def testKnnIndicesMaxDistance(self):
points = tf.constant([[[1, 1, 1], [2, 2, 2], [4, 4, 4], [5, 5, 5]]],
dtype=tf.float32)
query_points = tf.constant([[
[1, 1, 1],
[4, 4, 4],
]], dtype=tf.float32)
valid_num = tf.constant([4], dtype=tf.int32)
# Max distance so that one neighbor can be selected.
max_distance = 2.0
expected_1nn = np.array([[[0], [2]]], dtype=np.int32)
expected_1nn_padding = np.array([[[0], [0]]], dtype=np.float32)
expected_2nn = np.array([[[0, 1], [2, 3]]], dtype=np.int32)
expected_2nn_padding = np.array([[[0, 0], [0, 0]]], dtype=np.float32)
expected_3nn = np.array([[[0, 1, 0], [2, 3, 2]]], dtype=np.int32)
expected_3nn_padding = np.array([[[0, 0, 1], [0, 0, 1]]], dtype=np.float32)
with self.session() as sess:
output_1nn, output_1nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 1, valid_num, max_distance))
self.assertAllEqual(output_1nn, expected_1nn)
self.assertAllEqual(output_1nn_padding, expected_1nn_padding)
output_2nn, output_2nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 2, valid_num, max_distance))
self.assertAllEqual(output_2nn, expected_2nn)
self.assertAllEqual(output_2nn_padding, expected_2nn_padding)
output_3nn, output_3nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 3, valid_num, max_distance))
self.assertAllEqual(output_3nn, expected_3nn)
self.assertAllEqual(output_3nn_padding, expected_3nn_padding)
# Max distance so that only itself can be selected.
max_distance = 0.1
expected_1nn = np.array([[[0], [2]]], dtype=np.int32)
expected_1nn_padding = np.array([[[0], [0]]], dtype=np.float32)
expected_2nn = np.array([[[0, 0], [2, 2]]], dtype=np.int32)
expected_2nn_padding = np.array([[[0, 1], [0, 1]]], dtype=np.float32)
expected_3nn = np.array([[[0, 0, 0], [2, 2, 2]]], dtype=np.int32)
expected_3nn_padding = np.array([[[0, 1, 1], [0, 1, 1]]], dtype=np.float32)
with self.session() as sess:
output_1nn, output_1nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 1, valid_num, max_distance))
self.assertAllEqual(output_1nn, expected_1nn)
self.assertAllEqual(output_1nn_padding, expected_1nn_padding)
output_2nn, output_2nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 2, valid_num, max_distance))
self.assertAllEqual(output_2nn, expected_2nn)
self.assertAllEqual(output_2nn_padding, expected_2nn_padding)
output_3nn, output_3nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 3, valid_num, max_distance))
self.assertAllEqual(output_3nn, expected_3nn)
self.assertAllEqual(output_3nn_padding, expected_3nn_padding)
def testKnnIndices(self):
points = tf.constant([[[1, 1], [2, 2], [4, 4], [5, 5]]], dtype=tf.float32)
query_points = tf.constant([[
[1, 1],
[4, 4],
]], dtype=tf.float32)
# Case 1: when all points are valid.
valid_num = tf.constant([4], dtype=tf.int32)
expected_1nn = np.array([[[0], [2]]], dtype=np.int32)
expected_2nn = np.array([[[0, 1], [2, 3]]], dtype=np.int32)
expected_3nn = np.array([[[0, 1, 2], [2, 3, 1]]], dtype=np.int32)
with self.session() as sess:
output_1nn, _ = sess.run(
car_lib.KnnIndices(points, query_points, 1, valid_num))
self.assertAllEqual(output_1nn, expected_1nn)
output_2nn, _ = sess.run(
car_lib.KnnIndices(points, query_points, 2, valid_num))
self.assertAllEqual(output_2nn, expected_2nn)
output_3nn, _ = sess.run(
car_lib.KnnIndices(points, query_points, 3, valid_num))
self.assertAllEqual(output_3nn, expected_3nn)
# Case 2: not all points are valid.
valid_num = tf.constant([2], dtype=tf.int32)
expected_1nn = np.array([[[0], [1]]], dtype=np.int32)
expected_1nn_padding = np.array([[[0], [0]]], dtype=np.float32)
expected_2nn = np.array([[[0, 1], [1, 0]]], dtype=np.int32)
expected_2nn_padding = np.array([[[0, 0], [0, 0]]], dtype=np.float32)
expected_3nn = np.array([[[0, 1, 2], [1, 0, 2]]], dtype=np.int32)
expected_3nn_padding = np.array([[[0, 0, 1], [0, 0, 1]]], dtype=np.float32)
with self.session() as sess:
output_1nn, output_1nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 1, valid_num))
self.assertAllEqual(output_1nn, expected_1nn)
self.assertAllEqual(output_1nn_padding, expected_1nn_padding)
output_2nn, output_2nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 2, valid_num))
self.assertAllEqual(output_2nn, expected_2nn)
self.assertAllEqual(output_2nn_padding, expected_2nn_padding)
output_3nn, output_3nn_padding = sess.run(
car_lib.KnnIndices(points, query_points, 3, valid_num))
self.assertAllEqual(output_3nn, expected_3nn)
self.assertAllEqual(output_3nn_padding, expected_3nn_padding)
# Case 3: explicit padding.
padding = tf.constant([[1, 0, 1, 0]])
expected_1nn = np.array([[[1], [3]]], dtype=np.int32)
expected_1nn_padding = np.array([[[0], [0]]], dtype=np.float32)
expected_2nn = np.array([[[1, 3], [3, 1]]], dtype=np.int32)
expected_2nn_padding = np.array([[[0, 0], [0, 0]]], dtype=np.float32)
expected_3nn = np.array([[[1, 3, 0], [3, 1, 2]]], dtype=np.int32)
expected_3nn_padding = np.array([[[0, 0, 1], [0, 0, 1]]], dtype=np.float32)
with self.session() as sess:
output_1nn, output_1nn_padding = sess.run(
car_lib.NeighborhoodIndices(points, query_points, 1, padding))
self.assertAllEqual(output_1nn, expected_1nn)
self.assertAllEqual(output_1nn_padding, expected_1nn_padding)
output_2nn, output_2nn_padding = sess.run(
car_lib.NeighborhoodIndices(points, query_points, 2, padding))
self.assertAllEqual(output_2nn, expected_2nn)
self.assertAllEqual(output_2nn_padding, expected_2nn_padding)
output_3nn, output_3nn_padding = sess.run(
car_lib.NeighborhoodIndices(points, query_points, 3, padding))
self.assertAllEqual(output_3nn, expected_3nn)
self.assertAllEqual(output_3nn_padding, expected_3nn_padding)