def test_gmi():
"""
Testing computed global mutual information between images using image.global_mutual_information by comparing to precomputed.
"""
# fixed non trival value
t1 = np.array(range(108)).reshape((4, 3, 3, 3, 1)) / 108.0
t1 = tf.convert_to_tensor(t1, dtype=tf.float32)
t2 = t1 + 0.05
get = image.global_mutual_information(t1, t2)
expect = tf.constant(
[0.84280217, 0.84347117, 0.8441777, 0.8128618], dtype=tf.float32
)
assert is_equal_tf(get, expect)
# zero values
t1 = tf.zeros((4, 3, 3, 3, 1), dtype=tf.float32)
t2 = t1
get = image.global_mutual_information(t1, t2)
expect = tf.constant([0, 0, 0, 0], dtype=tf.float32)
assert is_equal_tf(get, expect)
# zero value and negative value
t1 = tf.zeros((4, 3, 3, 3, 1), dtype=tf.float32)
t2 = t1 - 1.0 # will be clipped to zero
get = image.global_mutual_information(t1, t2)
expect = tf.constant([0, 0, 0, 0], dtype=tf.float32)
assert is_equal_tf(get, expect)
# one values
t1 = tf.ones((4, 3, 3, 3, 1), dtype=tf.float32)
t2 = t1
get = image.global_mutual_information(t1, t2)
expect = tf.constant([0, 0, 0, 0], dtype=tf.float32)
assert is_equal_tf(get, expect)
def test_init_GlobalNet():
"""
Testing init of GlobalNet is built as expected.
"""
# Initialising GlobalNet instance
global_test = g.GlobalNet(
image_size=[1, 2, 3],
out_channels=3,
num_channel_initial=3,
extract_levels=[1, 2, 3],
out_kernel_initializer="softmax",
out_activation="softmax",
)
# Asserting initialised var for extract_levels is the same - Pass
assert global_test._extract_levels == [1, 2, 3]
# Asserting initialised var for extract_max_level is the same - Pass
assert global_test._extract_max_level == 3
# self reference grid
# assert global_test.reference_grid correct shape, Pass
assert global_test.reference_grid.shape == [1, 2, 3, 3]
# assert correct reference grid returned, Pass
expected_ref_grid = tf.convert_to_tensor(
[[
[[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 2.0]],
[[0.0, 1.0, 0.0], [0.0, 1.0, 1.0], [0.0, 1.0, 2.0]],
]],
dtype=tf.float32,
)
assert is_equal_tf(global_test.reference_grid, expected_ref_grid)
# Testing constant initializer
# We initialize the expected tensor and initialise another from the
# class variable using tf.Variable
test_tensor_return = tf.convert_to_tensor(
[[1.0, 0.0], [0.0, 0.0], [0.0, 1.0], [0.0, 0.0], [0.0, 0.0],
[1.0, 0.0]],
dtype=tf.float32,
)
global_return = tf.Variable(
global_test.transform_initial(shape=[6, 2], dtype=tf.float32))
# Asserting they are equal - Pass
assert is_equal_tf(test_tensor_return,
tf.convert_to_tensor(global_return, dtype=tf.float32))
# Assert downsample blocks type is correct, Pass
assert all(
isinstance(item, layer.DownSampleResnetBlock)
for item in global_test._downsample_blocks)
# Assert number of downsample blocks is correct (== max level), Pass
assert len(global_test._downsample_blocks) == 3
# Assert conv3dBlock type is correct, Pass
assert isinstance(global_test._conv3d_block, layer.Conv3dBlock)
# Asserting type is dense_layer, Pass
assert isinstance(global_test._dense_layer, layer.Dense)
def test_call(self, y_true, y_pred, binary, scales, expected):
expected = np.array([expected] *
self.shape[0]) # call returns (batch, )
got = label.JaccardIndex(binary=binary,
scales=scales).call(y_true=y_true,
y_pred=y_pred)
assert is_equal_tf(got, expected)
got = label.JaccardLoss(binary=binary,
scales=scales).call(y_true=y_true,
y_pred=y_pred)
assert is_equal_tf(got, -expected)
def test_call(self, y_true, y_pred, binary, neg_weight, scales, expected):
expected = np.array([expected] *
self.shape[0]) # call returns (batch, )
got = label.DiceScore(binary=binary,
neg_weight=neg_weight,
scales=scales).call(y_true=y_true, y_pred=y_pred)
assert is_equal_tf(got, expected)
got = label.DiceLoss(binary=binary,
neg_weight=neg_weight,
scales=scales).call(y_true=y_true, y_pred=y_pred)
assert is_equal_tf(got, -expected)
def test_pyramid_combinations():
"""
Test pyramid_combinations by confirming that it generates
appropriate solutions for simple 1D and 2D cases.
