def test_linear_transformation_md5_01():
"""
Test LinearTransformation op: valid params (transformation_matrix, mean_vector)
Expected to pass
"""
logger.info("test_linear_transformation_md5_01")
# Initialize parameters
height = 50
weight = 50
dim = 3 * height * weight
transformation_matrix = np.ones([dim, dim])
mean_vector = np.zeros(dim)
# Generate dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([height, weight]),
py_vision.ToTensor(),
py_vision.LinearTransformation(transformation_matrix, mean_vector)
]
transform = py_vision.ComposeOp(transforms)
data1 = data1.map(input_columns=["image"], operations=transform())
# Compare with expected md5 from images
filename = "linear_transformation_01_result.npz"
save_and_check_md5(data1, filename, generate_golden=GENERATE_GOLDEN)
def test_linear_transformation_md5_05():
"""
Test LinearTransformation op: mean_vector does not match dimension of transformation_matrix
Expected to raise ValueError
"""
logger.info("test_linear_transformation_md5_05")
# Initialize parameters
height = 50
weight = 50
dim = 3 * height * weight
transformation_matrix = np.ones([dim, dim])
mean_vector = np.zeros(dim - 1)
# Generate dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
try:
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([height, weight]),
py_vision.ToTensor(),
py_vision.LinearTransformation(transformation_matrix, mean_vector)
]
transform = py_vision.ComposeOp(transforms)
data1 = data1.map(input_columns=["image"], operations=transform())
except ValueError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "should match" in str(e)
def test_linear_transformation_exception_02():
"""
Test LinearTransformation op: mean_vector is not provided
Expected to raise ValueError
"""
logger.info("test_linear_transformation_exception_02")
# Initialize parameters
height = 50
weight = 50
dim = 3 * height * weight
transformation_matrix = np.ones([dim, dim])
# Generate dataset
data1 = ds.TFRecordDataset(DATA_DIR, SCHEMA_DIR, columns_list=["image"], shuffle=False)
try:
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([height, weight]),
py_vision.ToTensor(),
py_vision.LinearTransformation(transformation_matrix, None)
]
transform = py_vision.ComposeOp(transforms)
data1 = data1.map(input_columns=["image"], operations=transform())
except TypeError as e:
logger.info("Got an exception in DE: {}".format(str(e)))
assert "Argument mean_vector with value None is not of type (<class 'numpy.ndarray'>,)" in str(e)
def test_linear_transformation_op(plot=False):
"""
Test LinearTransformation op: verify if images transform correctly
"""
logger.info("test_linear_transformation_01")
# Initialize parameters
height = 50
weight = 50
dim = 3 * height * weight
transformation_matrix = np.eye(dim)
mean_vector = np.zeros(dim)
# Define operations
transforms = [
py_vision.Decode(),
py_vision.CenterCrop([height, weight]),
py_vision.ToTensor()
]
transform = py_vision.ComposeOp(transforms)
# First dataset
data1 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data1 = data1.map(input_columns=["image"], operations=transform())
# Note: if transformation matrix is diagonal matrix with all 1 in diagonal,
# the output matrix in expected to be the same as the input matrix.
data1 = data1.map(input_columns=["image"],
operations=py_vision.LinearTransformation(
transformation_matrix, mean_vector))
# Second dataset
data2 = ds.TFRecordDataset(DATA_DIR,
SCHEMA_DIR,
columns_list=["image"],
shuffle=False)
data2 = data2.map(input_columns=["image"], operations=transform())
image_transformed = []
image = []
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_transformed.append(image1)
image.append(image2)
mse = diff_mse(image1, image2)
assert mse == 0
if plot:
visualize_list(image, image_transformed)
