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VNet.py
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VNet.py
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# MIT License
#
# Copyright (c) 2018 Miguel Monteiro
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
import tensorflow as tf
from Layers import convolution, down_convolution, up_convolution, get_num_channels,prelu
def convolution_block(layer_input, num_convolutions, keep_prob, activation_fn, is_training):
x = layer_input
n_channels = get_num_channels(x)
for i in range(num_convolutions):
with tf.variable_scope('conv_' + str(i+1)):
x = convolution(x, [5, 5, 5, n_channels, n_channels])
layer_input = tf.layers.batch_normalization(layer_input, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
if i == num_convolutions - 1:
x = x + layer_input
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
def convolution_block_2(layer_input, fine_grained_features, num_convolutions, keep_prob, activation_fn, is_training):
x = tf.concat((layer_input, fine_grained_features), axis=-1)
n_channels = get_num_channels(layer_input)
if num_convolutions == 1:
with tf.variable_scope('conv_' + str(1)):
x = convolution(x, [5, 5, 5, n_channels * 2, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
layer_input = tf.layers.batch_normalization(layer_input, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = x + layer_input
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
with tf.variable_scope('conv_' + str(1)):
x = convolution(x, [5, 5, 5, n_channels * 2, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
for i in range(1, num_convolutions):
with tf.variable_scope('conv_' + str(i+1)):
x = convolution(x, [5, 5, 5, n_channels, n_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
layer_input = tf.layers.batch_normalization(layer_input, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
if i == num_convolutions - 1:
x = x + layer_input
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=is_training)
x = activation_fn(x)
x = tf.nn.dropout(x, keep_prob)
return x
class VNet(object):
def __init__(self,
num_classes,
keep_prob=1.0,
num_channels=16,
num_levels=4,
num_convolutions=(1, 2, 3, 3),
bottom_convolutions=3,
is_training = True,
activation_fn="relu"):
"""
Implements VNet architecture https://arxiv.org/abs/1606.04797
:param num_classes: Number of output classes.
:param keep_prob: Dropout keep probability, set to 1.0 if not training or if no dropout is desired.
:param num_channels: The number of output channels in the first level, this will be doubled every level.
:param num_levels: The number of levels in the network. Default is 4 as in the paper.
:param num_convolutions: An array with the number of convolutions at each level.
:param bottom_convolutions: The number of convolutions at the bottom level of the network.
:param activation_fn: The activation function.
"""
self.num_classes = num_classes
self.keep_prob = keep_prob
self.num_channels = num_channels
assert num_levels == len(num_convolutions)
self.num_levels = num_levels
self.num_convolutions = num_convolutions
self.bottom_convolutions = bottom_convolutions
self.is_training = is_training
self.train_phase = tf.placeholder(tf.bool,name="train_phase_placeholder")
if (activation_fn == "relu"):
self.activation_fn = tf.nn.relu
elif(activation_fn == "prelu"):
self.activation_fn = prelu
def network_fn(self, x):
keep_prob = self.keep_prob if self.is_training else 1.0
# if the input has more than 1 channel it has to be expanded because broadcasting only works for 1 input
# channel
input_channels = int(x.get_shape()[-1])
with tf.variable_scope('vnet/input_layer'):
if input_channels == 1:
x = tf.tile(x, [1, 1, 1, 1, self.num_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=self.train_phase)
else:
x = convolution(x, [5, 5, 5, input_channels, self.num_channels])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=self.train_phase)
x = self.activation_fn(x)
features = list()
for l in range(self.num_levels):
with tf.variable_scope('vnet/encoder/level_' + str(l + 1)):
x = convolution_block(x, self.num_convolutions[l], keep_prob, activation_fn=self.activation_fn, is_training=self.train_phase)
features.append(x)
with tf.variable_scope('down_convolution'):
x = down_convolution(x, factor=2, kernel_size=[2, 2, 2])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=self.train_phase)
x = self.activation_fn(x)
with tf.variable_scope('vnet/bottom_level'):
x = convolution_block(x, self.bottom_convolutions, keep_prob, activation_fn=self.activation_fn, is_training=self.train_phase)
for l in reversed(range(self.num_levels)):
with tf.variable_scope('vnet/decoder/level_' + str(l + 1)):
f = features[l]
with tf.variable_scope('up_convolution'):
x = up_convolution(x, tf.shape(f), factor=2, kernel_size=[2, 2, 2])
x = tf.layers.batch_normalization(x, momentum=0.99, epsilon=0.001,center=True, scale=True,training=self.train_phase)
x = self.activation_fn(x)
x = convolution_block_2(x, f, self.num_convolutions[l], keep_prob, activation_fn=self.activation_fn, is_training=self.train_phase)
with tf.variable_scope('vnet/output_layer'):
logits = convolution(x, [1, 1, 1, self.num_channels, self.num_classes])
logits = tf.layers.batch_normalization(logits, momentum=0.99, epsilon=0.001,center=True, scale=True,training=self.train_phase)
return logits