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(
