def test_multi_scale_loss_true_len():
"""
Test assertion error raised if a wrongly sized tensor
is passed to the multi-scale loss function.
"""
tensor_true = np.zeros((3, 3, 3))
tensor_pred = np.zeros((3, 3, 3, 3))
with pytest.raises(AssertionError):
label.multi_scale_loss(tensor_true,
tensor_pred,
loss_type="jaccard",
loss_scales=[0, 1, 2])
def test_multi_scale_loss_kernel():
"""
Test multi-scale loss kernel returns the appropriate
loss tensor for same inputs and jaccard cal.
"""
loss_values = np.asarray([1, 2, 3])
array_eye = np.identity((3))
tensor_pred = np.zeros((3, 3, 3, 3))
tensor_eye = np.zeros((3, 3, 3, 3))
tensor_eye[:, :, 0:3, 0:3] = array_eye
tensor_pred[:, :, 0, 0] = array_eye
tensor_eye = tf.convert_to_tensor(tensor_eye, dtype=tf.double)
tensor_pred = tf.convert_to_tensor(tensor_pred, dtype=tf.double)
list_losses = np.array([
label.single_scale_loss(
y_true=label.separable_filter3d(tensor_eye,
label.gauss_kernel1d(s)),
y_pred=label.separable_filter3d(tensor_pred,
label.gauss_kernel1d(s)),
loss_type="jaccard",
) for s in loss_values
])
expect = np.mean(list_losses, axis=0)
get = label.multi_scale_loss(tensor_eye, tensor_pred, "jaccard",
loss_values)
assert assertTensorsEqual(get, expect)
def test_multi_scale_loss_kernel():
"""
Test multi-scale loss kernel returns the appropriate
loss tensor for same inputs and jaccard cal.
"""
loss_values = [1, 2, 3]
array_eye = np.identity(3, dtype=np.float32)
tensor_pred = np.zeros((3, 3, 3, 3), dtype=np.float32)
tensor_eye = np.zeros((3, 3, 3, 3), dtype=np.float32)
tensor_eye[:, :, 0:3, 0:3] = array_eye
tensor_pred[:, :, 0, 0] = array_eye
tensor_eye = tf.convert_to_tensor(tensor_eye, dtype=tf.float32)
tensor_pred = tf.convert_to_tensor(tensor_pred, dtype=tf.float32)
expect = tf.constant([0.9938445, 0.9924956, 0.9938445], dtype=tf.float32)
get = label.multi_scale_loss(tensor_eye, tensor_pred, "jaccard",
loss_values)
assert is_equal_tf(get, expect)