207 lines
6.8 KiB
Python
207 lines
6.8 KiB
Python
"""
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GTSAM Copyright 2010-2019, Georgia Tech Research Corporation,
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Atlanta, Georgia 30332-0415
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All Rights Reserved
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See LICENSE for the license information
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CustomFactor unit tests.
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Author: Fan Jiang
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"""
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import unittest
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from typing import List
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import gtsam
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import numpy as np
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from gtsam import CustomFactor, JacobianFactor, Pose2, Values
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from gtsam.utils.test_case import GtsamTestCase
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class TestCustomFactor(GtsamTestCase):
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def test_new(self):
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"""Test the creation of a new CustomFactor"""
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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"""Minimal error function stub"""
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return np.array([1, 0, 0]), H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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cf = CustomFactor(noise_model, [0], error_func)
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def test_new_keylist(self):
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"""Test the creation of a new CustomFactor"""
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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"""Minimal error function stub"""
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return np.array([1, 0, 0]), H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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cf = CustomFactor(noise_model, [0], error_func)
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def test_call(self):
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"""Test if calling the factor works (only error)"""
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expected_pose = Pose2(1, 1, 0)
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]) -> np.ndarray:
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"""Minimal error function with no Jacobian"""
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key0 = this.keys()[0]
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error = -v.atPose2(key0).localCoordinates(expected_pose)
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return error, H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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cf = CustomFactor(noise_model, [0], error_func)
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v = Values()
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v.insert(0, Pose2(1, 0, 0))
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e = cf.error(v)
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self.assertEqual(e, 0.5)
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def test_jacobian(self):
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"""Tests if the factor result matches the GTSAM Pose2 unit test"""
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gT1 = Pose2(1, 2, np.pi / 2)
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gT2 = Pose2(-1, 4, np.pi)
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expected = Pose2(2, 2, np.pi / 2)
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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"""
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the custom error function. One can freely use variables captured
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from the outside scope. Or the variables can be acquired by indexing `v`.
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Jacobian is passed by modifying the H array of numpy matrices.
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"""
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key0 = this.keys()[0]
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key1 = this.keys()[1]
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gT1, gT2 = v.atPose2(key0), v.atPose2(key1)
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error = expected.localCoordinates(gT1.between(gT2))
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if H is not None:
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result = gT1.between(gT2)
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H[0] = -result.inverse().AdjointMap()
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H[1] = np.eye(3)
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return error, H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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cf = CustomFactor(noise_model, [0, 1], error_func)
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v = Values()
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v.insert(0, gT1)
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v.insert(1, gT2)
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bf = gtsam.BetweenFactorPose2(0, 1, expected, noise_model)
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gf = cf.linearize(v)
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gf_b = bf.linearize(v)
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J_cf, b_cf = gf.jacobian()
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J_bf, b_bf = gf_b.jacobian()
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np.testing.assert_allclose(J_cf, J_bf)
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np.testing.assert_allclose(b_cf, b_bf)
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def test_printing(self):
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"""Tests if the factor result matches the GTSAM Pose2 unit test"""
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gT1 = Pose2(1, 2, np.pi / 2)
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gT2 = Pose2(-1, 4, np.pi)
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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"""Minimal error function stub"""
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return np.array([1, 0, 0]), H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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from gtsam.symbol_shorthand import X
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cf = CustomFactor(noise_model, [X(0), X(1)], error_func)
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cf_string = """CustomFactor on x0, x1
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noise model: unit (3)
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"""
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self.assertEqual(cf_string, repr(cf))
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def test_no_jacobian(self):
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"""Tests that we will not calculate the Jacobian if not requested"""
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gT1 = Pose2(1, 2, np.pi / 2)
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gT2 = Pose2(-1, 4, np.pi)
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expected = Pose2(2, 2, np.pi / 2)
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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"""
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Error function that mimics a BetweenFactor
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:param this: reference to the current CustomFactor being evaluated
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:param v: Values object
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:param H: list of references to the Jacobian arrays
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:return: the non-linear error
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"""
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key0 = this.keys()[0]
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key1 = this.keys()[1]
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gT1, gT2 = v.atPose2(key0), v.atPose2(key1)
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error = expected.localCoordinates(gT1.between(gT2))
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self.assertTrue(H is None) # Should be true if we only request the error
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if H is not None:
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result = gT1.between(gT2)
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H[0] = -result.inverse().AdjointMap()
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H[1] = np.eye(3)
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return error, H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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cf = CustomFactor(noise_model, [0, 1], error_func)
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v = Values()
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v.insert(0, gT1)
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v.insert(1, gT2)
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bf = gtsam.BetweenFactorPose2(0, 1, expected, noise_model)
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e_cf = cf.error(v)
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e_bf = bf.error(v)
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np.testing.assert_allclose(e_cf, e_bf)
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def test_optimization(self):
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"""Tests if a factor graph with a CustomFactor can be properly optimized"""
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gT1 = Pose2(1, 2, np.pi / 2)
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gT2 = Pose2(-1, 4, np.pi)
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expected = Pose2(2, 2, np.pi / 2)
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def error_func(this: CustomFactor, v: gtsam.Values, H: List[np.ndarray]):
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"""
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Error function that mimics a BetweenFactor
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:param this: reference to the current CustomFactor being evaluated
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:param v: Values object
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:param H: list of references to the Jacobian arrays
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:return: the non-linear error
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"""
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key0 = this.keys()[0]
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key1 = this.keys()[1]
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gT1, gT2 = v.atPose2(key0), v.atPose2(key1)
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error = expected.localCoordinates(gT1.between(gT2))
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if H is not None:
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result = gT1.between(gT2)
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H[0] = -result.inverse().AdjointMap()
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H[1] = np.eye(3)
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return error, H
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noise_model = gtsam.noiseModel.Unit.Create(3)
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cf = CustomFactor(noise_model, [0, 1], error_func)
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fg = gtsam.NonlinearFactorGraph()
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fg.add(cf)
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fg.add(gtsam.PriorFactorPose2(0, gT1, noise_model))
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v = Values()
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v.insert(0, Pose2(0, 0, 0))
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v.insert(1, Pose2(0, 0, 0))
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params = gtsam.LevenbergMarquardtParams()
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optimizer = gtsam.LevenbergMarquardtOptimizer(fg, v, params)
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result = optimizer.optimize()
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self.gtsamAssertEquals(result.atPose2(0), gT1, tol=1e-5)
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self.gtsamAssertEquals(result.atPose2(1), gT2, tol=1e-5)
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if __name__ == "__main__":
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unittest.main()
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