def __init__(self, input_tensor, encoder):
net = input_tensor
with tf.variable_scope('decoder'):
with tf.variable_scope('layer-1'):
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=4, num_atoms=4, upsamp_type='deconv',
scaling=2, padding='same', routings=3)(net)
net = layers.Concatenate(axis=-2)([net, encoder.l3])
self.l1 = net
with tf.variable_scope('layer-2'):
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=4, num_atoms=2, upsamp_type='deconv',
scaling=2, padding='same', routings=3)(net)
net = layers.Concatenate(axis=-2)([net, encoder.l2])
self.l2 = net
with tf.variable_scope('layer-3'):
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=1, num_atoms=2, upsamp_type='deconv',
scaling=2, padding='same', routings=3)(net)
net = layers.Concatenate(axis=-2)([net, encoder.l1])
self.l3 = net
with tf.variable_scope('layer-4'):
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=1, num_atoms=2, upsamp_type='deconv',
scaling=1, padding='same', routings=3)(net)
net = layers.Reshape((encoder.H.value, encoder.W.value, encoder.C.value))(net)
self.output = net
def __init__(self, input_tensor, encoder):
net = input_tensor
with tf.variable_scope('decoder'):
with tf.variable_scope('layer-1'):
net = capsule_layers.DeconvCapsuleLayer(
kernel_size=4,
num_capsule=8,
num_atoms=2,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='deconv_cap_1_1')(net)
self.l1 = net
with tf.variable_scope('layer-2'):
net = capsule_layers.DeconvCapsuleLayer(
kernel_size=4,
num_capsule=1,
num_atoms=2,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='deconv_cap_1_1')(net)
net = layers.Reshape(
(encoder.H.value, encoder.W.value, encoder.C.value))(net)
self.output = net
def __init__(self, input_tensor, encoder, is_training, reuse):
net = input_tensor
with tf.variable_scope('Decoder'):
# Layer 1 Up: Deconvolutional capsules, skip connection, convolutional capsules
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=8, num_atoms=16, upsamp_type='deconv',
scaling=2, padding='same', routings=3, name='deconv_cap_1')(net)
net = mf.batch_norm(net, is_training=is_training, reuse=reuse)
self.upcap_1 = net
net = layers.Concatenate(axis=-2, name='skip_1')([net, encoder.conv_cap_3])
# Layer 2 Up: Deconvolutional capsules, skip connection, convolutional capsules
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=4, num_atoms=8, upsamp_type='deconv',
scaling=2, padding='same', routings=3, name='deconv_cap_2')(net)
net = mf.batch_norm(net, is_training=is_training, reuse=reuse)
self.upcap_2 = net
net = layers.Concatenate(axis=-2, name='skip_2')([net, encoder.conv_cap_2])
# Layer 3 Up: Deconvolutional capsules, skip connection
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=2, num_atoms=8, upsamp_type='deconv',
scaling=2, padding='same', routings=3, name='deconv_cap_3')(net)
net = mf.batch_norm(net, is_training=is_training, reuse=reuse)
self.upcap_3 = net
net = layers.Concatenate(axis=-2, name='skip_3')([net, encoder.primary_caps])
# Layer 4 Up: Deconvolutional capsules, skip connection
net = capsule_layers.DeconvCapsuleLayer(kernel_size=4, num_capsule=1, num_atoms=16, upsamp_type='deconv',
scaling=2, padding='same', routings=3, name='deconv_cap_4')(net)
self.upcap_4 = net
# Reconstruction - Reshape, skip connection + 3x conventional Conv2D layers
_, H, W, C, D = net.get_shape()
net = layers.Reshape((H.value, W.value, D.value))(net)
net = layers.Concatenate(axis=-1, name='skip_4')([net, encoder.conv1])
net = layers.Conv2D(filters=64, kernel_size=1, padding='same', kernel_initializer='he_normal',
activation='relu', name='recon_1')(net)
net = layers.Conv2D(filters=128, kernel_size=1, padding='same', kernel_initializer='he_normal',
activation='relu', name='recon_2')(net)
if tf.rank(encoder.input_tensor) == 3:
self.out_depth = 1
else:
self.out_depth = encoder.input_tensor.shape[3].value
net = layers.Conv2D(filters=self.out_depth, kernel_size=1, padding='same', kernel_initializer='he_normal',
activation='sigmoid', name='out_recon')(net)
self.output = net
def __init__(self, input_tensor, encoder, is_training, reuse):
net = input_tensor
with tf.variable_scope('Decoder'):
with tf.variable_scope('UpCaps_1'):
# Layer 1 Up: Deconvolutional capsules, skip connection, convolutional capsules
net = capsule_layers.DeconvCapsuleLayer(
kernel_size=4,
num_capsule=8,
num_atoms=32,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='deconv_cap_1_1')(net)
self.upcap_1_1 = net
