def build_train_op(self, global_step):
'''
Builds two train ops, one for the Generator and one for the Discriminator. These can
be run independently any number of times, and each time will increase the global_step.
Args:
global_step: A Tensor to be incremented
Returns:
[ g_train_op, d_train_op ]
'''
if not self.model_built or not self.losses_built:
raise RuntimeError(
"Cannot build optimizers until 'build_model' ({0}) and 'get_losses' {1} are run"
.format(self.model_built, self.losses_built))
self.global_step = global_step
self.global_step_copy = tf.identity(global_step,
name='global_step_copy')
t_vars = tf.trainable_variables()
# Create the optimizer train_op for the generator
self.g_optimizer = optimize.build_optimizer(
global_step=self.global_step, cfg=self.cfg)
self.g_vars = slim.get_variables(
scope='encoder', collection=tf.GraphKeys.TRAINABLE_VARIABLES)
self.g_vars += slim.get_variables(
scope='decoder', collection=tf.GraphKeys.TRAINABLE_VARIABLES)
self.g_train_op = optimize.create_train_op(
self.loss_g_total,
self.g_optimizer,
variables_to_train=self.g_vars,
update_global_step=True)
self.g_lnorm_op = optimize.create_train_op(
self.l1_loss,
self.g_optimizer,
variables_to_train=self.g_vars,
update_global_step=True)
# Create a train_op for the discriminator
if 'discriminator_learning_args' in self.cfg: # use these
discriminator_learning_args = self.cfg[
'discriminator_learning_args']
else:
discriminator_learning_args = self.cfg
self.d_optimizer = optimize.build_optimizer(
global_step=self.global_step, cfg=discriminator_learning_args)
self.d_vars = slim.get_variables(
scope='discriminator', collection=tf.GraphKeys.TRAINABLE_VARIABLES)
self.d_vars += slim.get_variables(
scope='discriminator_1',
collection=tf.GraphKeys.TRAINABLE_VARIABLES)
self.d_train_op = slim.learning.create_train_op(
self.loss_d_total,
self.d_optimizer,
variables_to_train=self.d_vars)
self.train_op = [self.g_train_op, self.d_train_op, self.g_lnorm_op]
self.train_op_built = True
return self.train_op
def build_train_op( self, global_step ):
'''
Builds train ops for discriminative task
Args:
global_step: A Tensor to be incremented
Returns:
[ loss_op, accuracy ]
'''
if not self.model_built or self.total_loss is None :
raise RuntimeError( "Cannot build optimizers until 'build_model' ({0}) and 'get_losses' {1} are run".format(
self.model_built, self.losses_built ) )
self.global_step = global_step
t_vars = tf.trainable_variables()
# Create the optimizer train_op for the generator
self.optimizer = optimize.build_optimizer( global_step=self.global_step, cfg=self.cfg )
if 'clip_norm' in self.cfg:
self.loss_op = optimize.create_train_op( self.total_loss, self.optimizer, update_global_step=True, clip_gradient_norm=self.cfg['clip_norm'])
else:
if self.is_training:
self.loss_op = optimize.create_train_op( self.total_loss, self.optimizer, update_global_step=True )
else:
self.loss_op = optimize.create_train_op( self.total_loss, self.optimizer, is_training=False, update_global_step=True )
# Create a train_op for the discriminator
self.train_op = [ self.loss_op, self.accuracy]
self.train_op_built = True
return self.train_op
def build_train_op(self, global_step):
if not self.model_built or self.total_loss is None:
raise RuntimeError(
"Cannot build optimizers until 'build_model' ({0}) and 'get_losses' {1} are run"
.format(self.model_built, self.total_loss is not None))
self.global_step = global_step
self.optimizer = optimizers.build_optimizer(global_step=global_step,
cfg=self.cfg)
self.train_op = slim.learning.create_train_op(self.total_loss,
self.optimizer)
def build_train_op( self, global_step ):
'''
Builds train ops for discriminative task
Args:
global_step: A Tensor to be incremented
Returns:
[ loss_op, accuracy ]
'''
if not self.model_built or self.total_loss is None :
raise RuntimeError( "Cannot build optimizers until 'build_model' ({0}) and 'get_losses' {1} are run".format(
self.model_built, self.losses_built ) )
self.global_step = global_step
vars_to_train = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope=self.encoder_scope)
# Create the optimizer train_op for the generator
self.optimizer = optimize.build_optimizer( global_step=self.global_step, cfg=self.cfg )
if 'clip_norm' in self.cfg:
self.loss_op = optimize.create_train_op(
self.total_loss, self.optimizer,
update_global_step=True, clip_gradient_norm=self.cfg['clip_norm'],
variables_to_train=vars_to_train )
else:
if self.is_training:
self.loss_op = optimize.create_train_op(
self.total_loss, self.optimizer,
update_global_step=True, variables_to_train=vars_to_train )
else:
self.loss_op = optimize.create_train_op(
self.total_loss, self.optimizer,
is_training=False, update_global_step=True,
variables_to_train=vars_to_train )
self.train_op = [ self.loss_op, 0 ]
self.train_op_built = True
return self.train_op
