def run(self, network_pkl, run_dir=None, dataset_args=None, mirror_augment=None, num_gpus=1, tf_config=None, log_results=True):
self._network_pkl = network_pkl
self._dataset_args = dataset_args
self._mirror_augment = mirror_augment
self._results = []
if (dataset_args is None or mirror_augment is None) and run_dir is not None:
run_config = misc.parse_config_for_previous_run(run_dir)
self._dataset_args = dict(run_config['dataset'])
self._dataset_args['shuffle_mb'] = 0
self._mirror_augment = run_config['train'].get('mirror_augment', False)
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(tf_config).as_default(): # pylint: disable=not-context-manager
_G, _D, Gs = misc.load_pkl(self._network_pkl)
self._evaluate(Gs, num_gpus=num_gpus)
self._eval_time = time.time() - time_begin
if log_results:
result_str = self.get_result_str()
if run_dir is not None:
log = os.path.join(run_dir, 'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log, 'a'):
print(result_str)
else:
print(result_str)
def _reset(self,
network_pkl=None,
run_dir=None,
data_dir=None,
dataset_args_train=None,
dataset_args=None,
mirror_augment=None):
if self._dataset_obj is not None:
self._dataset_obj.close()
self._network_pkl = network_pkl
self._data_dir = data_dir
self._dataset_args = dataset_args
self._dataset_args_train = dataset_args_train
self._dataset_obj = None
self._mirror_augment = mirror_augment
self._eval_time = 0
self._results = []
if (dataset_args is None
or mirror_augment is None) and run_dir is not None:
run_config = misc.parse_config_for_previous_run(run_dir)
self._dataset_args_train = dict(run_config['dataset'])
self._dataset_args_train['shuffle_mb'] = 0
self._dataset_args_train['max_images'] = None
self._dataset_args_train['skip_images'] = None
self._dataset_args = dict(run_config['dataset_eval'])
self._dataset_args['shuffle_mb'] = 0
self._dataset_args['max_images'] = None
self._dataset_args['skip_images'] = None
self._mirror_augment = run_config['train'].get(
'mirror_augment', False)
def run(self,
network_pkl,
run_dir=None,
dataset_args=None,
mirror_augment=None,
num_gpus=1,
tf_config=None,
log_results=True,
model_type="rignet"):
create_dir(config.EVALUATION_DIR, exist_ok=True)
self._network_pkl = network_pkl
self._dataset_args = dataset_args
self._mirror_augment = mirror_augment
self._results = []
self.model_type = model_type
if (dataset_args is None
or mirror_augment is None) and run_dir is not None:
run_config = misc.parse_config_for_previous_run(run_dir)
self._dataset_args = dict(run_config['dataset'])
self._dataset_args['shuffle_mb'] = 0
self._mirror_augment = run_config['train'].get(
'mirror_augment', False)
time_begin = time.time()
with tf.Graph().as_default(), tflib.create_session(
tf_config).as_default(): # pylint: disable=not-context-manager
E, _G, _D, Gs = misc.load_pkl(self._network_pkl)
print("Loaded Encoder")
Inv, _, _, _ = misc.load_pkl(config.INVERSION_PICKLE_DIR)
print("Loaded Inv")
self._evaluate(Gs, E, Inv, num_gpus=num_gpus)
self._eval_time = time.time() - time_begin
if log_results:
result_str = self.get_result_str()
if run_dir is not None:
log = os.path.join(run_dir, 'metric-%s.txt' % self.name)
with dnnlib.util.Logger(log, 'a'):
print(result_str)
else:
print(result_str)
result_path = os.path.join(
config.EVALUATION_DIR, "result_" +
convert_pickle_path_to_name(self._network_pkl) + ".txt")
write_to_file(result_str + "\n\n\n", result_path)
def _reset(self,
network_pkl=None,
run_dir=None,
data_dir=None,
dataset_args=None,
mirror_augment=None):
if self._dataset_obj is not None:
self._dataset_obj.close()
self._network_pkl = network_pkl
self._data_dir = data_dir
self._dataset_args = dataset_args
self._eval_time = 0
self._results = []
if (dataset_args is None
or mirror_augment is None) and run_dir is not None:
run_config = misc.parse_config_for_previous_run(run_dir)
self._dataset_args = dict(run_config['dataset'])
self._dataset_args['shuffle_mb'] = 0
