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_separable_filter_else(): """ Testing separable filter case where non zero length tensor is passed to the function. """ k = np.ones((3, 3, 3, 3)) array_eye = np.identity((3)) tensor_pred = np.zeros((3, 3, 3, 3)) tensor_pred[:, :, 0, 0] = array_eye expect = np.ones((3, 3, 3, 3)) get = label.separable_filter3d(tensor_pred, k) assert assertTensorsEqual(get, expect)
def test_separable_filter_else(): """ Testing separable filter case where non zero length tensor is passed to the function. """ k = np.ones((3, 3, 3, 3), dtype=np.float32) array_eye = np.identity(3, dtype=np.float32) tensor_pred = np.zeros((3, 3, 3, 3), dtype=np.float32) tensor_pred[:, :, 0, 0] = array_eye tensor_pred = tf.convert_to_tensor(tensor_pred, dtype=tf.float32) k = tf.convert_to_tensor(k, dtype=tf.float32) expect = np.ones((3, 3, 3, 3), dtype=np.float32) expect = tf.convert_to_tensor(expect, dtype=tf.float32) get = label.separable_filter3d(tensor_pred, k) assert is_equal_tf(get, expect)