def denseConvlayer(layer_inputs, bottleneck_scale, growth_rate, is_training):
# Build the bottleneck operation
net = layer_inputs
net_temp = tf.identity(net)
net = batchnorm(net, is_training)
net = prelu_tf(net, name='Prelu_1')
net = conv2(net,
kernel=1,
output_channel=bottleneck_scale * growth_rate,
stride=1,
use_bias=False,
scope='conv1x1')
net = batchnorm(net, is_training)
net = prelu_tf(net, name='Prelu_2')
net = conv2(net,
kernel=3,
output_channel=growth_rate,
stride=1,
use_bias=False,
scope='conv3x3')
# Concatenate the processed feature to the feature
net = tf.concat([net_temp, net], axis=3)
return net
def generatorDense(gen_inputs, gen_output_channels, reuse=False, is_training=None):
# The main netowrk
with tf.variable_scope('generator_unit', reuse=reuse):
# The input stage
with tf.variable_scope('input_stage'):
net = conv2(gen_inputs, 9, 64, 1, scope='conv')
net = prelu_tf(net)
# The dense block part
# Define the denseblock configuration
layer_per_block = 16
bottleneck_scale = 4
growth_rate = 12
transition_output_channel = 128
with tf.variable_scope('denseBlock_1'):
net = denseBlock(net, layer_per_block, bottleneck_scale, growth_rate, is_training)
with tf.variable_scope('transition_layer_1'):
net = transitionLayer(net, transition_output_channel, is_training)
with tf.variable_scope('subpixelconv_stage1'):
net = conv2(net, 3, 256, 1, scope='conv')
net = pixelShuffler(net, scale=2)
net = prelu_tf(net)
with tf.variable_scope('subpixelconv_stage2'):
net = conv2(net, 3, 256, 1, scope='conv')
net = pixelShuffler(net, scale=2)
net = prelu_tf(net)
with tf.variable_scope('output_stage'):
net = conv2(net, 9, gen_output_channels, 1, scope='conv')
return net
def transitionLayer(layer_inputs, output_channel, is_training):
net = layer_inputs
# net = batchnorm(net, is_training)
net = prelu_tf(net)
net = conv2(net, 1, output_channel, stride=1, use_bias=False, scope='conv1x1')
return net
def residual_block(inputs, output_channel, stride, scope):
with tf.variable_scope(scope):
net = conv2(inputs,
3,
output_channel,
stride,
use_bias=False,
scope='conv_1')
net = prelu_tf(net)
net = conv2(net,
3,
output_channel,
stride,
use_bias=False,
scope='conv_2')
net = net + inputs
return net
def residual_block(inputs, output_channels, stride, scope):
with tf.variable_scope(scope):
net = ops.conv3d(inputs,
3,
output_channels,
stride,
use_bias=False,
scope='conv_1')
if (FLAGS.GAN_type == 'GAN'):
net = ops.batchnorm(net, FLAGS.is_training)
net = ops.prelu_tf(net)
net = ops.conv3d(net,
3,
output_channels,
stride,
use_bias=False,
scope='conv_2')
if (FLAGS.GAN_type == 'GAN'):
net = ops.batchnorm(net, FLAGS.is_training)
net = net + inputs
return net
def generator(gen_inputs, gen_output_channels, reuse=False, FLAGS=None):
# Check the flag
if FLAGS is None:
raise ValueError('No FLAGS is provided for generator')
# The Bx residual blocks
def residual_block(inputs, output_channels, stride, scope):
with tf.variable_scope(scope):
net = ops.conv3d(inputs,
3,
output_channels,
stride,
use_bias=False,
scope='conv_1')
if (FLAGS.GAN_type == 'GAN'):
net = ops.batchnorm(net, FLAGS.is_training)
net = ops.prelu_tf(net)
net = ops.conv3d(net,
3,
output_channels,
stride,
use_bias=False,
scope='conv_2')
if (FLAGS.GAN_type == 'GAN'):
net = ops.batchnorm(net, FLAGS.is_training)
net = net + inputs
return net
with tf.variable_scope('generator_unit', reuse=reuse):
# The input layer
with tf.variable_scope('input_stage'):
net = ops.conv3d(gen_inputs, 9, 64, 1, scope='conv')
net = ops.prelu_tf(net)
stage1_output = net
# The residual block parts
for i in range(1, FLAGS.num_resblock + 1, 1):
name_scope = 'resblock_%d' % (i)
net = residual_block(net, 64, 1, name_scope)
with tf.variable_scope('resblock_output'):
net = ops.conv3d(net, 3, 64, 1, use_bias=False, scope='conv')
if (FLAGS.GAN_type == 'GAN'):
net = ops.batchnorm(net, FLAGS.is_training)
net = net + stage1_output
with tf.variable_scope('subpixelconv_stage1'):
net = ops.conv3d(net, 3, 256, 1, scope='conv')
net = ops.pixelShuffler(net, scale=2)
net = ops.prelu_tf(net)
