def test_random_affine_exception_shear_value(): """ Test RandomAffine: shear is a number but is not positive, expected to raise ValueError """ logger.info("test_random_affine_exception_shear_value") try: _ = py_vision.RandomAffine(degrees=15, shear=-5) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Input shear must be greater than 0." try: _ = py_vision.RandomAffine(degrees=15, shear=(5, 1)) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str( e) == "Input shear[1] must be equal to or greater than shear[0]" try: _ = py_vision.RandomAffine(degrees=15, shear=(5, 1, 2, 8)) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Input shear[1] must be equal to or greater than shear[0] and " \ "shear[3] must be equal to or greater than shear[2]." try: _ = py_vision.RandomAffine(degrees=15, shear=(5, 9, 2, 1)) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Input shear[1] must be equal to or greater than shear[0] and " \ "shear[3] must be equal to or greater than shear[2]."
def test_random_affine_md5(): """ Test RandomAffine with md5 comparison """ logger.info("test_random_affine_md5") original_seed = config_get_set_seed(55) original_num_parallel_workers = config_get_set_num_parallel_workers(1) # define map operations transforms = [ py_vision.Decode(), py_vision.RandomAffine(degrees=(-5, 15), translate=(0.1, 0.3), scale=(0.9, 1.1), shear=(-10, 10, -5, 5)), py_vision.ToTensor() ] transform = mindspore.dataset.transforms.py_transforms.Compose(transforms) # Generate dataset data = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data = data.map(operations=transform, input_columns=["image"]) # check results with md5 comparison filename = "random_affine_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_affine_exception_scale_value(): """ Test RandomAffine: scale is not valid, expected to raise ValueError """ logger.info("test_random_affine_exception_scale_value") try: _ = py_vision.RandomAffine(degrees=15, scale=(0.0, 0.0)) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Input scale[1] must be greater than 0." try: _ = py_vision.RandomAffine(degrees=15, scale=(2.0, 1.1)) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Input scale[1] must be equal to or greater than scale[0]."
def test_random_affine_exception_negative_degrees(): """ Test RandomAffine: input degrees in negative, expected to raise ValueError """ logger.info("test_random_affine_exception_negative_degrees") try: _ = py_vision.RandomAffine(degrees=-15) except ValueError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Input degrees is not within the required interval of (0 to inf)."
def test_random_affine_exception_shear_size(): """ Test RandomAffine: shear is not a list or tuple of length 2 or 4, expected to raise TypeError """ logger.info("test_random_affine_exception_shear_size") try: _ = py_vision.RandomAffine(degrees=15, shear=(-5, 5, 10)) except TypeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "shear must be of length 2 or 4."
def test_random_affine_exception_degrees_size(): """ Test RandomAffine: degrees is a list or tuple and its length is not 2, expected to raise TypeError """ logger.info("test_random_affine_exception_degrees_size") try: _ = py_vision.RandomAffine(degrees=[15]) except TypeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "If degrees is a sequence, the length must be 2."
def test_random_affine_exception_scale_size(): """ Test RandomAffine: scale is not a list or tuple of length 2, expected to raise TypeError """ logger.info("test_random_affine_exception_scale_size") try: _ = py_vision.RandomAffine(degrees=15, scale=(0.5)) except TypeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str(e) == "Argument scale with value 0.5 is not of type [<class 'tuple'>," \ " <class 'list'>]."
def test_random_affine_exception_translate_size(): """ Test RandomAffine: translate is not list or a tuple of length 2, expected to raise TypeError """ logger.info("test_random_affine_exception_translate_size") try: _ = py_vision.RandomAffine(degrees=15, translate=(0.1)) except TypeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert str( e) == "Argument translate with value 0.1 is not of type (<class 'list'>," \ " <class 'tuple'>)."
def test_random_affine_py_exception_non_pil_images(): """ Test RandomAffine: input img is ndarray and not PIL, expected to raise RuntimeError """ logger.info("test_random_affine_exception_negative_degrees") dataset = ds.MnistDataset(MNIST_DATA_DIR, num_samples=3, num_parallel_workers=3) try: transform = mindspore.dataset.transforms.py_transforms.Compose([py_vision.ToTensor(), py_vision.RandomAffine(degrees=(15, 15))]) dataset = dataset.map(operations=transform, input_columns=["image"], num_parallel_workers=3) for _ in dataset.create_dict_iterator(num_epochs=1): pass except RuntimeError as e: logger.info("Got an exception in DE: {}".format(str(e))) assert "Pillow image" in str(e)
def test_random_affine_op(plot=False): """ Test RandomAffine in python transformations """ logger.info("test_random_affine_op") # define map operations transforms1 = [ py_vision.Decode(), py_vision.RandomAffine(degrees=15, translate=(0.1, 0.1), scale=(0.9, 1.1)), py_vision.ToTensor() ] transform1 = mindspore.dataset.transforms.py_transforms.Compose( transforms1) transforms2 = [py_vision.Decode(), py_vision.ToTensor()] transform2 = mindspore.dataset.transforms.py_transforms.Compose( transforms2) # First dataset data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data1 = data1.map(operations=transform1, input_columns=["image"]) # Second dataset data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False) data2 = data2.map(operations=transform2, input_columns=["image"]) image_affine = [] image_original = [] for item1, item2 in zip( data1.create_dict_iterator(num_epochs=1, output_numpy=True), data2.create_dict_iterator(num_epochs=1, output_numpy=True)): image1 = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image2 = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8) image_affine.append(image1) image_original.append(image2) if plot: visualize_list(image_original, image_affine)