def test_smooth(
self,
value: float,
smooth_nr: float,
smooth_dr: float,
expected: float,
):
"""
Test values in extreme cases where variances are all zero.
:param value: value for input.
:param smooth_nr: constant for numerator.
:param smooth_dr: constant for denominator.
:param expected: target value.
"""
kernel_size = 5
mid = kernel_size // 2
shape = (1, kernel_size, kernel_size, kernel_size, 1)
y_true = tf.ones(shape=shape) * value
y_pred = tf.ones(shape=shape) * value
got = image.LocalNormalizedCrossCorrelation(
kernel_size=kernel_size,
smooth_nr=smooth_nr,
smooth_dr=smooth_dr,
).calc_ncc(
y_true,
y_pred,
)
got = got[0, mid, mid, mid, 0]
expected = tf.constant(expected)
assert is_equal_tf(got, expected)
def test_error(self):
y = np.ones(shape=(3, 3, 3, 3))
with pytest.raises(ValueError) as err_info:
image.LocalNormalizedCrossCorrelation(kernel_type="constant").call(
y, y)
assert "Wrong kernel_type constant for LNCC loss type." in str(
err_info.value)
def test_get_config(self):
got = image.LocalNormalizedCrossCorrelation().get_config()
expected = dict(
kernel_size=9,
kernel_type="rectangular",
reduction=tf.keras.losses.Reduction.SUM,
name="LocalNormalizedCrossCorrelation",
)
assert got == expected
def test_zero_info(self, y_true, y_pred, shape, kernel_type, expected):
y_true = y_true * tf.ones(shape=shape)
y_pred = y_pred * tf.ones(shape=shape)
expected = expected * tf.ones(shape=(shape[0], ))
got = image.LocalNormalizedCrossCorrelation(
kernel_type=kernel_type).call(
y_true,
y_pred,
)
assert is_equal_tf(got, expected)
def test_get_config(self):
"""Test the config is saved correctly."""
got = image.LocalNormalizedCrossCorrelation().get_config()
expected = dict(
kernel_size=9,
kernel_type="rectangular",
reduction=tf.keras.losses.Reduction.AUTO,
name="LocalNormalizedCrossCorrelation",
smooth_nr=1e-5,
smooth_dr=1e-5,
)
assert got == expected
def test_input_shape_err(self, y_true_shape: Tuple, y_pred_shape: Tuple,
name: str):
"""
Current LNCC does not support image having channel dimension > 1.
:param y_true_shape: input shape for y_true.
:param y_pred_shape: input shape for y_pred.
:param name: name of the tensor having error.
"""
y_true = tf.ones(shape=y_true_shape)
y_pred = tf.ones(shape=y_pred_shape)
with pytest.raises(ValueError) as err_info:
image.LocalNormalizedCrossCorrelation().call(y_true, y_pred)
assert f"Last dimension of {name} is not one." in str(err_info.value)
def test_input_shape(self, y_true_shape: Tuple, y_pred_shape: Tuple):
"""
Test input with / without channel axis.
:param y_true_shape: input shape for y_true.
:param y_pred_shape: input shape for y_pred.
"""
y_true = tf.ones(shape=y_true_shape)
y_pred = tf.ones(shape=y_pred_shape)
got = image.LocalNormalizedCrossCorrelation().call(
y_true,
y_pred,
)
assert got.shape == y_true_shape[:1]
def test_exact_value(self, kernel_type, kernel_size):
"""
Test the exact value at the center of a cube.
:param kernel_type: name of kernel.
:param kernel_size: size of the kernel and the cube.
"""
# init
mid = kernel_size // 2
tf.random.set_seed(0)
y_true = tf.random.uniform(shape=(1, kernel_size, kernel_size,
kernel_size, 1))
y_pred = tf.random.uniform(shape=(1, kernel_size, kernel_size,
kernel_size, 1))
loss = image.LocalNormalizedCrossCorrelation(kernel_type=kernel_type,
kernel_size=kernel_size)
# obtained value
got = loss.calc_ncc(y_true=y_true, y_pred=y_pred)
got = got[0, mid, mid, mid, 0] # center voxel
# target value
kernel_3d = (loss.kernel[:, None, None] * loss.kernel[None, :, None] *
loss.kernel[None, None, :])
kernel_3d = kernel_3d[None, :, :, :, None]
y_true_mean = tf.reduce_sum(y_true * kernel_3d) / loss.kernel_vol
y_true_normalized = y_true - y_true_mean
y_true_var = tf.reduce_sum(y_true_normalized**2 * kernel_3d)
y_pred_mean = tf.reduce_sum(y_pred * kernel_3d) / loss.kernel_vol
y_pred_normalized = y_pred - y_pred_mean
y_pred_var = tf.reduce_sum(y_pred_normalized**2 * kernel_3d)
cross = tf.reduce_sum(y_true_normalized * y_pred_normalized *
kernel_3d)
expected = (cross**2 + EPS) / (y_pred_var * y_true_var + EPS)
# check
assert is_equal_tf(got, expected)
def test_kernel_error(self):
"""Test the error message when using wrong kernel."""
with pytest.raises(ValueError) as err_info:
image.LocalNormalizedCrossCorrelation(kernel_type="constant")
assert "Wrong kernel_type constant for LNCC loss type." in str(
err_info.value)