def test_random_apply_md5(): """ Test RandomApply op with md5 check """ logger.info("test_random_apply_md5") original_seed = config_get_set_seed(10) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)] transforms = [ py_vision.Decode(), # Note: using default value "prob=0.5" py_vision.RandomApply(transforms_list), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(input_columns=["image"], operations=transform()) # check results with md5 comparison filename = "random_apply_01_result.npz" save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers((original_num_parallel_workers))
def test_random_apply_exception_random_crop_badinput(): """ Test RandomApply: test invalid input for one of the transform functions, expected to raise error """ logger.info("test_random_apply_exception_random_crop_badinput") original_seed = config_get_set_seed(200) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms_list = [ py_vision.Resize([32, 32]), py_vision.RandomCrop(100), # crop size > image size py_vision.RandomRotation(30) ] transforms = [ py_vision.Decode(), py_vision.RandomApply(transforms_list, prob=0.6), py_vision.ToTensor() ] transform = py_vision.ComposeOp(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(input_columns=["image"], operations=transform()) try: _ = data.create_dict_iterator().get_next() except RuntimeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "Crop size" in str(e) # Restore configuration ds.config.set_seed(original_seed) ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_apply_op(plot=False): """ Test RandomApply in python transformations """ logger.info("test_random_apply_op") # define map operations transforms_list = [py_vision.CenterCrop(64), py_vision.RandomRotation(30)] transforms1 = [ py_vision.Decode(), py_vision.RandomApply(transforms_list, prob=0.6), py_vision.ToTensor() ] transform1 = py_vision.ComposeOp(transforms1) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = py_vision.ComposeOp(transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(input_columns=["image"], operations=transform1()) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(input_columns=["image"], operations=transform2()) image_apply = [] image_original = [] for item1, item2 in zip(data1.create_dict_iterator(), data2.create_dict_iterator()): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_apply.append(image1) image_original.append(image2) if plot: visualize(image_original, image_apply)