def test_synthetic_random_data(self): ds = DataGenerator.get_random_dataset(height=32, width=32, num_classes=5, data_type=tf.float32) assert (DataGenerator.evaluate_size_dataset(ds) == 1) assert (isinstance(ds, tf.data.Dataset))
def test_check_size(self): ds = tf.data.Dataset.from_tensor_slices([1, 2, 3]) assert (DataGenerator.evaluate_size_dataset(ds) == 3)
args = parser.parse_args() logging.info(f'args = {args}') dataset_name = args.dataset dataset_path = args.dataset_path ds_train, _, ds_size, _, _ = DatasetFactory.get_dataset( dataset_name=dataset_name, dataset_path=dataset_path, split='train', img_datatype=float, micro_batch_size=1, accelerator_side_preprocess=False, apply_preprocessing=False) train_split_match = DataGenerator.evaluate_size_dataset( ds_train) == ds_size ds_valid, _, ds_valid_size, _, _ = DatasetFactory.get_dataset( dataset_name=dataset_name, dataset_path=dataset_path, split='test', img_datatype=float, micro_batch_size=1, accelerator_side_preprocess=False, apply_preprocessing=False) test_split_match = DataGenerator.evaluate_size_dataset( ds_valid) == ds_valid_size if not train_split_match: logging.warning(