"""
# Check numerical outputs are correct for a simple 1D pair of weights, values - Pass
weights = tf.constant(np.array([[0.2]], dtype=np.float32))
values = tf.constant(np.array([[1], [2]], dtype=np.float32))
# expected = 1 * 0.2 + 2 * 2
expected = tf.constant(np.array([1.8], dtype=np.float32))
got = layer_util.pyramid_combination(values=values, weights=weights)
assert is_equal_tf(got, expected)
# Check numerical outputs are correct for a 2D pair of weights, values - Pass
weights = tf.constant(np.array([[0.2], [0.3]], dtype=np.float32))
values = tf.constant(
np.array(
[
[1], # value at corner (0, 0), weight = 0.2 * 0.3
[2], # value at corner (0, 1), weight = 0.2 * 0.7
[3], # value at corner (1, 0), weight = 0.8 * 0.3
[4], # value at corner (1, 1), weight = 0.8 * 0.7
],
dtype=np.float32,
)
)
# expected = 1 * 0.2 * 0.3
# + 2 * 0.2 * 0.7
# + 3 * 0.8 * 0.3
# + 4 * 0.8 * 0.7
expected = tf.constant(np.array([3.3], dtype=np.float32))
got = layer_util.pyramid_combination(values=values, weights=weights)
assert is_equal_tf(got, expected)
# Check input lengths match - Fail
weights = tf.constant(np.array([[[0.2]], [[0.2]]], dtype=np.float32))
values = tf.constant(np.array([[1], [2]], dtype=np.float32))
with pytest.raises(ValueError) as err_info:
layer_util.pyramid_combination(values=values, weights=weights)
assert (
"In pyramid_combination, elements of values and weights should have same dimension"
in str(err_info.value)
)
# Check input lengths match - Fail
weights = tf.constant(np.array([[0.2]], dtype=np.float32))
values = tf.constant(np.array([[1]], dtype=np.float32))
with pytest.raises(ValueError) as err_info:
layer_util.pyramid_combination(values=values, weights=weights)
assert (
"In pyramid_combination, num_dim = len(weights), len(values) must be 2 ** num_dim"
in str(err_info.value)
)
def test_dissimilarity_fn():
"""
Testing computed dissimilarity function by comparing to precomputed, the dissimilarity function can be either normalized cross correlation or sum square error function.
