def testSampleFewerCentersThanPoints(self, cmethod, nmethod):
b, n, m, k = 1, 100, 128, 8
g = tf.Graph()
with g.as_default():
points = tf.random.uniform(shape=(b, n, 3))
points_padding = tf.zeros(shape=(b, n))
center, center_padding, indices, indices_padding = ops.sample_points(
points=points,
points_padding=points_padding,
num_seeded_points=0,
center_selector=cmethod,
neighbor_sampler=nmethod,
num_centers=m,
center_z_min=-np.inf,
center_z_max=np.inf,
num_neighbors=k,
max_distance=0.25)
# Ensure shapes are known at graph construction.
self.assertListEqual(center.shape.as_list(), [b, m])
self.assertListEqual(center_padding.shape.as_list(), [b, m])
self.assertListEqual(indices.shape.as_list(), [b, m, k])
self.assertListEqual(indices_padding.shape.as_list(), [b, m, k])
with self.session(graph=g) as sess:
p, c = sess.run([center_padding, center])
self.assertAllEqual(p[0, :n], np.zeros([n]))
self.assertAllEqual(p[0, n:], np.ones([m - n]))
tf.logging.info('c[:n]=%s', c[0, :n])
self.assertAllEqual(np.sort(c[0, :n]), np.arange(n))
self.assertAllEqual(c[0, n:], np.zeros([m - n]))
def testBasic(self, cmethod, nmethod, nalgo):
b, n, m, k = 3, 10000, 128, 128
g = tf.Graph()
with g.as_default():
points = tf.random.uniform(shape=(b, n, 3))
points_padding = tf.zeros(shape=(b, n))
center, center_padding, indices, indices_padding = ops.sample_points(
points=points,
points_padding=points_padding,
num_seeded_points=0,
center_selector=cmethod,
neighbor_sampler=nmethod,
neighbor_algorithm=nalgo,
num_centers=m,
center_z_min=-np.inf,
center_z_max=np.inf,
num_neighbors=k,
max_distance=0.25)
# Ensure shapes are known at graph construction.
self.assertListEqual(center.shape.as_list(), [b, m])
self.assertListEqual(center_padding.shape.as_list(), [b, m])
self.assertListEqual(indices.shape.as_list(), [b, m, k])
self.assertListEqual(indices_padding.shape.as_list(), [b, m, k])
with self.session(graph=g) as sess:
cp, c, i, p = sess.run(
[center_padding, center, indices, indices_padding])
# Very basic validity checking.
self.assertEqual(cp.shape, (b, m))
self.assertTrue(np.all(cp == 0.0))
self.assertEqual(c.shape, (b, m))
self.assertTrue(np.all(np.logical_and(0 <= c, c < n)))
self.assertEqual(i.shape, (b, m, k))
self.assertTrue(np.all(np.logical_and(0 <= i, i < n)))
self.assertEqual(p.shape, (b, m, k))
self.assertTrue(np.all(np.logical_or(0. == p, 1. == p)))
def testZFilter(self, cmethod, nmethod):
b, n, m, k = 1, 10000, 128, 128
g = tf.Graph()
with g.as_default():
points = tf.random.uniform(shape=(b, n, 3))
points_padding = tf.zeros(shape=(b, n))
center, center_padding, indices, indices_padding = ops.sample_points(
points=points,
points_padding=points_padding,
num_seeded_points=0,
center_selector=cmethod,
neighbor_sampler=nmethod,
num_centers=m,
center_z_min=0.25,
center_z_max=0.75,
num_neighbors=k,
max_distance=0.25)
# Ensure shapes are known at graph construction.
self.assertListEqual(center.shape.as_list(), [b, m])
self.assertListEqual(center_padding.shape.as_list(), [b, m])
self.assertListEqual(indices.shape.as_list(), [b, m, k])
self.assertListEqual(indices_padding.shape.as_list(), [b, m, k])
with self.session(graph=g) as sess:
c1, p1 = sess.run([center, points])
c2, p2 = sess.run([center, points])
# With extremely high probability, sampling centers twice should be
# different.
self.assertGreater(np.setdiff1d(c1, c2).size, 0)
# Centers should be filtered by z range.
self.assertTrue((0.25 <= p1[0, c1[0], 2]).all())
self.assertTrue((p1[0, c1[0], 2] <= 0.75).all())
self.assertTrue((0.25 <= p2[0, c2[0], 2]).all())
self.assertTrue((p2[0, c2[0], 2] <= 0.75).all())