def setup(self, session):
try:
if self.args.model_name == 'DXN':
self.model = dexined(self.args)
else:
print_error("Error setting model, {}".format(
self.args.model_name))
if self.args.trained_model_dir is None:
meta_model_file = os.path.join(
self.args.checkpoint_dir,
os.path.join(
self.args.model_name + '_' + self.args.train_dataset,
os.path.join(
'train', '{}-{}'.format(self.args.model_name,
self.args.test_snapshot))))
else:
meta_model_file = os.path.join(
self.args.checkpoint_dir,
os.path.join(
self.args.model_name + '_' + self.args.train_dataset,
os.path.join(
self.args.trained_model_dir,
'{}-{}'.format(self.args.model_name,
self.args.test_snapshot))))
saver = tf.train.Saver()
saver.restore(session, meta_model_file)
print_info(
'Done restoring DexiNed model from {}'.format(meta_model_file))
except Exception as err:
print_error(
'Error setting up DexiNed traied model, {}'.format(err))
def setup(self):
try:
if self.args.model_name=='DXN':
self.model = dexined(self.args)
else:
print_error("Error setting model, {}".format(self.args.model_name))
print_info("DL model Set")
except Exception as err:
print_error("Error setting up DL model, {}".format(err))
self.init=False