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remove np_utils Matrix and Vector functions 

Not needed anymore.
release/4.3a0
Duy-Nguyen Ta 2017-03-15 13:54:00 -04:00
parent 6bf7ea23cf
commit 685b0cb62f
11 changed files with 90 additions and 89 deletions
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26
cython/README.md
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@ -35,12 +35,18 @@ See the tests for examples.
## Some important notes:
- Vector/Matrix: Due to a design choice of eigency, numpy.array matrices with the default order='A'
will always be transposed in C++ no matter how you transpose it in Python. Use order='F', or use
two functions Vector and Matrix in cython/gtsam/utils/np_utils.py for your conveniences. These two functions
also help to avoid a common but very subtle bug of using integers when creating numpy arrays,
e.g. np.array([1,2,3]). These can't be an input for gtsam functions as they only accept floating-point arrays.
For more details, see: https://github.com/wouterboomsma/eigency#storage-layout---why-arrays-are-sometimes-transposed
- Vector/Matrix:
+ GTSAM expects double-precision floating point vectors and matrices.
Hence, you should pass numpy matrices with dtype=float, or 'float64'.
+ Also, GTSAM expects *column-major* matrices, unlike the default storage
scheme in numpy. Hence, you should pass column-major matrices to gtsam using
the flag order='F'. And you always get column-major matrices back.
For more details, see: https://github.com/wouterboomsma/eigency#storage-layout---why-arrays-are-sometimes-transposed
+ Passing row-major matrices of different dtype, e.g. 'int', will also work
as the wrapper converts them to column-major and dtype float for you,
using numpy.array.astype(float, order='F', copy=False).
However, this will result a copy if your matrix is not in the expected type
and storage order.
- Inner namespace: Classes in inner namespace will be prefixed by <innerNamespace>_ in Python.
Examples: noiseModel_Gaussian, noiseModel_mEstimator_Tukey
@ -110,12 +116,20 @@ KNOWN ISSUES
TODO
=====
☐ Unify cython/gtsam.h and the original gtsam.h
✔ 06-03-17: manage to remove the requirements for default and copy constructors
- 25-11-16:
Try to unify but failed. Main reasons are: Key/size_t, std containers, KeyVector/KeyList/KeySet.
Matlab doesn't need to know about Key, but I can't make Cython to ignore Key as it couldn't cast KeyVector, i.e. FastVector<Key>,
to FastVector<size_t>.
☐ Unit tests for cython wrappers
☐ Fix Python tests: don't use " import <package> * ": Bad style!!!
☐ Marginal and JointMarginal: revert changes
- add doc for generate
- matlab 6 arguments?
Completed/Cancelled:
✔ Convert input numpy Matrix/Vector to float dtype and storage order 'F' automatically, cannot crash! @done (15-03-17 13:00)
✔ Remove requirements.txt - Frank: don't bother with only 2 packages and a special case for eigency! @done (08-03-17 10:30)
✔ CMake install script @done (25-11-16 02:30)
✘ [REFACTOR] better name for uninstantiateClass: very vague!! @cancelled (25-11-16 02:30) -- lazy
✘ forward declaration? @cancelled (23-11-16 13:00) - nothing to do, seem to work?

4
cython/gtsam/tests/experiments.py
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@ -3,9 +3,9 @@ This file contains small experiments to test the wrapper with gtsam_short,
not real unittests. Its name convention is different from other tests so it
won't be discovered.
"""
from gtsam.gtsam import *
from gtsam import *
import numpy as np
from gtsam.utils import Vector, Matrix
from utils import Vector, Matrix
r = Rot3()
print(r)

