def __init__(self, num_steps, model_load_path, num_test_rec):
"""
Initializes the Adversarial Video Generation Runner.
@param num_steps: The number of training steps to run.
@param model_load_path: The path from which to load a previously-saved model.
Default = None.
@param num_test_rec: The number of recursive generations to produce when testing. Recursive
generations use previous generations as input to predict further into
the future.
"""
self.global_step = 0
self.num_steps = num_steps
self.num_test_rec = num_test_rec
self.sess = tf.Session()
self.summary_writer = tf.summary.FileWriter(c.SUMMARY_SAVE_DIR, graph=self.sess.graph)
if c.ADVERSARIAL:
print 'Init discriminator...'
self.d_model = DiscriminatorModel(self.sess,
self.summary_writer,
c.TRAIN_HEIGHT,
c.TRAIN_WIDTH,
c.SCALE_CONV_FMS_D,
c.SCALE_KERNEL_SIZES_D,
c.SCALE_FC_LAYER_SIZES_D)
print 'Init generator...'
c.FULL_HEIGHT = 120
c.FULL_WIDTH = 160
self.g_model = GeneratorModel(self.sess,
self.summary_writer,
c.TRAIN_HEIGHT,
c.TRAIN_WIDTH,
c.FULL_HEIGHT,
c.FULL_WIDTH,
c.SCALE_FMS_GE,
c.SCALE_FMS_GD,
c.SCALE_KERNEL_SIZES_GE,
c.SCALE_KERNEL_SIZES_GD)
print 'Init variables...'
self.saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
self.sess.run(tf.global_variables_initializer())
# if load path specified, load a saved model
if model_load_path is not None:
print('\n------------------------------------')
print('loadpath: ' + model_load_path)
print('------------------------------------\n')
self.saver.restore(self.sess, model_load_path)
print 'Model restored from ' + model_load_path
def __init__(self, num_steps, model_load_path):
"""
Initializes the Adversarial Video Generation Runner.
@param num_steps: The number of training steps to run.
@param model_load_path: The path from which to load a previously-saved model.
Default = None.
"""
self.global_step = 0
self.num_steps = num_steps
self.sess = tf.Session()
#self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True, log_device_placement=True))
self.summary_writer = tf.summary.FileWriter(c.SUMMARY_SAVE_DIR,
graph=self.sess.graph)
if c.ADVERSARIAL:
print('Init discriminator...')
self.d_model = DiscriminatorModel(self.sess, self.summary_writer,
c.TRAIN_HEIGHT, c.TRAIN_WIDTH,
c.SCALE_CONV_FMS_D,
c.SCALE_KERNEL_SIZES_D,
c.SCALE_FC_LAYER_SIZES_D)
print('Init generator...')
self.g_model = GeneratorModel(self.sess, self.summary_writer,
c.TRAIN_HEIGHT, c.TRAIN_WIDTH,
c.FULL_HEIGHT, c.FULL_WIDTH,
c.SCALE_FMS_G, c.SCALE_KERNEL_SIZES_G)
print('Init variables...')
self.summary_writer.add_graph(self.sess.graph)
self.saver = tf.train.Saver(keep_checkpoint_every_n_hours=2)
self.sess.run(tf.global_variables_initializer())
# if load path specified, load a saved model
if model_load_path is not None:
self.saver.restore(self.sess, model_load_path)
print('Model restored from ' + model_load_path)
