def test_random_posterize_exception_bit():
"""
Test RandomPosterize: out of range input bits and invalid type
"""
logger.info("test_random_posterize_exception_bit")
# Test max > 8
try:
_ = c_vision.RandomPosterize((1, 9))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Input is not within the required interval of [1, 8]."
# Test min < 1
try:
_ = c_vision.RandomPosterize((0, 7))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Input is not within the required interval of [1, 8]."
# Test max < min
try:
_ = c_vision.RandomPosterize((8, 1))
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Input is not within the required interval of [1, 8]."
# Test wrong type (not uint8)
try:
_ = c_vision.RandomPosterize(1.1)
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Argument bits with value 1.1 is not of type [<class 'list'>, <class 'tuple'>, <class 'int'>]."
# Test wrong number of bits
try:
_ = c_vision.RandomPosterize((1, 1, 1))
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Size of bits should be a single integer or a list/tuple (min, max) of length 2."
def test_random_posterize_op_fixed_point_c(plot=False, run_golden=True):
"""
Test RandomPosterize in C transformations with fixed point
"""
logger.info("test_random_posterize_op_c")
# define map operations
transforms1 = [
c_vision.Decode(),
c_vision.RandomPosterize(1)
]
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=transforms1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=[c_vision.Decode()], input_columns=["image"])
image_posterize = []
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"]
image2 = item2["image"]
image_posterize.append(image1)
image_original.append(image2)
if run_golden:
# check results with md5 comparison
filename = "random_posterize_fixed_point_01_result_c.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image_original, image_posterize)
def test_random_posterize_op_c(plot=False, run_golden=False):
"""
Test RandomPosterize in C transformations (uses assertion on mse as using md5 could have jpeg decoding
inconsistencies)
"""
logger.info("test_random_posterize_op_c")
original_seed = config_get_set_seed(55)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms1 = [c_vision.Decode(), c_vision.RandomPosterize((1, 8))]
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data1 = data1.map(operations=transforms1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=[c_vision.Decode()], input_columns=["image"])
image_posterize = []
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"]
image2 = item2["image"]
image_posterize.append(image1)
image_original.append(image2)
# check mse as md5 can be inconsistent.
# mse = 2.9668956 is calculated from
# a thousand runs of diff_mse(np.array(image_original), np.array(image_posterize)) that all produced the same mse.
# allow for an error of 0.0000005
assert abs(2.9668956 - diff_mse(np.array(image_original),
np.array(image_posterize))) <= 0.0000005
if run_golden:
# check results with md5 comparison
filename = "random_posterize_01_result_c.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image_original, image_posterize)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_posterize_default_c_md5(plot=False, run_golden=True):
"""
Test RandomPosterize C Op (default params) with md5 comparison
"""
logger.info("test_random_posterize_default_c_md5")
original_seed = config_get_set_seed(5)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# define map operations
transforms1 = [c_vision.Decode(), c_vision.RandomPosterize()]
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data1 = data1.map(operations=transforms1, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=[c_vision.Decode()], input_columns=["image"])
image_posterize = []
image_original = []
for item1, item2 in zip(
data1.create_dict_iterator(output_numpy=True, num_epochs=1),
data2.create_dict_iterator(output_numpy=True, num_epochs=1)):
image1 = item1["image"]
image2 = item2["image"]
image_posterize.append(image1)
image_original.append(image2)
if run_golden:
# check results with md5 comparison
filename = "random_posterize_01_default_result_c.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
if plot:
visualize_list(image_original, image_posterize)
# Restore configuration
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_rescale_with_random_posterize():
"""
Test RandomPosterize: only support CV_8S/CV_8U
"""
logger.info("test_rescale_with_random_posterize")
DATA_DIR_10 = "../data/dataset/testCifar10Data"
dataset = ds.Cifar10Dataset(DATA_DIR_10)
rescale_op = c_vision.Rescale((1.0 / 255.0), 0.0)
dataset = dataset.map(operations=rescale_op, input_columns=["image"])
random_posterize_op = c_vision.RandomPosterize((4, 8))
dataset = dataset.map(operations=random_posterize_op, input_columns=["image"], num_parallel_workers=1)
try:
_ = dataset.output_shapes()
except RuntimeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "input image data type can not be float" in str(e)
