/
transformations_test.py
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/
transformations_test.py
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"""Test cases for various transformation functions."""
import tensorflow as tf
import numpy as np
import transformations_tf as ttf
import transformations as t
from tensorflow.python.framework import test_util
from tensorflow.python.platform import googletest
# is_close
# all_close
# identity_matrix
# translation_from_matrix
# reflection_matrix
# rotation_matrix
# homogeneous_rotation_matrix
# euler_matrix_nh
# euler_matrix
# quaternion_from_euler
# quaternion_about_axis
# quaternion_matrix_nh
# quaternion_matrix
# quaternion_multiply
# quaternion_conjugate
# quaternion_inverse
# quaternion_real
# quaternion_imag
# vector_norm2
# vector_norm
# unit_vector
# outer
# vector_product
# angle_between_vectors
# inverse_matrix
# concatenate_matrices
# is_same_transform
# is_same_quaternion
def _random_quaternion(dtype):
return t.random_quaternion().astype(dtype)
def _listwrap(value):
if isinstance(value, (float, int, np.ndarray)):
value = value,
assert(isinstance(value, (list, tuple)))
return value
class TransformationsTestCpu(test_util.TensorFlowTestCase):
"""
Class for testing `transformations_tf` functions.
Tests only run for cpu implementations.
"""
_use_gpu = False
_batch_len = 6
def _np_tf_compare(
self, input_fn, tf_fn, np_fn,
dtypes=[tf.float32, tf.float64]):
"""Compare numpy and tensorflow implementations."""
with self.test_session(use_gpu=self._use_gpu) as sess:
for dtype in dtypes:
np_dtype = dtype.as_numpy_dtype
in_np = [_listwrap(input_fn(np_dtype))
for i in range(self._batch_len)]
out_np = [_listwrap(np_fn(*inp)) for inp in in_np]
in_np = [np.array(z) for z in zip(*in_np)]
out_np = [np.array(z) for z in zip(*out_np)]
in_tf = [tf.constant(i, dtype=dtype) for i in in_np]
out_tf = _listwrap(sess.run(tf_fn(*in_tf)))
for o_tf, o_np in zip(out_tf, out_np):
if len(o_tf.shape) != len(o_np.shape):
for o_npi in o_np:
self.assertAllClose(o_tf, o_npi)
else:
self.assertAllClose(o_tf, o_np)
def test_close(self):
"""Test is_close, all_close."""
shape = (5, 4, 7)
def input_fn(dtype):
x = np.random.random(shape).astype(dtype)
y = (np.random.random(shape)*1e-6).astype(dtype)
return x, x + y
def tf_fn(x, z):
return ttf.is_close(x, z), \
ttf.all_close(x, z, axis=tuple(range(1, len(shape)+1)))
def np_fn(x, z):
return np.isclose(x, z), np.allclose(x, z)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_outer(self):
"""Test tensorflow implementation of outer."""
def input_fn(dtype):
return [np.random.random(3).astype(dtype) for i in range(2)]
self._np_tf_compare(input_fn, ttf.outer, np.outer)
def test_vector_product(self):
"""Test tensorflow implementation of vector_product."""
def input_fn(dtype):
return [np.random.random(3).astype(dtype) for i in range(2)]
self._np_tf_compare(input_fn, ttf.vector_product, t.vector_product)
def test_angle_between_vectors(self):
"""Test tensorflow implementation of angle_between_vectors."""
def input_fn(dtype):
return [np.random.random(3).astype(dtype) for i in range(2)]
self._np_tf_compare(
input_fn,
ttf.angle_between_vectors,
t.angle_between_vectors)
def test_identity_matrix(self):
"""Test identity_matrix function."""
def input_fn(dtype):
return t.identity_matrix().astype(dtype)
def tf_fn(in_tf):
return ttf.identity_matrix(dtype=in_tf.dtype)
def np_fn(in_np):
return in_np
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_translation_from_matrix(self):
"""Test tensorflow implementation of translation_from_matrix."""
