mode': self.mode,
'axis': self.axis,
'momentum': self.momentum,
'gamma_regularizer': regularizers.serialize(self.gamma_regularizer),
'beta_regularizer': regularizers.serialize(self.beta_regularizer)
}
base_config = super(BatchNormalization, self).get_config()
return dict(list(base_config.items()) + list(config.items()))