with tf.variable_scope('subpixelconv_stage2'):
net = ops.conv3d(net, 3, 256, 1, scope='conv')
net = ops.pixelShuffler(net, scale=2)
net = ops.prelu_tf(net)
with tf.variable_scope('output_stage'):
net = ops.conv3d(net, 9, gen_output_channels, 1, scope='conv')
return net
def generator_split(gen_inputs,
gen_output_channels,
num_resblock=16,
reuse=False,
is_training=None):
# The Bx residual blocks
def residual_block(inputs, output_channel, stride, scope):
with tf.variable_scope(scope):
net = conv2(inputs,
3,
output_channel,
stride,
use_bias=False,
scope='conv_1')
net = prelu_tf(net)
net = conv2(net,
3,
output_channel,
stride,
use_bias=False,
scope='conv_2')
net = net + inputs
return net
with tf.variable_scope('generator_unit', reuse=reuse):
# The input layer
with tf.variable_scope('input_stage'):
net = conv2(gen_inputs, 9, 64, 1, scope='conv')
net = prelu_tf(net)
stage1_output = net
# The residual block parts
for i in range(1, num_resblock + 1, 1):
name_scope = 'resblock_%d' % (i)
net = residual_block(net, 64, 1, name_scope)
with tf.variable_scope('resblock_output'):
net = conv2(net, 3, 64, 1, use_bias=False, scope='conv')
net = net + stage1_output
inputs_top = tf.slice(net, [0, 0, 0, 0], [-1, 17, -1, -1])
inputs_down = tf.slice(net, [0, 15, 0, 0], [-1, -1, -1, -1])
with tf.variable_scope('generator_unit_1', reuse=reuse):
with tf.variable_scope('subpixelconv_stage1'):
net = relate_conv(inputs_top, 64, 64, scope='conv')
net = pixelShuffler(net, scale=2)
net = prelu_tf(net)
with tf.variable_scope('subpixelconv_stage2'):
net = relate_conv(net, 64, 64, scope='conv')
net = pixelShuffler(net, scale=2)
net_top = prelu_tf(net)
with tf.variable_scope('generator_unit_2', reuse=reuse):
with tf.variable_scope('subpixelconv_stage1'):
net = relate_conv(inputs_down, 64, 64, scope='conv')
net = pixelShuffler(net, scale=2)
net = prelu_tf(net)
with tf.variable_scope('subpixelconv_stage2'):
net = relate_conv(net, 64, 64, scope='conv')
net = pixelShuffler(net, scale=2)
net_down = prelu_tf(net)
net = tf.concat([
tf.slice(net_top, [0, 0, 0, 0], [-1, 64, -1, -1]),
tf.slice(net_down, [0, 4, 0, 0], [-1, -1, -1, -1])
],
axis=1)
with tf.variable_scope('output_stage'):
net = conv2(net, 9, gen_output_channels, 1, scope='conv')
net = tf.nn.tanh(net)
return net
def generator(gen_inputs,
gen_output_channels,
num_resblock=16,
reuse=False,
is_training=None):
# The Bx residual blocks
def residual_block(inputs, output_channel, stride, scope):
with tf.variable_scope(scope):
net = conv2(inputs,
3,
output_channel,
stride,
use_bias=False,
scope='conv_1')
# net = batchnorm(net, is_training)
net = prelu_tf(net)
net = conv2(net,
3,
output_channel,
stride,
use_bias=False,
scope='conv_2')
# net = batchnorm(net, is_training)
net = net + inputs
return net
with tf.variable_scope('generator_unit', reuse=reuse):
# The input layer
with tf.variable_scope('input_stage'):
net = conv2(gen_inputs, 9, 64, 1, scope='conv')
net = prelu_tf(net)
stage1_output = net
# The residual block parts
for i in range(1, num_resblock + 1, 1):
name_scope = 'resblock_%d' % (i)
net = residual_block(net, 64, 1, name_scope)
with tf.variable_scope('resblock_output'):
net = conv2(net, 3, 64, 1, use_bias=False, scope='conv')
# net = batchnorm(net, is_training)
net = net + stage1_output
with tf.variable_scope('subpixelconv_stage1'):
# net = conv2(net, 3, 256, 1, scope='conv')
net = subpixel_pre(net,
input_channel=64,
output_channel=256,
scope='conv')
# net = relate_conv(net, 64, 64, scope='conv')
# net = interpolation_conv(net, 64, 64, scope='conv')
net = pixelShuffler(net, scale=2)
net = prelu_tf(net)
with tf.variable_scope('subpixelconv_stage2'):
# net = conv2(net, 3, 256, 1, scope='conv')
net = subpixel_pre(net,
input_channel=64,
output_channel=256,
scope='conv')
# net = relate_conv(net, 64, 64, scope='conv')
# net = interpolation_conv(net, 64, 64, scope='conv')
net = pixelShuffler(net, scale=2)
net = prelu_tf(net)
with tf.variable_scope('output_stage'):
net = conv2(net, 9, gen_output_channels, 1, scope='conv')
# net = tf.nn.tanh(net)
return net