def define_graph(self):
"""
Sets up the model graph in TensorFlow.
"""
with tf.name_scope('discriminator'):
##
# Setup scale networks. Each will make the predictions for images at a given scale.
##
self.scale_nets = []
#for scale_num in xrange(self.num_scale_nets):
for scale_num in range(self.num_scale_nets):
with tf.name_scope('scale_net_' + str(scale_num)):
scale_factor = 1. / 2**(
(self.num_scale_nets - 1) - scale_num)
self.scale_nets.append(
DScaleModel(scale_num, int(self.height * scale_factor),
int(self.width * scale_factor),
self.scale_conv_layer_fms[scale_num],
self.scale_kernel_sizes[scale_num],
self.scale_fc_layer_sizes[scale_num]))
# A list of the prediction tensors for each scale network
self.scale_preds = []
#for scale_num in xrange(self.num_scale_nets):
for scale_num in range(self.num_scale_nets):
self.scale_preds.append(self.scale_nets[scale_num].preds)
##
# Data
##
self.labels = tf.placeholder(tf.float32,
shape=[None, 1],
name='labels')
##
# Training
##
with tf.name_scope('training'):
# global loss is the combined loss from every scale network
self.global_loss = adv_loss(self.scale_preds, self.labels)
self.global_step = tf.Variable(0,
trainable=False,
name='global_step')
self.optimizer = tf.train.GradientDescentOptimizer(
c.LRATE_D, name='optimizer')
self.train_op = self.optimizer.minimize(
self.global_loss,
global_step=self.global_step,
name='train_op')
# add summaries to visualize in TensorBoard
#loss_summary = tf.scalar_summary('loss_D', self.global_loss)
loss_summary = tf.summary.scalar('loss_D', self.global_loss)
self.summaries = tf.summary.merge([loss_summary])
def setup_scale_nets(self):
"""
Setup scale networks. Each will make the predictions for images at a given scale. Done
separately from define_graph() so that the generator can define its graph using the
discriminator scale nets before this defines its graph using the generator.
"""
self.scale_nets = []
for scale_num in xrange(self.num_scale_nets):
with tf.name_scope('scale_net_' + str(scale_num)):
scale_factor = 1. / self.inverse_scale_factor[scale_num]
scale_model = DScaleModel(scale_num,
int(self.height * scale_factor),
int(self.width * scale_factor),
self.scale_conv_layer_fms[scale_num],
self.scale_kernel_sizes[scale_num],
self.scale_fc_layer_sizes[scale_num])
self.scale_nets.append(scale_model)
self.train_vars += scale_model.train_vars
def define_graph(self):
"""
Sets up the model graph in TensorFlow.
"""
with tf.name_scope('discriminator'):
##
# Setup scale networks. Each will make the predictions for images at a given scale.
##
self.scale_nets = []
for scale_num in range(self.num_scale_nets):
with tf.name_scope('scale_net_' + str(scale_num)):
scale_factor = 1. / 2**(
(self.num_scale_nets - 1) - scale_num)
self.scale_nets.append(
DScaleModel(scale_num, int(self.height * scale_factor),
int(self.width * scale_factor),
self.scale_conv_layer_fms[scale_num],
self.scale_kernel_sizes[scale_num],
self.scale_fc_layer_sizes[scale_num],
self.is_w))
# A list of the prediction tensors for each scale network
self.scale_preds = []
for scale_num in range(self.num_scale_nets):
self.scale_preds.append(self.scale_nets[scale_num].preds)
##
# Data
##
self.labels = tf.placeholder(tf.float32,
shape=[None, 1],
name='labels')
##
# Training
##
with tf.name_scope('training'):
# global loss is the combined loss from every scale network
self.global_loss = adv_loss(self.scale_preds, self.labels,
self.is_w)
self.global_step = tf.Variable(0.0,
trainable=False,
name='global_step')
with tf.control_dependencies(
tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
if self.is_w == True:
self.optimizer = tf.train.RMSPropOptimizer(
self.c.LRATE_D, name='optimizer')
else:
self.optimizer = tf.train.GradientDescentOptimizer(
self.c.LRATE_D, name='optimizer')
# self.optimizer = tf.train.GradientDescentOptimizer(self.c.LRATE_D, name='optimizer')
self.train_op = self.optimizer.minimize(
self.global_loss,
global_step=self.global_step,
name='train_op',
var_list=self.discriminator_vars)
# add summaries to visualize in TensorBoard
self.clip = [
v.assign(tf.clip_by_value(v, -0.01, 0.01))
for v in self.discriminator_vars
]
loss_summary = tf.summary.scalar('loss_D', self.global_loss)
self.summaries = tf.summary.merge([loss_summary])
def define_graph(self):
"""
Sets up the model graph in TensorFlow.
"""
with tf.name_scope('discriminator'):
##
# Setup scale networks. Each will make the predictions for images at a given scale.
##
self.scale_nets = []
for scale_num in range(self.num_scale_nets):
with tf.name_scope('scale_net_' + str(scale_num)):
scale_factor = 1. / 2 ** ((self.num_scale_nets - 1) - scale_num)
self.scale_nets.append(DScaleModel(scale_num,
int(self.height * scale_factor),
int(self.width * scale_factor),
self.scale_conv_layer_fms[scale_num],
self.scale_kernel_sizes[scale_num],
self.scale_fc_layer_sizes[scale_num]))
# A list of the prediction tensors for each scale network
self.scale_preds = []
for scale_num in range(self.num_scale_nets):
self.scale_preds.append(self.scale_nets[scale_num].preds)
##
# Data
##
self.labels = tf.placeholder(tf.float32, shape=[None, 1], name='labels')
##
# Training
##
with tf.name_scope('training'):
# global loss is the combined loss from every scale network
self.global_loss = adv_loss(self.scale_preds, self.labels)
if c.WASSERSTEIN and c.W_GP:
epsilon = tf.random_uniform([], 0.0, 1.0)
grad_penality = []
for scale_net in self.scale_nets:
fake, real = tf.split(scale_net.input_frames,2)
self.x_hat = real * epsilon + (1 - epsilon) * fake
self.d_hat = scale_net.generate_predictions(self.x_hat)
grad_penality.append(grad_penality_loss(self.x_hat, self.d_hat))
self.global_loss += c.LAM_GP * tf.reduce_mean(grad_penality)
self.global_step = tf.Variable(0, trainable=False, name='global_step')
self.optimizer = tf.train.AdamOptimizer(c.LRATE_D, name='optimizer')
self.train_op_ = self.optimizer.minimize(self.global_loss,
global_step=self.global_step,
name='train_op')
# Clipping to enforce 1-Lipschitz function
if c.WASSERSTEIN and not c.W_GP:
with tf.control_dependencies([self.train_op_]):
self.train_op = tf.group(*(tf.assign(var, tf.clip_by_value(var, -c.W_Clip, c.W_Clip)) for var in tf.trainable_variables() if 'discriminator' in var.name))
else:
self.train_op = self.train_op_
# add summaries to visualize in TensorBoard
loss_summary = tf.summary.scalar('loss_D', self.global_loss)
self.summaries = tf.summary.merge([loss_summary])