"""
# lncc diff images
tensor_true = np.array(range(12)).reshape((2, 1, 2, 3))
tensor_pred = 0.6 * np.ones((2, 1, 2, 3))
tensor_true = tf.convert_to_tensor(tensor_true, dtype=tf.float32)
tensor_pred = tf.convert_to_tensor(tensor_pred, dtype=tf.float32)
name_ncc = "lncc"
get_ncc = image.dissimilarity_fn(tensor_true, tensor_pred, name_ncc)
expect_ncc = [-0.68002254, -0.9608879]
assert is_equal_tf(get_ncc, expect_ncc)
# ssd diff images
tensor_true1 = np.zeros((2, 1, 2, 3))
tensor_pred1 = 0.6 * np.ones((2, 1, 2, 3))
tensor_true1 = tf.convert_to_tensor(tensor_true1, dtype=tf.float32)
tensor_pred1 = tf.convert_to_tensor(tensor_pred1, dtype=tf.float32)
name_ssd = "ssd"
get_ssd = image.dissimilarity_fn(tensor_true1, tensor_pred1, name_ssd)
expect_ssd = [0.36, 0.36]
assert is_equal_tf(get_ssd, expect_ssd)
# TODO gmi diff images
# lncc same image
get_zero_similarity_ncc = image.dissimilarity_fn(
tensor_pred1, tensor_pred1, name_ncc
)
assert is_equal_tf(get_zero_similarity_ncc, [-1, -1])
# ssd same image
get_zero_similarity_ssd = image.dissimilarity_fn(
tensor_true1, tensor_true1, name_ssd
)
assert is_equal_tf(get_zero_similarity_ssd, [0, 0])
# gmi same image
t = tf.ones([4, 3, 3, 3])
get_zero_similarity_gmi = image.dissimilarity_fn(t, t, "gmi")
assert is_equal_tf(get_zero_similarity_gmi, [0, 0, 0, 0])
# unknown func name
with pytest.raises(AssertionError):
image.dissimilarity_fn(
tensor_true1, tensor_pred1, "some random string that isn't ssd or lncc"
)
def test_compute_gradient_norm():
"""test the calculation of l1/l2 norm for image gradients"""
# l1 norm
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.compute_gradient_norm(tensor, l1=True)
expect = tf.zeros([4])
assert is_equal_tf(get, expect)
# l2 norm
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.compute_gradient_norm(tensor)
expect = tf.zeros([4])
assert is_equal_tf(get, expect)
def test_non_identical(self):
theta = tf.constant(
np.array(
[
[
[0.86, 0.75, 0.48],
[0.07, 0.98, 0.01],
[0.72, 0.52, 0.97],
[0.12, 0.4, 0.04],
]
],
dtype=np.float32,
)
) # shape = (1, 4, 3)
expected = tf.constant(
np.array(
[
[
[
[[0.12, 0.4, 0.04], [0.84, 0.92, 1.01], [1.56, 1.44, 1.98]],
[[0.19, 1.38, 0.05], [0.91, 1.9, 1.02], [1.63, 2.42, 1.99]],
]
]
],
dtype=np.float32,
)
) # shape = (1, 1, 2, 3, 3)
got = layer_util.warp_grid(grid=self.grid, theta=theta)
assert is_equal_tf(got, expected)
def test_dice_general():
"""
Testing general dice function with
non binary features and checking
against precomputed tensor.
"""
array_eye = 0.6 * np.identity(3, dtype=np.float32)
tensor_eye = np.zeros((3, 3, 3, 3), dtype=np.float32)
tensor_eye[:, :, 0:3, 0:3] = array_eye
tensor_eye = tf.convert_to_tensor(tensor_eye, dtype=tf.float32)
tensor_pred = np.zeros((3, 3, 3, 3), dtype=np.float32)
tensor_pred[:, 0:2, :, :] = array_eye
tensor_pred = tf.convert_to_tensor(tensor_pred, dtype=tf.float32)
y_prod = np.sum(tensor_eye * tensor_pred, axis=(1, 2, 3))
y_sum = np.sum(tensor_eye, axis=(1, 2, 3)) + np.sum(tensor_pred,
axis=(1, 2, 3))
num = 2 * y_prod
den = y_sum
expect = num / den
get = label.dice_score_generalized(tensor_eye, tensor_pred)
assert is_equal_tf(get, expect)
def test_random_transform_generator():
"""
Test random_transform_generator by confirming that it generates
appropriate solutions and output sizes for seeded examples.
"""
# Check shapes are correct Batch Size = 1 - Pass
batch_size = 1
transforms = deepreg.dataset.preprocess.gen_rand_affine_transform(
batch_size, 0)
assert transforms.shape == (batch_size, 4, 3)
# Check numerical outputs are correct for a given seed - Pass
batch_size = 1
scale = 0.1
seed = 0
expected = tf.constant(
np.array(
[[
[9.4661278e-01, -3.8267835e-03, 3.6934228e-03],
[5.5613145e-03, 9.8034811e-01, -1.8044969e-02],
[1.9651605e-04, 1.4576728e-02, 9.6243286e-01],
[-2.5107686e-03, 1.9579126e-02, -1.2195010e-02],
]],
dtype=np.float32,
)) # shape = (1, 4, 3)
got = deepreg.dataset.preprocess.gen_rand_affine_transform(
batch_size=batch_size, scale=scale, seed=seed)
assert is_equal_tf(got, expected)
def test_warp_grid():
"""
Test warp_grid by confirming that it generates
appropriate solutions for a simple precomputed case.
