def network_fn(self, x, is_training):
keep_prob = self.keep_prob if 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])
else:
x = self.activation_fn(
convolution(x,
[5, 5, 5, input_channels, self.num_channels]))
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)
features.append(x)
with tf.variable_scope('down_convolution'):
x = self.activation_fn(
down_convolution(x, factor=2, kernel_size=[2, 2, 2]))
with tf.variable_scope('vnet/bottom_level'):
x = convolution_block(x,
self.bottom_convolutions,
keep_prob,
activation_fn=self.activation_fn)
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 = self.activation_fn(
up_convolution(x,
tf.shape(f),
factor=2,
kernel_size=[2, 2, 2]))
x = convolution_block_2(x,
f,
self.num_convolutions[l],
keep_prob,
activation_fn=self.activation_fn)
with tf.variable_scope('vnet/output_layer'):
logits = convolution(
x, [1, 1, 1, self.num_channels, self.num_classes])
return logits
def network_fn(self, x):
# keep_prob = self.keep_prob if self.is_training else 1.0
# use 0.0 for tf 1.15
# keep_prob = self.keep_prob if self.is_training else 0.0
keep_prob = self.keep_prob
# 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