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 = 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_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 positive, expected to raise ValueError
"""
logger.info("test_random_affine_exception_scale_value")
try:
_ = py_vision.RandomAffine(degrees=15, scale=(0.0, 1.1))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Input scale[0] 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_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 = 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_affine = []
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_affine.append(image1)
image_original.append(image2)
if plot:
visualize_list(image_original, image_affine)
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) == "If shear is a single number, it must be positive."
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_scale_value():
"""
Test RandomAffine: scale is not positive, expected to raise ValueError
"""
logger.info("test_random_affine_exception_scale_value")
try:
_ = py_vision.RandomAffine(degrees=15, scale=(0.0, 1.1))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "scale values should be positive"
def test_random_affine_exception_translation_range():
"""
Test RandomAffine: translation value is not in [0, 1], expected to raise ValueError
"""
logger.info("test_random_affine_exception_translation_range")
try:
_ = py_vision.RandomAffine(degrees=15, translate=(0.1, 1.5))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "translation values should be between 0 and 1"
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) == "scale should be a list or tuple of length 2."
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) == "If degrees is a single number, it cannot be negative."
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_translation_range():
"""
Test RandomAffine: translation value is not in [0, 1], expected to raise ValueError
"""
logger.info("test_random_affine_exception_translation_range")
try:
_ = py_vision.RandomAffine(degrees=15, translate=(0.1, 1.5))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(
e
) == "Input translate at 1 is not within the required interval of (0.0 to 1.0)."
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_parallel_workers=3)
try:
transform = py_vision.ComposeOp([py_vision.ToTensor(),
py_vision.RandomAffine(degrees=(15, 15))])
dataset = dataset.map(input_columns=["image"], operations=transform(), num_parallel_workers=3,
python_multiprocessing=True)
for _ in dataset.create_dict_iterator():
break
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Pillow image" in str(e)