"""
grid = tf.constant(
np.array(
[[[[0, 0, 0], [0, 0, 1], [0, 0, 2]],
[[0, 1, 0], [0, 1, 1], [0, 1, 2]]]],
dtype=np.float32,
)) # shape = (1, 2, 3, 3)
theta = tf.constant(
np.array(
[[
[0.86, 0.75, 0.48],
[0.07, 0.98, 0.01],
[0.72, 0.52, 0.97],
[0.12, 0.4, 0.04],
]],
dtype=np.float32,
)) # shape = (1, 4, 3)
expected = tf.constant(
np.array(
[[[
[[0.12, 0.4, 0.04], [0.84, 0.92, 1.01], [1.56, 1.44, 1.98]],
[[0.19, 1.38, 0.05], [0.91, 1.9, 1.02], [1.63, 2.42, 1.99]],
]]],
dtype=np.float32,
)) # shape = (1, 1, 2, 3, 3)
got = layer_util.warp_grid(grid=grid, theta=theta)
assert is_equal_tf(got, expected)
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_output(self, y_true, y_pred, expected):
"""
Testing computed global mutual information between images
using image.global_mutual_information by comparing to precomputed.
"""
got = image.global_mutual_information(y_true=y_true, y_pred=y_pred)
assert is_equal_tf(got, expected, atol=1.0e-6)
def test_call(self, l1):
tensor = tf.ones([4, 50, 50, 50, 3])
got = deform.GradientNorm(l1=l1)(tensor)
expected = tf.zeros([
4,
])
assert is_equal_tf(got, expected)
def test_smooth(
self,
value: float,
smooth_nr: float,
smooth_dr: float,
expected: float,
):
"""
Test values in extreme cases where numerator/denominator are all zero.
:param value: value for input.
:param smooth_nr: constant for numerator.
:param smooth_dr: constant for denominator.
:param expected: target value.
"""
shape = (1, 10)
y_true = tf.ones(shape=shape) * value
y_pred = tf.ones(shape=shape) * value
got = label.DiceScore(smooth_nr=smooth_nr, smooth_dr=smooth_dr).call(
y_true,
y_pred,
)
expected = tf.constant(expected)
assert is_equal_tf(got[0], expected)
def testcall(self, y_true, y_pred, binary, background_weight, expected):
expected = np.array([expected] *
self.shape[0]) # call returns (batch, )
got = label.CrossEntropy(
binary=binary,
background_weight=background_weight,
).call(y_true=y_true, y_pred=y_pred)
assert is_equal_tf(got, expected)
def test_output(self, y_true, y_pred, kernel_type, expected):
"""
Testing computed local normalized cross correlation function by comparing the output to expected.
"""
got = image.local_normalized_cross_correlation(
y_true, y_pred, kernel_type=kernel_type
)
assert is_equal_tf(got, expected)
def test_bending_energy():
"""test the calculation of bending energy"""
tensor = tf.ones([4, 50, 50, 50, 3])
got = deform.BendingEnergy()(tensor)
expected = tf.zeros([
4,
])
assert is_equal_tf(got, expected)
def test_output(self, y_true, y_pred, expected):
"""
Testing ssd function (sum of squared differences) by comparing the output to expected.