net = layers.Concatenate(
axis=-2, name='up_1')([net, encoder.conv_cap_3_1])
net = capsule_layers.ConvCapsuleLayer(
kernel_size=5,
num_capsule=4,
num_atoms=32,
strides=1,
padding='same',
routings=3,
name='deconv_cap_1_2')(net)
self.upcap_1_2 = net
with tf.variable_scope('UpCaps_2'):
# Layer 2 Up: Deconvolutional capsules, skip connection, convolutional capsules
net = capsule_layers.DeconvCapsuleLayer(
kernel_size=4,
num_capsule=4,
num_atoms=16,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='deconv_cap_2_1')(net)
self.upcap_2_1 = net
net = layers.Concatenate(
axis=-2, name='up_2')([net, encoder.conv_cap_2_1])
net = capsule_layers.ConvCapsuleLayer(
kernel_size=5,
num_capsule=4,
num_atoms=16,
strides=1,
padding='same',
routings=3,
name='deconv_cap_2_2')(net)
self.upcap_2_2 = net
with tf.variable_scope('UpCaps_3'):
# Layer 3 Up: Deconvolutional capsules, skip connection
net = capsule_layers.DeconvCapsuleLayer(
kernel_size=4,
num_capsule=2,
num_atoms=16,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='deconv_cap_3_1')(net)
self.upcap_3_1 = net
net = layers.Concatenate(axis=-2,
name='up_3')([net, encoder.conv1])
with tf.variable_scope('Reconstruction'):
# Layer 4: Reconstruction - Convolutional Capsule: 1x1, 3x conventional Conv2D layers
net = capsule_layers.ConvCapsuleLayer(kernel_size=1,
num_capsule=1,
num_atoms=16,
strides=1,
padding='same',
routings=3,
name='seg_caps')(net)
_, H, W, C, D = net.get_shape()
net = layers.Reshape((H.value, W.value, D.value))(net)
net = layers.Conv2D(filters=64,
kernel_size=1,
padding='same',
kernel_initializer='he_normal',
activation='relu',
name='recon_1')(net)
net = layers.Conv2D(filters=128,
kernel_size=1,
padding='same',
kernel_initializer='he_normal',
activation='relu',
name='recon_2')(net)
net = layers.Conv2D(filters=1,
kernel_size=1,
padding='same',
kernel_initializer='he_normal',
activation='sigmoid',
name='out_recon')(net)
self.output = net
def __init__(self, input_tensor, encoder, is_training, reuse):
net = input_tensor
with tf.variable_scope('Decoder'):
#net = capsule_layers.DeconvCapsuleLayer(kernel_size=5, num_capsule=32, num_atoms=2, upsamp_type='deconv',
# scaling=2, padding='same', routings=3,
# name='caps_deconv5')(net)
#self.upcap_1 = net
#net = layers.Concatenate(axis=3, name='up4')([net, encoder.caps_conv5])
#net = capsule_layers.DeconvCapsuleLayer(kernel_size=5, num_capsule=16, num_atoms=2, upsamp_type='deconv',
# scaling=2, padding='same', routings=3,
# name='caps_deconv4')(net)
#self.upcap_2 = net
#net = layers.Concatenate(axis=3, name='up3')([net, encoder.caps_conv4])
net = capsule_layers.DeconvCapsuleLayer(kernel_size=5,
num_capsule=8,
num_atoms=8,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='caps_deconv3')(net)
net = mf.batch_norm(net, is_training=is_training, reuse=reuse)
self.upcap_3 = net
net = layers.Concatenate(axis=3,
name='up2')([net, encoder.caps_conv3])
net = capsule_layers.DeconvCapsuleLayer(kernel_size=5,
num_capsule=4,
num_atoms=4,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='caps_deconv2')(net)
net = mf.batch_norm(net, is_training=is_training, reuse=reuse)
self.upcap_4 = net
net = layers.Concatenate(axis=3,
name='up1')([net, encoder.caps_conv2])
net = capsule_layers.DeconvCapsuleLayer(kernel_size=5,
num_capsule=2,
num_atoms=2,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='caps_deconv1')(net)
net = mf.batch_norm(net, is_training=is_training, reuse=reuse)
self.upcap_5 = net
net = layers.Concatenate(axis=3,
name='up0')([net, encoder.caps_conv1])
net = capsule_layers.DeconvCapsuleLayer(kernel_size=5,
num_capsule=1,
num_atoms=2,
upsamp_type='deconv',
scaling=2,
padding='same',
routings=3,
name='caps_deconv0')(net)
self.upcap_6 = net
_, H, W, C, A = net.get_shape()
net = layers.Reshape((H.value, W.value, A.value))(net)
#net = layers.Concatenate(axis=-1, name='skip_4')([net, encoder.conv1])
net = layers.Conv2D(filters=64,
kernel_size=1,
padding='same',
kernel_initializer='he_normal',
activation='relu',
name='recon_1')(net)
net = layers.Conv2D(filters=128,
kernel_size=1,
padding='same',
kernel_initializer='he_normal',
activation='relu',
name='recon_2')(net)
if tf.rank(encoder.input_tensor) == 3:
self.out_depth = 1
else:
self.out_depth = encoder.input_tensor.shape[3].value
net = layers.Conv2D(filters=self.out_depth,
kernel_size=1,
padding='same',
kernel_initializer='he_normal',
activation='sigmoid',
name='out_recon')(net)
self.output = net
