def restore(ae):
restore_path = os.path.join(FLAGS.LOG_DIR, FLAGS.CHECKPOINT_DIR)
latest_checkpoint = tf.train.latest_checkpoint(restore_path)
log_string(
tf_util.toYellow("----#-> Model restoring from: %s..." % restore_path))
ae.saver.restore(ae.sess, latest_checkpoint)
log_string(tf_util.toYellow("----- Restored from %s." % latest_checkpoint))
def restore(ae):
restore_path = get_restore_path()
latest_checkpoint = tf.train.latest_checkpoint(restore_path)
log_string(
tf_util.toYellow("----#-> Model restoring from: %s..." % restore_path))
ae.saver.restore(ae.sess, latest_checkpoint)
log_string(tf_util.toYellow("----- Restored from %s." % latest_checkpoint))
def restore_from_iter(ae, iter):
restore_path = os.path.join(FLAGS.LOG_DIR, FLAGS.CHECKPOINT_DIR)
ckpt_path = os.path.join(restore_path, 'model.ckpt-{0}'.format(iter))
print(
tf_util.toYellow(
"----#-> Model restoring from: {} using {} iterations...".format(
restore_path, iter)))
ae.saver.restore(ae.sess, ckpt_path)
print(tf_util.toYellow("----- Restored from %s." % ckpt_path))
####### log writing
FLAGS.LOG_DIR = FLAGS.LOG_DIR + '/' + FLAGS.task_name
#FLAGS.CHECKPOINT_DIR = os.path.join(FLAGS.CHECKPOINT_DIR, FLAGS.task_name)
#tf_util.mkdir(FLAGS.CHECKPOINT_DIR)
if not FLAGS.is_training:
agent = ActiveAgent(FLAGS)
restore_from_iter(agent, FLAGS.test_iter)
test(agent, FLAGS.test_episode_num, FLAGS.test_iter)
sys.exit()
if not os.path.exists(FLAGS.LOG_DIR):
os.mkdir(FLAGS.LOG_DIR)
print tf_util.toYellow('===== Created %s.' % FLAGS.LOG_DIR)
else:
# os.system('rm -rf %s/*'%FLAGS.LOG_DIR)
if not (FLAGS.restore):
def check_delete():
if FLAGS.force_delete:
return True
delete_key = raw_input(
tf_util.toRed(
'===== %s exists. Delete? [y (or enter)/N] ' %
FLAGS.LOG_DIR))
return delete_key == 'y' or delete_key == ''
if check_delete():
os.system('rm -rf %s/*' % FLAGS.LOG_DIR)
def restore_pretrain(ae):
restore_path = FLAGS.pretrain_restore_path
log_string(
tf_util.toYellow("----#-> Model restoring from: %s..." % restore_path))
ae.saver.restore(ae.sess, restore_path)
log_string(tf_util.toYellow("----- Restored from %s." % restore_path))
#MODEL_FILE = os.path.join(BASE_DIR, 'models', FLAGS.model_file+'.py')
FLAGS.LOG_DIR = FLAGS.LOG_DIR + '/' + FLAGS.task_name
#FLAGS.CHECKPOINT_DIR = os.path.join(FLAGS.CHECKPOINT_DIR, FLAGS.task_name)
#tf_util.mkdir(FLAGS.CHECKPOINT_DIR)
if not FLAGS.is_training:
FLAGS.batch_size = 1
ae = AE_vox2vox(FLAGS)
restore_from_iter(ae, FLAGS.test_iter)
test(ae, FLAGS.test_iter)
sys.exit()
if not os.path.exists(FLAGS.LOG_DIR):
os.mkdir(FLAGS.LOG_DIR)
print tf_util.toYellow('===== Created %s.' % FLAGS.LOG_DIR)
else:
# os.system('rm -rf %s/*'%FLAGS.LOG_DIR)
if not (FLAGS.restore):
def check_delete():
if FLAGS.force_delete:
return True
delete_key = raw_input(
tf_util.toRed(
'===== %s exists. Delete? [y (or enter)/N] ' %
FLAGS.LOG_DIR))
return delete_key == 'y' or delete_key == ''
if check_delete():
os.system('rm -rf %s/*' % FLAGS.LOG_DIR)
# return True
# delete_key = raw_input(tf_util.toRed('===== %s exists. Delete? [y (or enter)/N] '%FLAGS.LOG_DIR))
# return delete_key == 'y' or delete_key == ''
#
# if check_delete():
# os.system('rm -rf %s/*'%FLAGS.LOG_DIR)
# #os.system('rm -rf %s/*'%FLAGS.CHECKPOINT_DIR)
# print tf_util.toRed('Deleted.'+FLAGS.LOG_DIR)
# else:
# print tf_util.toRed('Overwrite.')
# else:
# print tf_util.toRed('To Be Restored...')
#tf_util.mkdir(os.path.join(FLAGS.LOG_DIR, 'saved_images'))
#os.system('cp %s %s' % (MODEL_FILE, FLAGS.LOG_DIR)) # bkp of model def
#os.system('cp train.py %s' % (FLAGS.LOG_DIR)) # bkp of train procedure
#prepare_plot()
log_string(
tf_util.toYellow('<<<<' + FLAGS.task_name + '>>>> ' +
str(tf.flags.FLAGS.__flags)))
ae = AE_rgb2d(FLAGS)
restore(ae)
test(ae, FLAGS.test_list_path)
# z_list = []
# test_demo_render_z(ae, z_list)
#FLAGS.LOG_FOUT.close()
