def test_random_solarize_errors():
"""
Test that RandomSolarize errors with bad input
"""
with pytest.raises(ValueError) as error_info:
vision.RandomSolarize((12, 1))
assert "threshold must be in min max format numbers" in str(
error_info.value)
with pytest.raises(ValueError) as error_info:
vision.RandomSolarize((12, 1000))
assert "Input is not within the required interval of [0, 255]." in str(
error_info.value)
with pytest.raises(TypeError) as error_info:
vision.RandomSolarize((122.1, 140))
assert "Argument threshold[0] with value 122.1 is not of type [<class 'int'>]" in str(
error_info.value)
with pytest.raises(ValueError) as error_info:
vision.RandomSolarize((122, 100, 30))
assert "threshold must be a sequence of two numbers" in str(
error_info.value)
with pytest.raises(ValueError) as error_info:
vision.RandomSolarize((120, ))
assert "threshold must be a sequence of two numbers" in str(
error_info.value)
def test_random_solarize_op(threshold=(10, 150), plot=False, run_golden=True):
"""
Test RandomSolarize
"""
logger.info("Test RandomSolarize")
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
decode_op = vision.Decode()
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
if threshold is None:
solarize_op = vision.RandomSolarize()
else:
solarize_op = vision.RandomSolarize(threshold)
data1 = data1.map(operations=decode_op, input_columns=["image"])
data1 = data1.map(operations=solarize_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(operations=decode_op, input_columns=["image"])
if run_golden:
filename = "random_solarize_01_result.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
image_solarized = []
image = []
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)):
image_solarized.append(item1["image"].copy())
image.append(item2["image"].copy())
if plot:
visualize_list(image, image_solarized)
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
def test_random_solarize_mnist(plot=False, run_golden=True):
"""
Test RandomSolarize op with MNIST dataset (Grayscale images)
"""
mnist_1 = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
mnist_2 = ds.MnistDataset(dataset_dir=MNIST_DATA_DIR,
num_samples=2,
shuffle=False)
mnist_2 = mnist_2.map(operations=vision.RandomSolarize((0, 255)),
input_columns="image")
images = []
images_trans = []
labels = []
for _, (data_orig, data_trans) in enumerate(zip(mnist_1, mnist_2)):
image_orig, label_orig = data_orig
image_trans, _ = data_trans
images.append(image_orig.asnumpy())
labels.append(label_orig.asnumpy())
images_trans.append(image_trans.asnumpy())
if plot:
visualize_one_channel_dataset(images, images_trans, labels)
if run_golden:
filename = "random_solarize_02_result.npz"
save_and_check_md5(mnist_2, filename, generate_golden=GENERATE_GOLDEN)