61
cython/gtsam/tests/test_JacobianFactor.py
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@ -2,36 +2,39 @@ import unittest
from gtsam import *
from math import *
import numpy as np
from gtsam.utils import Matrix, Vector
class TestJacobianFactor(unittest.TestCase):
def test_eliminate(self):
Ax2 = Matrix(
[-5., 0.],
# Recommended way to specify a matrix (see cython/README)
Ax2 = np.array(
[[-5., 0.],
[+0., -5.],
[10., 0.],
[+0., 10.])
[+0., 10.]], order='F')
Al1 = Matrix(
[5., 0.],
[0., 5.],
[0., 0.],
[0., 0.])
# This is good too
Al1 = np.array(
[[5, 0],
[0, 5],
[0, 0],
[0, 0]], dtype=float, order = 'F')
Ax1 = Matrix(
[0.00, 0.], # f4
[0.00, 0.], # f4
[-10., 0.], # f2
[0.00, -10.]) # f2
# Not recommended for performance reasons, but should still work
# as the wrapper should convert it to the correct type and storage order
Ax1 = np.array(
[[0, 0], # f4
[0, 0], # f4
[-10, 0], # f2
[0, -10]]) # f2
x2 = 1
l1 = 2
x1 = 3
# the RHS
b2 = Vector(-1., 1.5, 2., -1.)
sigmas = Vector(1., 1., 1., 1.)
b2 = np.array([-1., 1.5, 2., -1.])
sigmas = np.array([1., 1., 1., 1.])
model4 = noiseModel_Diagonal.Sigmas(sigmas)
combined = JacobianFactor(x2, Ax2, l1, Al1, x1, Ax1, b2, model4)
@ -42,29 +45,29 @@ class TestJacobianFactor(unittest.TestCase):
actualCG, lf = combined.eliminate(ord)
# create expected Conditional Gaussian
R11 = Matrix([11.1803, 0.00],
[0.00, 11.1803])
S12 = Matrix([-2.23607, 0.00],
[+0.00, -2.23607])
S13 = Matrix([-8.94427, 0.00],
[+0.00, -8.94427])
d = Vector(2.23607, -1.56525)
R11 = np.array([[11.1803, 0.00],
[0.00, 11.1803]])
S12 = np.array([[-2.23607, 0.00],
[+0.00, -2.23607]])
S13 = np.array([[-8.94427, 0.00],
[+0.00, -8.94427]])
d = np.array([2.23607, -1.56525])
expectedCG = GaussianConditional(
x2, d, R11, l1, S12, x1, S13, noiseModel_Unit.Create(2))
# check if the result matches
self.assertTrue(actualCG.equals(expectedCG, 1e-4))
# the expected linear factor
Bl1 = Matrix([4.47214, 0.00],
[0.00, 4.47214])
Bl1 = np.array([[4.47214, 0.00],
[0.00, 4.47214]])
Bx1 = Matrix(
Bx1 = np.array(
# x1
[-4.47214, 0.00],
[+0.00, -4.47214])
[[-4.47214, 0.00],
[+0.00, -4.47214]])
# the RHS
b1 = Vector(0.0, 0.894427)
b1 = np.array([0.0, 0.894427])
model2 = noiseModel_Diagonal.Sigmas(np.array([1., 1.]))
expectedLF = JacobianFactor(l1, Bl1, x1, Bx1, b1, model2)

3
cython/gtsam/tests/test_SFMExample.py
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@ -2,7 +2,6 @@ import unittest
from gtsam import *
from math import *
import numpy as np
from gtsam.utils import Vector, Matrix
import gtsam.utils.visual_data_generator as generator
@ -17,7 +16,7 @@ class TestSFMExample(unittest.TestCase):
measurementNoiseSigma = 1.0
pointNoiseSigma = 0.1
poseNoiseSigmas = Vector([0.001, 0.001, 0.001, 0.1, 0.1, 0.1])
poseNoiseSigmas = np.array([0.001, 0.001, 0.001, 0.1, 0.1, 0.1])
graph = NonlinearFactorGraph()

3
cython/gtsam/tests/test_StereoVOExample.py
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@ -2,7 +2,6 @@ import unittest
from gtsam import *
from math import *
import numpy as np
from gtsam.utils import Vector, Matrix
class TestStereoVOExample(unittest.TestCase):
@ -32,7 +31,7 @@ class TestStereoVOExample(unittest.TestCase):
## Create realistic calibration and measurement noise model
# format: fx fy skew cx cy baseline
K = Cal3_S2Stereo(1000, 1000, 0, 320, 240, 0.2)
stereo_model = noiseModel_Diagonal.Sigmas(Vector([1.0, 1.0, 1.0]))
stereo_model = noiseModel_Diagonal.Sigmas(np.array([1.0, 1.0, 1.0]))
## Add measurements
# pose 1

20
cython/gtsam/tests/test_Values.py
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@ -22,11 +22,25 @@ class TestValues(unittest.TestCase):
values.insert(9, E)
values.insert(10, imuBias_ConstantBias())
# special cases for Vector and Matrix:
vec = np.array([1., 2., 3.])
# Special cases for Vectors and Matrices
# Note that gtsam's Eigen Vectors and Matrices requires double-precision
# floating point numbers in column-major (Fortran style) storage order,
# whereas by default, numpy.array is in row-major order and the type is
# in whatever the number input type is, e.g. np.array([1,2,3])
# will have 'int' type.
#
# The wrapper will automatically fix the type and storage order for you,
# but for performace reasons, it's recommended to specify the correct
# type and storage order.
vec = np.array([1., 2., 3.]) # for vectors, the order is not important, but dtype still is
values.insert(11, vec)
mat = np.array([[1., 2.], [3., 4.]], order='F')
values.insert(12, mat)
# Test with dtype int and the default order='C'
# This still works as the wrapper converts to the correct type and order for you
# but is nornally not recommended!
mat2 = np.array([[1,2,],[3,5]])
values.insert(13, mat2)
self.assertTrue(values.atPoint2(0).equals(Point2(), tol))
self.assertTrue(values.atPoint3(1).equals(Point3(), tol))
@ -46,6 +60,8 @@ class TestValues(unittest.TestCase):
self.assertTrue(np.allclose(vec, actualVector, tol))
actualMatrix = values.atMatrix(12)
self.assertTrue(np.allclose(mat, actualMatrix, tol))
actualMatrix2 = values.atMatrix(13)
self.assertTrue(np.allclose(mat2, actualMatrix2, tol))
if __name__ == "__main__":
unittest.main()

