def test_interpolate_preset(self, points, weights, indices, _, out):
"""Tests whether interpolation results are correct."""
weights = tf.convert_to_tensor(value=weights)
result_unnormalized = weighted.interpolate(
points=points, weights=weights, indices=indices, normalize=False)
result_normalized = weighted.interpolate(
points=points, weights=2.0 * weights, indices=indices, normalize=True)
estimated_unnormalized = self.evaluate(result_unnormalized)
estimated_normalized = self.evaluate(result_normalized)
self.assertAllClose(estimated_unnormalized, out)
self.assertAllClose(estimated_normalized, out)
def test_interp_unnormalizable_raised_(self, points, weights, indices, _):
"""Tests whether exception is raised when weights are unnormalizable."""
with self.assertRaises(tf.errors.InvalidArgumentError):
result = weighted.interpolate(points=points,
weights=weights,
indices=indices,
normalize=True,
allow_negative_weights=True)
self.evaluate(result)
def test_interpolate_negative_weights_raised(self, dim_points, num_points,
num_outputs,
num_pts_to_interpolate):
"""Tests whether exception is raised when weights are negative."""
points, weights, indices = self._get_tensors_from_shapes(
num_points, dim_points, num_outputs, num_pts_to_interpolate)
weights *= -1.0
with self.assertRaises(tf.errors.InvalidArgumentError):
result = weighted.interpolate(
points=points, weights=weights, indices=indices, normalize=True)
self.evaluate(result)
def test_interpolate_jacobian_random(self, dim_points, num_points,
num_outputs, num_pts_to_interpolate):
"""Tests whether jacobian is correct."""
points_np, weights_np, indices_np = self._get_tensors_from_shapes(
num_points, dim_points, num_outputs, num_pts_to_interpolate)
points = tf.convert_to_tensor(value=points_np)
weights = tf.convert_to_tensor(value=weights_np)
indices = tf.convert_to_tensor(value=indices_np)
y = weighted.interpolate(points=points,
weights=weights,
indices=indices,
normalize=True)
with self.subTest(name="points"):
self.assert_jacobian_is_correct(points, points_np, y)
with self.subTest(name="weights"):
self.assert_jacobian_is_correct(weights, weights_np, y)
def test_interpolate_jacobian_random(self, dim_points, num_points,
num_outputs, num_pts_to_interpolate):
"""Tests whether jacobian is correct."""
points_np, weights_np, indices_np = self._get_tensors_from_shapes(
num_points, dim_points, num_outputs, num_pts_to_interpolate)
# Wrap these in identities because some assert_* ops look at the constant
# tensor value and mark it as unfeedable.
points = tf.identity(tf.convert_to_tensor(value=points_np))
weights = tf.identity(tf.convert_to_tensor(value=weights_np))
indices = tf.identity(tf.convert_to_tensor(value=indices_np))
y = weighted.interpolate(
points=points, weights=weights, indices=indices, normalize=True)
with self.subTest(name="points"):
self.assert_jacobian_is_correct(points, points_np, y)
with self.subTest(name="weights"):
self.assert_jacobian_is_correct(weights, weights_np, y)
def interpolate_fn(points, weights):
return weighted.interpolate(
points=points, weights=weights, indices=indices_np, normalize=True)