def input_fn(dtype):
return np.random.random((4, 4)).astype(dtype)
def tf_fn(m):
return ttf.translation_from_matrix(m)
def np_fn(m):
return t.translation_from_matrix(m)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_reflection_matrix(self):
"""Test tensorflow implementation of reflection_matrix."""
def input_fn(dtype):
return np.random.random((4, 4)).astype(dtype)
def tf_fn(m):
return ttf.translation_from_matrix(m)
def np_fn(m):
return t.translation_from_matrix(m)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_homogeneous_rotation_matrix(self):
"""Test tensorflow implementation of homogeneous_rotation_matrix."""
def input_fn(dtype):
m = t.random_rotation_matrix().astype(dtype)
R = m[:3, :3]
return m, R
def tf_fn(m, R):
return ttf.homogeneous_rotation_matrix(R)
def np_fn(m, R):
return m
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_rotation_matrix_nh(self):
"""Test tensorflow implementation of rotation_matrix_nh."""
def input_fn(dtype):
angle = np.array(np.random.random()*2*np.pi, dtype=dtype)
axis = np.random.random(3).astype(dtype)
return angle, axis
def tf_fn(angle, axis):
r = ttf.rotation_matrix_nh(angle, axis)
return r
def np_fn(angle, axis):
return t.rotation_matrix(angle, axis)[:3, :3].astype(angle.dtype)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_rotation_matrix(self):
"""Test tensorflow implementation of rotation_matrix."""
def input_fn(dtype):
angle = np.array(np.random.random()*2*np.pi, dtype=dtype)
axis = np.random.random(3).astype(dtype)
return angle, axis
def tf_fn(angle, axis):
return ttf.rotation_matrix(angle, axis)
def np_fn(angle, axis):
return t.rotation_matrix(angle, axis).astype(angle.dtype)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_euler_matrix_nh(self):
"""Test tensorflow implementation of euler_matrix_nh."""
def input_fn(dtype):
return (np.random.random(3)*2*np.pi).astype(dtype)
def tf_fn(angles):
return ttf.euler_matrix_nh(*[angles[..., i] for i in range(3)])
def np_fn(angles):
return t.euler_matrix(*angles)[:3, :3].astype(angles.dtype)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_euler_matrix(self):
"""Test tensorflow implementation of euler_matrix."""
def input_fn(dtype):
return (np.random.random(3)*2*np.pi).astype(dtype)
def tf_fn(angles):
return ttf.euler_matrix(*[angles[..., i] for i in range(3)])
def np_fn(angles):
return t.euler_matrix(*angles).astype(angles.dtype)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_quaternion_from_euler(self):
"""Test tensorflow implementation of quaternion_from_euler."""
def input_fn(dtype):
return (np.random.random(3)*2*np.pi).astype(dtype)
def tf_fn(angles):
return ttf.quaternion_from_euler(
*[angles[..., i] for i in range(3)])
def np_fn(angles):
return t.quaternion_from_euler(*angles).astype(angles.dtype)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_quaternion_about_axis(self):
"""Test tensorflow implementation of quaternion_about_axis."""
def input_fn(dtype):
angle = np.array(np.random.random()*2*np.pi, dtype=dtype)
axis = np.random.random(3).astype(dtype)
return angle, axis
def tf_fn(angle, axis):
return ttf.quaternion_about_axis(angle, axis)
def np_fn(angle, axis):
return t.quaternion_about_axis(angle, axis)
self._np_tf_compare(input_fn, tf_fn, np_fn)
def test_quaternion_matrix_nh(self):
"""Test tensorflow implementation of quaternion_matrix_nh."""
def np_fn(q):
return t.quaternion_matrix(q)[:3, :3].astype(q.dtype)
self._np_tf_compare(
_random_quaternion,
ttf.quaternion_matrix_nh,
np_fn)
def test_quaternion_matrix(self):
"""Test tensorflow implementation of quaternion_matrix."""
