| # Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Tests for saving and loading with mixed APIs with distribution strategies. |
| |
| For saving, Keras's export_saved_model() API is used; and for loading, |
| saved_model's load() API is used. Keras's export_save_model() when used with |
| `serving_only` parameter equals to True should be the same as using |
| tf.saved_model.save(). |
| """ |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| from tensorflow.python.distribute import combinations |
| from tensorflow.python.distribute import saved_model_test_base as test_base |
| from tensorflow.python.eager import test |
| from tensorflow.python.keras.saving import saved_model_experimental as keras_saved_model |
| |
| _DEFAULT_FUNCTION_KEY = 'serving_default' |
| |
| |
| class SavedModelSaveAndLoadTest(test_base.TestSavedModelBase): |
| |
| def setUp(self): |
| self._root_dir = 'saved_model_save_load' |
| super(SavedModelSaveAndLoadTest, self).setUp() |
| |
| def _save_model(self, model, saved_dir): |
| keras_saved_model.export_saved_model(model, saved_dir, serving_only=True) |
| |
| def _load_and_run_model(self, distribution, saved_dir, predict_dataset, |
| output_name): |
| return test_base.load_and_run_with_saved_model_api(distribution, saved_dir, |
| predict_dataset, |
| output_name) |
| |
| @combinations.generate(test_base.simple_models_with_strategies()) |
| def test_save_no_strategy_restore_strategy(self, model_and_input, |
| distribution, run_distributed): |
| self.run_test_save_no_strategy_restore_strategy(model_and_input, |
| distribution, |
| run_distributed) |
| |
| @combinations.generate( |
| combinations.times(test_base.simple_models_with_strategies(), |
| combinations.combine(save_in_scope=[True, False]))) |
| def test_save_strategy_restore_no_strategy(self, model_and_input, |
| distribution, save_in_scope, |
| run_distributed): |
| if save_in_scope: |
| self.skipTest(('Saving model within tf.distribute.Strategy scope is not ', |
| 'supported.')) |
| self.run_test_save_strategy_restore_no_strategy(model_and_input, |
| distribution, save_in_scope, |
| run_distributed) |
| |
| @combinations.generate( |
| combinations.times(test_base.simple_models_with_strategy_pairs(), |
| combinations.combine(save_in_scope=[True, False]))) |
| def test_save_strategy_restore_strategy(self, model_and_input, |
| distribution_for_saving, |
| distribution_for_restoring, |
| save_in_scope, run_distributed): |
| if save_in_scope: |
| self.skipTest(('Saving model within tf.distribute.Strategy scope is not ', |
| 'supported.')) |
| self.run_test_save_strategy_restore_strategy(model_and_input, |
| distribution_for_saving, |
| distribution_for_restoring, |
| save_in_scope, run_distributed) |
| |
| |
| if __name__ == '__main__': |
| test.main() |