def construct_latent_tower(self, images, time_axis):
"""Create the latent tower."""
# No latent in the first phase
first_phase = tf.less(
self.get_iteration_num(), self.hparams.num_iterations_1st_stage)
# use all frames by default but this allows more
# predicted frames at inference time
latent_num_frames = self.hparams.latent_num_frames
tf.logging.info("Creating latent tower with %d frames." % latent_num_frames)
if latent_num_frames > 0:
images = images[:latent_num_frames]
return common_video.conv_latent_tower(
images=images,
time_axis=time_axis,
latent_channels=self.hparams.latent_channels,
min_logvar=self.hparams.latent_std_min,
is_training=self.is_training,
random_latent=first_phase,
tiny_mode=self.hparams.tiny_mode)
def construct_latent_tower(self, images, time_axis):
"""Create the latent tower."""
# No latent in the first phase
first_phase = tf.less(
self.get_iteration_num(), self.hparams.num_iterations_1st_stage)
# use all frames by default but this allows more
# predicted frames at inference time
latent_num_frames = self.hparams.latent_num_frames
tf.logging.info("Creating latent tower with %d frames." % latent_num_frames)
if latent_num_frames > 0:
images = images[:latent_num_frames]
return common_video.conv_latent_tower(
images=images,
time_axis=time_axis,
latent_channels=self.hparams.latent_channels,
min_logvar=self.hparams.latent_std_min,
is_training=self.is_training,
random_latent=first_phase,
tiny_mode=self.hparams.tiny_mode,
small_mode=self.hparams.small_mode)