"""
got = image.ssd(
y_true,
y_pred,
)
assert is_equal_tf(got, expected)
def test_zero_info(self, y_true, y_pred, shape, expected):
y_true = y_true * np.ones(shape=shape)
y_pred = y_pred * np.ones(shape=shape)
expected = expected * np.ones(shape=(shape[0], ))
got = image.GlobalMutualInformation().call(
y_true,
y_pred,
)
assert is_equal_tf(got, expected)
def test_output(self, y_true, y_pred, shape, expected):
y_true = y_true * np.ones(shape=shape)
y_pred = y_pred * np.ones(shape=shape)
expected = expected * np.ones(shape=(shape[0], ))
got = image.SumSquaredDifference().call(
y_true,
y_pred,
)
assert is_equal_tf(got, expected)
def test_rectangular_kernel1d(kernel_size):
"""
Testing the 1-D rectangular kernel
:param kernel_size: int
:return:
"""
expected = tf.ones(shape=(kernel_size, ), dtype=tf.float32)
got = rectangular_kernel1d(kernel_size)
assert is_equal_tf(got, expected)
def test_ssd():
"""
Testing computed sum squared error function between images using image.ssd by comparing to precomputed.
"""
tensor_true = 0.3 * np.array(range(108)).reshape((2, 3, 3, 3, 2))
tensor_pred = 0.1 * np.ones((2, 3, 3, 3, 2))
tensor_pred[:, :, :, :, :] = 1
get = image.ssd(tensor_true, tensor_pred)
expect = [70.165, 557.785]
assert is_equal_tf(get, expect)
def test_negative_loss_mixin():
"""Test DiceScore and DiceLoss have reversed sign."""
shape = (2, 3, 4, 5)
y_true = tf.random.uniform(shape=shape)
y_pred = tf.random.uniform(shape=shape)
dice_score = DiceScore().call(y_pred=y_pred, y_true=y_true)
dice_loss = DiceLoss().call(y_pred=y_pred, y_true=y_true)
assert is_equal_tf(dice_score, -dice_loss)
def test_gradient_dxyz():
"""test the calculation of gradient of a 3D images along xyz-axis"""
# gradient_dx
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.gradient_dxyz(tensor, deform.gradient_dx)
expect = tf.zeros([4, 48, 48, 48, 3])
assert is_equal_tf(get, expect)
# gradient_dy
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.gradient_dxyz(tensor, deform.gradient_dy)
expect = tf.zeros([4, 48, 48, 48, 3])
assert is_equal_tf(get, expect)
# gradient_dz
tensor = tf.ones([4, 50, 50, 50, 3])
get = deform.gradient_dxyz(tensor, deform.gradient_dz)
expect = tf.zeros([4, 48, 48, 48, 3])
assert is_equal_tf(get, expect)
def test_1d(self):
weights = tf.constant(np.array([[0.2]], dtype=np.float32))
values = tf.constant(np.array([[1], [2]], dtype=np.float32))
# expected = 1 * 0.2 + 2 * 2
expected = tf.constant(np.array([1.8], dtype=np.float32))
got = layer_util.pyramid_combination(values=values,
weight_floor=weights,
weight_ceil=1 - weights)
assert is_equal_tf(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_cauchy_kernel1d(sigma):
"""
Testing the 1-D cauchy kernel
:param sigma: float
:return:
"""
tail = int(sigma * 5)
expected = [1 / ((x / sigma)**2 + 1) for x in range(-tail, tail + 1)]
expected = expected / np.sum(expected)
got = cauchy_kernel1d(sigma)
assert is_equal_tf(got, expected)
def test_local_normalized_cross_correlation():
"""
Testing computed local normalized cross correlation function between images using image.local_normalized_cross_correlation by comparing to precomputed.
"""
tensor_true = np.array(range(24)).reshape((2, 1, 2, 3, 2))
tensor_pred = 0.6 * np.ones((2, 1, 2, 3, 2))
expect = [0.7281439, 0.9847701]
get = image.local_normalized_cross_correlation(
tensor_true, tensor_pred, kernel_size=9
)
assert is_equal_tf(get, expect)
def test_gaussian_kernel1d_sigma(sigma):
"""
Testing the 1-D gaussian kernel given sigma as input
:param sigma: float
:return:
"""
tail = int(sigma * 3)
expected = [np.exp(-0.5 * x**2 / sigma**2) for x in range(-tail, tail + 1)]
expected = expected / np.sum(expected)
got = gaussian_kernel1d_sigma(sigma)
assert is_equal_tf(got, expected)
