Python gin.clear_config() Examples

def setUp(self):
    super(DataLoadingTest, self).setUp()

    gin.clear_config()
    gin_file = os.path.join(
        './',
        'rl_reliability_metrics/evaluation',
        'eval_metrics_test.gin')
    gin.parse_config_file(gin_file)

    # fake set of training curves to test analysis
    test_data_dir = os.path.join(
        './',
        'rl_reliability_metrics/evaluation/test_data')
    self.run_dirs = [
        os.path.join(test_data_dir, 'run%d' % i, 'train') for i in range(3)
    ] 

def setUp(self):
    super(EvalMetricsTest, self).setUp()

    gin.clear_config()
    gin_file = os.path.join(
        './',
        'rl_reliability_metrics/evaluation',
        'eval_metrics_test.gin')
    gin.parse_config_file(gin_file)

    # fake set of training curves to test analysis
    self.test_data_dir = os.path.join(
        './',
        'rl_reliability_metrics/evaluation/test_data')
    self.run_dirs = [
        os.path.join(self.test_data_dir, 'run%d' % i, 'train') for i in range(3)
    ] 

def setUp(self):
    gin.clear_config() 

def setUp(self):
    super(CompareGanTestCase, self).setUp()
    # Use fake datasets instead of reading real files.
    FLAGS.data_fake_dataset = True
    # Clear the gin cofiguration.
    gin.clear_config()
    # Mock the inception graph.
    fake_inception_graph = create_fake_inception_graph()
    self.inception_graph_def_mock = mock.patch.object(
        eval_utils,
        "get_inception_graph_def",
        return_value=fake_inception_graph).start() 

def setUp(self):
    super(ResNetInitTest, self).setUp()
    gin.clear_config() 

def setUp(self):
    super(ResNet5BigGanTest, self).setUp()
    gin.clear_config() 

def setUp(self):
    super(EvalGanLibTest, self).setUp()
    gin.clear_config()
    FLAGS.data_fake_dataset = True
    self.mock_get_graph = mock.patch.object(
        eval_utils, "get_inception_graph_def").start()
    self.mock_get_graph.return_value = create_fake_inception_graph() 

def setUp(self):
    super(SingleObservationSingleActionTest, self).setUp()
    gin.clear_config() 

def setUp(self):
    super(SingleObservationSingleActionTest, self).setUp()
    gin.clear_config() 

def tearDown(self):
    gin.clear_config()
    super(CategoricalQNetworkTest, self).tearDown() 

def setUp(self):
    super(TestCase, self).setUp()
    tf.compat.v1.enable_resource_variables()
    # Guard against tests calling gin.parse_config() without calling
    # gin.clear_config(), which can cause nasty bugs that show up in a
    # completely different test. See b/139088071 for example.
    gin.clear_config() 

def tearDown(self):
    gin.clear_config()
    super(SuiteBsuiteTest, self).tearDown() 

def tearDown(self):
    gin.clear_config()
    super(SuiteMujocoTest, self).tearDown() 

def tearDown(self):
    gin.clear_config()
    super(SuiteGymTest, self).tearDown() 

def tearDown(self):
    gin.clear_config()
    super(SuitePybulletTest, self).tearDown() 

def setUp(self):
    super(CheckpointPredictorTest, self).setUp()
    gin.clear_config()
    gin.parse_config('tf.estimator.RunConfig.save_checkpoints_steps=1') 

def setUp(self):
    super(ExportedSavedmodelPredictorTest, self).setUp()
    gin.clear_config()
    gin.parse_config('tf.estimator.RunConfig.save_checkpoints_steps=1') 

def tearDown(self):
    gin.clear_config()
    super(TrainEvalTest, self).tearDown() 

def setUp(self):
    super(RunnerLibTest, self).setUp()
    gin.clear_config() 

def setUp(self):
    super().setUp()
    gin.clear_config() 

def setUp(self):
    super().setUp()
    gin.clear_config() 

def setUp(self):
    super().setUp()
    gin.clear_config()
    gin.add_config_file_search_path(_CONFIG_DIR) 

def test_get_t5_preprocessor_by_name(self):
    gin.clear_config()

    gin.parse_config("""
      get_t5_preprocessor_by_name.name = 'rekey'
      get_t5_preprocessor_by_name.fn_kwargs = {'key_map': {'inputs': 'other', 'targets': 'text'}}
    """)
    prep_rekey = tf_inputs.get_t5_preprocessor_by_name()
    og_dataset = tf.data.Dataset.from_tensors({
        'text': 'That is good.', 'other': 'That is bad.'})
    training = True
    dataset = prep_rekey(og_dataset, training)
    t5_test_utils.assert_dataset(
        dataset,
        {'inputs': 'That is bad.', 'targets': 'That is good.'}) 

def setUp(self):
    super().setUp()
    gin.clear_config() 

def setUp(self):
    gin.clear_config() 

def evaluate_disentanglement_metric(model, metric_names=['mig'], dataset_name='mpi3d_toy'):
    # These imports are included only inside this function for code base to run on systems without
    # proper installation of tensorflow and libcublas
    from aicrowd import utils_pytorch
    from aicrowd.evaluate import evaluate
    from disentanglement_lib.config.unsupervised_study_v1 import sweep as unsupervised_study_v1

    _study = unsupervised_study_v1.UnsupervisedStudyV1()
    evaluation_configs = sorted(_study.get_eval_config_files())
    evaluation_configs.append(os.path.join(os.getenv("PWD", ""), "extra_metrics_configs/irs.gin"))

    results_dict_all = dict()
    for metric_name in metric_names:
        eval_bindings = [
            "evaluation.random_seed = {}".format(0),
            "evaluation.name = '{}'".format(metric_name)
        ]

        # Get the correct config file and load it
        my_config = get_gin_config(evaluation_configs, metric_name)
        if my_config is None:
            logging.warning('metric {} not among available configs: {}'.format(metric_name, evaluation_configs))
            return 0
        # gin.parse_config_file(my_config)
        gin.parse_config_files_and_bindings([my_config], eval_bindings)

        model_path = os.path.join(model.ckpt_dir, 'pytorch_model.pt')
        utils_pytorch.export_model(utils_pytorch.RepresentationExtractor(model.model.encoder, 'mean'),
                                   input_shape=(1, model.num_channels, model.image_size, model.image_size),
                                   path=model_path)

        output_dir = os.path.join(model.ckpt_dir, 'eval_results', metric_name)
        os.makedirs(os.path.join(model.ckpt_dir, 'results'), exist_ok=True)

        results_dict = evaluate(model.ckpt_dir, output_dir, True)
        gin.clear_config()
        results = 0
        for key, value in results_dict.items():
            if key != 'elapsed_time' and key != 'uuid' and key != 'num_active_dims':
                results = value
        logging.info('Evaluation   {}={}'.format(metric_name, results))
        results_dict_all['eval_{}'.format(metric_name)] = results
    # print(results_dict)
    return results_dict_all