def test_normalizepad_exception_invalid_range_py():
"""
Test NormalizePad in python transformation: value is not in range [0,1]
expected to raise ValueError
"""
logger.info("test_normalizepad_exception_invalid_range_py")
try:
_ = py_vision.NormalizePad([0.75, 1.25, 0.5], [0.1, 0.18, 1.32])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Input mean_value is not within the required interval of [0.0, 1.0]." in str(e)
def test_normalizepad_exception_unequal_size_py():
"""
Test NormalizePad in python transformation: len(mean) != len(std)
expected to raise ValueError
"""
logger.info("test_normalizepad_exception_unequal_size_py")
try:
_ = py_vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71, 0.72])
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "Length of mean and std must be equal."
try:
_ = py_vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71], 1)
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "dtype should be string."
try:
_ = py_vision.NormalizePad([0.50, 0.30, 0.75], [0.18, 0.32, 0.71], "")
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert str(e) == "dtype only support float32 or float16."
def test_normalizepad_op_py(plot=False):
"""
Test NormalizePad in python transformations
"""
logger.info("Test Normalize in python")
mean = [0.475, 0.45, 0.392]
std = [0.275, 0.267, 0.278]
# define map operations
transforms = [
py_vision.Decode(),
py_vision.ToTensor()
]
transform = mindspore.dataset.transforms.py_transforms.Compose(transforms)
normalizepad_op = py_vision.NormalizePad(mean, std)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data1 = data1.map(operations=transform, input_columns=["image"])
data1 = data1.map(operations=normalizepad_op, input_columns=["image"])
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
data2 = data2.map(operations=transform, input_columns=["image"])
num_iter = 0
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_de_normalized = (item1["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
image_np_normalized = (normalizepad_np(item2["image"].transpose(1, 2, 0), mean, std) * 255).astype(np.uint8)
image_original = (item2["image"].transpose(1, 2, 0) * 255).astype(np.uint8)
mse = diff_mse(image_de_normalized, image_np_normalized)
logger.info("image_{}, mse: {}".format(num_iter + 1, mse))
assert mse < 0.01
if plot:
visualize_image(image_original, image_de_normalized, mse, image_np_normalized)
num_iter += 1