def test_inputs_missing_image_raises_value_error(self):
inputs = {
'depth': tf.ones((50, 50, 1)),
'ignore_label': 255,
}
with self.assertRaises(ValueError):
preprocessor.preprocess(inputs=inputs)
def test_points_with_wrong_dimension_raises_value_error(self):
inputs = {
'points': tf.zeros((1000, 1, 3)),
'normals': tf.zeros((1000, 1, 3)),
'colors': tf.zeros((1000, 3), dtype=tf.uint8),
'semantic_labels': tf.zeros((1000, 1), dtype=tf.int32),
}
with self.assertRaises(ValueError):
preprocessor.preprocess(inputs=inputs)
def test_preprocess_points_with_padding(self):
for is_training in [True, False]:
points = tf.random.uniform(
(1000, 3), minval=10.0, maxval=50.0, dtype=tf.float32)
normals = tf.random.uniform(
(1000, 3), minval=-0.5, maxval=0.5, dtype=tf.float32)
colors = tf.random.uniform((1000, 3),
minval=0,
maxval=255,
dtype=tf.int32)
semantic_labels = tf.random.uniform((1000, 1),
minval=0,
maxval=10,
dtype=tf.int32)
colors = tf.cast(colors, dtype=tf.uint8)
points_centered = points - tf.expand_dims(
tf.reduce_mean(points, axis=0), axis=0)
points_centered = tf.pad(points_centered, paddings=[[0, 1000], [0, 0]])
inputs = {
'points': points,
'normals': normals,
'colors': colors,
'semantic_labels': semantic_labels,
'ignore_label': 255,
}
inputs = preprocessor.preprocess(
inputs=inputs,
z_min_degree_rotation=-50.0,
z_max_degree_rotation=50.0,
is_training=is_training,
points_pad_or_clip_size=2000)
self.assertEqual(inputs['ignore_label'], 255)
inputs = {
k: v for k, v in six.iteritems(inputs) if isinstance(v, tf.Tensor)
}
self.assertEqual(
[2000, 3],
list(inputs[standard_fields.InputDataFields.point_positions].shape))
self.assertEqual(
[2000, 3],
list(inputs[standard_fields.InputDataFields.point_normals].shape))
self.assertEqual(
[2000, 3],
list(inputs[standard_fields.InputDataFields.point_colors].shape))
self.assertEqual(
[2000, 1],
list(
inputs[standard_fields.InputDataFields.point_loss_weights].shape))
self.assertEqual(1000,
inputs[standard_fields.InputDataFields.num_valid_points])
self.assertAllClose(
points_centered.numpy()[:, 2],
inputs[standard_fields.InputDataFields.point_positions].numpy()[:, 2])
def test_empty_inputs_raises_value_error(self):
with self.assertRaises(ValueError):
empty_input = {}
preprocessor.preprocess(inputs=empty_input)