1
cython/gtsam/tests/test_VisualISAMExample.py
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@ -2,7 +2,6 @@ import unittest
from gtsam import *
from math import *
import numpy as np
from gtsam.utils import Vector, Matrix
import gtsam.utils.visual_data_generator as generator
import gtsam.utils.visual_isam as visual_isam

1
cython/gtsam/utils/__init__.py
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@ -1 +0,0 @@
from .np_utils import *

4
cython/gtsam/utils/circlePose3.py
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@ -1,6 +1,6 @@
from gtsam import *
from math import *
from np_utils import *
import numpy as np
def circlePose3(numPoses = 8, radius = 1.0, symbolChar = 0):
"""
@ -23,7 +23,7 @@ def circlePose3(numPoses = 8, radius = 1.0, symbolChar = 0):
values = gtsam.Values()
theta = 0.0
dtheta = 2*pi/numPoses
gRo = gtsam.Rot3(Matrix([0., 1., 0.], [1., 0., 0.], [0., 0., -1.]))
gRo = gtsam.Rot3(np.array([[0., 1., 0.], [1., 0., 0.], [0., 0., -1.]], order='F'))
for i in range(numPoses):
key = gtsam.symbol(symbolChar, i)
gti = gtsam.Point3(radius*cos(theta), radius*sin(theta), 0)

27
cython/gtsam/utils/np_utils.py
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@ -1,27 +0,0 @@
import numpy as np
def Vector(*args):
"""
Convenient function to create numpy vector to use with gtsam cython wrapper
Usage: Vector(1), Vector(1,2,3), Vector(3,2,4)
"""
ret = np.squeeze(np.asarray(args, dtype='float'))
if ret.ndim == 0:
ret = np.expand_dims(ret, axis=0)
return ret
def Matrix(*args):
"""
Convenient function to create numpy matrix to use with gtsam cython wrapper
Usage: Matrix([1])
Matrix([1,2,3])
Matrix((3,2,4))
Matrix([1,2,3],[2,3,4])
Matrix((1,2,3),(2,3,4))
"""
ret = np.asarray(args, dtype='float', order='F')
if ret.ndim == 1:
ret = np.expand_dims(ret, axis=0)
return ret

13
cython/gtsam/utils/visual_data_generator.py
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@ -59,11 +59,11 @@ class Data:
# Set Noise parameters
self.noiseModels = Data.NoiseModels()
self.noiseModels.posePrior = noiseModel_Diagonal.Sigmas(
Vector([0.001, 0.001, 0.001, 0.1, 0.1, 0.1]))
np.array([0.001, 0.001, 0.001, 0.1, 0.1, 0.1]))
# noiseModels.odometry = noiseModel_Diagonal.Sigmas(
# Vector([0.001,0.001,0.001,0.1,0.1,0.1]))
# np.array([0.001,0.001,0.001,0.1,0.1,0.1]))
self.noiseModels.odometry = noiseModel_Diagonal.Sigmas(
Vector([0.05, 0.05, 0.05, 0.2, 0.2, 0.2]))
np.array([0.05, 0.05, 0.05, 0.2, 0.2, 0.2]))
self.noiseModels.pointPrior = noiseModel_Isotropic.Sigma(3, 0.1)
self.noiseModels.measurement = noiseModel_Isotropic.Sigma(2, 1.0)
@ -84,8 +84,7 @@ def generate_data(options):
r = 10
for j in range(len(truth.points)):
theta = j * 2 * pi / nrPoints
truth.points[j] = Point3(
v=Vector([r * cos(theta), r * sin(theta), 0]))
truth.points[j] = Point3(r * cos(theta), r * sin(theta), 0)
else: # 3D landmarks as vertices of a cube
truth.points = [Point3(10, 10, 10),
Point3(-10, 10, 10),
@ -101,9 +100,9 @@ def generate_data(options):
r = 40
for i in range(options.nrCameras):
theta = i * 2 * pi / options.nrCameras
t = Point3(Vector(r * cos(theta), r * sin(theta), height))
t = Point3(r * cos(theta), r * sin(theta), height)
truth.cameras[i] = SimpleCamera.Lookat(
t, Point3(), Point3(Vector(0, 0, 1)), truth.K)
t, Point3(), Point3(0, 0, 1), truth.K)
# Create measurements
for j in range(nrPoints):
# All landmarks seen in every frame