self._np_tf_compare(
_random_quaternion,
ttf.quaternion_matrix,
t.quaternion_matrix)
def test_quaternion_multiply(self):
"""Test tensorflow implementation of quaternion_multiply."""
def input_fn(dtype):
return [_random_quaternion(dtype) for i in range(2)]
self._np_tf_compare(
input_fn, ttf.quaternion_multiply, t.quaternion_multiply)
def test_quaternion_conjugate(self):
"""Test tensorflow implementation of quaternion_conjugate."""
self._np_tf_compare(
_random_quaternion,
ttf.quaternion_conjugate,
t.quaternion_conjugate)
def test_quaternion_inverse(self):
"""Test tensorflow implementation of quaternion_inverse."""
self._np_tf_compare(
_random_quaternion,
ttf.quaternion_inverse,
t.quaternion_inverse)
def test_quaternion_real(self):
"""Test tensorflow implementation of quaternion_real."""
self._np_tf_compare(
_random_quaternion,
ttf.quaternion_real,
t.quaternion_real)
def test_quaternion_imag(self):
"""Test tensorflow implementation of quaternion_imag."""
self._np_tf_compare(
_random_quaternion,
ttf.quaternion_imag,
t.quaternion_imag)
def test_vector_norm2(self):
"""Test tensorflow implementation of vector_norm2."""
shape = (5, 4, 7)
def input_fn(dtype):
return np.random.random(shape).astype(dtype)
def np_fn(v):
return np.sum(v**2, axis=-1)
self._np_tf_compare(input_fn, ttf.vector_norm2, np_fn)
def test_vector_norm(self):
"""Test tensorflow implementation of vector_norm."""
shape = (5, 4, 7)
def input_fn(dtype):
return np.random.random(shape).astype(dtype)
def np_fn(v):
return np.sqrt(np.sum(v**2, axis=-1))
self._np_tf_compare(input_fn, ttf.vector_norm, np_fn)
def test_unit_vector(self):
"""Test tensorflow implementation of unit_vector."""
def input_fn(dtype):
return np.random.random(3).astype(dtype)
self._np_tf_compare(input_fn, ttf.unit_vector, t.unit_vector)
def test_inverse_matrix(self):
"""Test tensorflow implementation of inverse_matrix."""
def input_fn(dtype):
return np.random.random((5, 5)).astype(dtype)
self._np_tf_compare(input_fn, ttf.inverse_matrix, t.inverse_matrix)
def test_concatenate_matrices(self):
"""Test tensorflow implementation of concatenate_matrices."""
def input_fn(dtype):
return tuple(np.random.random((4, 4)) for i in range(5))
self._np_tf_compare(
input_fn,
ttf.concatenate_matrices,
t.concatenate_matrices)
def test_is_same_transform(self):
"""Test tensorflow implementation of is_same_transform."""
def input_fn_true(dtype):
m0 = np.random.random((4, 4)).astype(dtype)
m1 = 5*m0
return m0, m1
def input_fn_false(dtype):
m0 = np.random.random((4, 4)).astype(dtype)
m1 = np.random.random((4, 4)).astype(dtype)
return m0, m1
for input_fn in [input_fn_true, input_fn_false]:
self._np_tf_compare(
input_fn, ttf.is_same_transform, t.is_same_transform)
def test_is_same_quaternion(self):
"""Test tensorflow implementation of is_same_quaternion."""
def input_fn_pos(dtype):
q = _random_quaternion(dtype)
return q, q
def input_fn_neg(dtype):
q = _random_quaternion(dtype)
return q, q
def input_fn_false(dtype):
return [_random_quaternion(dtype) for i in range(2)]
for input_fn in [input_fn_pos, input_fn_neg, input_fn_false]:
self._np_tf_compare(
input_fn, ttf.is_same_quaternion, t.is_same_quaternion)
class TransformationsTestGpu(TransformationsTestCpu):
"""
Class for testing `transformations_tf` functions.
Tests only run for gpu implementations.
"""
_use_gpu = False
if __name__ == "__main__":
googletest.main()