def serialize_config(params: base_configs.ExperimentConfig,
model_dir: str):
"""Serializes and saves the experiment config."""
params_save_path = os.path.join(model_dir, 'params.yaml')
logging.info('Saving experiment configuration to %s', params_save_path)
tf.io.gfile.makedirs(model_dir)
params_dict.save_params_dict_to_yaml(params, params_save_path)
def _save_config(self, model_dir):
"""Save parameters to config files if model_dir is defined."""
logging.info('Save config to model_dir %s.', model_dir)
if model_dir:
if not tf.io.gfile.exists(model_dir):
tf.io.gfile.makedirs(model_dir)
self._params.lock()
params_dict.save_params_dict_to_yaml(self._params,
model_dir + '/params.yaml')
else:
logging.warning('model_dir is empty, so skip the save config.')
def test_save_params_dict_to_yaml(self):
params = params_dict.ParamsDict(
{'a': 'aa', 'b': 2, 'c': {'c1': 10, 'c2': 20}})
output_yaml_file = os.path.join(self.get_temp_dir(), 'params.yaml')
params_dict.save_params_dict_to_yaml(params, output_yaml_file)
with tf.io.gfile.GFile(output_yaml_file, 'r') as f:
params_d = yaml.load(f)
self.assertEqual(params.a, params_d['a'])
self.assertEqual(params.b, params_d['b'])
self.assertEqual(params.c.c1, params_d['c']['c1'])
self.assertEqual(params.c.c2, params_d['c']['c2'])
