Merged in feature/BAD_using_charts (pull request #41)
Working on a prototype of wrapping external typesrelease/4.3a0
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c570f53e57
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* @file chartTesting.h
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* @brief
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* @date November, 2014
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* @author Paul Furgale
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*/
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#pragma once
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Manifold.h>
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#include <gtsam/base/Testable.h>
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#include <CppUnitLite/TestResult.h>
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#include <CppUnitLite/Test.h>
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#include <CppUnitLite/Failure.h>
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namespace gtsam {
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// Do a full concept check and test the invertibility of local() vs. retract().
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template<typename T>
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void testDefaultChart(TestResult& result_,
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const std::string& name_,
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const T& value) {
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typedef typename gtsam::DefaultChart<T> Chart;
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typedef typename Chart::vector Vector;
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// First, check the basic chart concept. This checks that the interface is satisfied.
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// The rest of the function is even more detailed, checking the correctness of the chart.
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BOOST_CONCEPT_ASSERT((ChartConcept<Chart>));
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T other = value;
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// Check for the existence of a print function.
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gtsam::traits::print<T>()(value, "value");
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gtsam::traits::print<T>()(other, "other");
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// Check for the existence of "equals"
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EXPECT(gtsam::traits::equals<T>()(value, other, 1e-12));
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// Check that the dimension of the local value matches the chart dimension.
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Vector dx = Chart::local(value, other);
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EXPECT_LONGS_EQUAL(Chart::getDimension(value), dx.size());
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// And that the "local" of a value vs. itself is zero.
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EXPECT(assert_equal(Matrix(dx), Matrix(Eigen::VectorXd::Zero(dx.size()))));
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// Test the invertibility of retract/local
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dx.setRandom();
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T updated = Chart::retract(value, dx);
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Vector invdx = Chart::local(value, updated);
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EXPECT(assert_equal(Matrix(dx), Matrix(invdx), 1e-9));
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// And test that negative steps work as well.
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dx = -dx;
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updated = Chart::retract(value, dx);
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invdx = Chart::local(value, updated);
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EXPECT(assert_equal(Matrix(dx), Matrix(invdx), 1e-9));
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}
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} // namespace gtsam
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/// \brief Perform a concept check on the default chart for a type.
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/// \param value An instantiation of the type to be tested.
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#define CHECK_CHART_CONCEPT(value) \
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{ gtsam::testDefaultChart(result_, name_, value); }
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@ -30,6 +30,13 @@
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Manifold.h>
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#include <gtsam/linear/VectorValues.h>
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#include <gtsam/linear/JacobianFactor.h>
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#include <gtsam/nonlinear/Values.h>
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#include <CppUnitLite/TestResult.h>
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#include <CppUnitLite/Test.h>
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#include <CppUnitLite/Failure.h>
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namespace gtsam {
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@ -516,4 +523,72 @@ inline Matrix numericalHessian323(double (*f)(const X1&, const X2&, const X3&),
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boost::function<double(const X1&, const X2&, const X3&)>(f), x1, x2, x3,
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delta);
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}
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// The benefit of this method is that it does not need to know what types are involved
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// to evaluate the factor. If all the machinery of gtsam is working correctly, we should
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// get the correct numerical derivatives out the other side.
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template<typename FactorType>
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JacobianFactor computeNumericalDerivativeJacobianFactor(const FactorType& factor,
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const Values& values,
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double fd_step) {
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Eigen::VectorXd e = factor.unwhitenedError(values);
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const size_t rows = e.size();
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std::map<Key, Matrix> jacobians;
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typename FactorType::const_iterator key_it = factor.begin();
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VectorValues dX = values.zeroVectors();
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for(; key_it != factor.end(); ++key_it) {
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size_t key = *key_it;
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// Compute central differences using the values struct.
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const size_t cols = dX.dim(key);
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Matrix J = Matrix::Zero(rows, cols);
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for(size_t col = 0; col < cols; ++col) {
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Eigen::VectorXd dx = Eigen::VectorXd::Zero(cols);
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dx[col] = fd_step;
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dX[key] = dx;
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Values eval_values = values.retract(dX);
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Eigen::VectorXd left = factor.unwhitenedError(eval_values);
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dx[col] = -fd_step;
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dX[key] = dx;
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eval_values = values.retract(dX);
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Eigen::VectorXd right = factor.unwhitenedError(eval_values);
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J.col(col) = (left - right) * (1.0/(2.0 * fd_step));
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}
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jacobians[key] = J;
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}
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// Next step...return JacobianFactor
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return JacobianFactor(jacobians, -e);
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}
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template<typename FactorType>
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void testFactorJacobians(TestResult& result_,
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const std::string& name_,
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const FactorType& f,
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const gtsam::Values& values,
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double fd_step,
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double tolerance) {
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// Check linearization
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JacobianFactor expected = computeNumericalDerivativeJacobianFactor(f, values, fd_step);
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boost::shared_ptr<GaussianFactor> gf = f.linearize(values);
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boost::shared_ptr<JacobianFactor> jf = //
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boost::dynamic_pointer_cast<JacobianFactor>(gf);
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typedef std::pair<Eigen::MatrixXd, Eigen::VectorXd> Jacobian;
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Jacobian evalJ = jf->jacobianUnweighted();
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Jacobian estJ = expected.jacobianUnweighted();
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EXPECT(assert_equal(evalJ.first, estJ.first, tolerance));
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EXPECT(assert_equal(evalJ.second, Eigen::VectorXd::Zero(evalJ.second.size()), tolerance));
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EXPECT(assert_equal(estJ.second, Eigen::VectorXd::Zero(evalJ.second.size()), tolerance));
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}
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} // namespace gtsam
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/// \brief Check the Jacobians produced by a factor against finite differences.
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/// \param factor The factor to test.
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/// \param values Values filled in for testing the Jacobians.
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/// \param numerical_derivative_step The step to use when computing the numerical derivative Jacobians
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/// \param tolerance The numerical tolerance to use when comparing Jacobians.
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#define EXPECT_CORRECT_FACTOR_JACOBIANS(factor, values, numerical_derivative_step, tolerance) \
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{ gtsam::testFactorJacobians(result_, name_, factor, values, numerical_derivative_step, tolerance); }
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/base/lieProxies.h>
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#include <gtsam/base/ChartTesting.h>
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#include <boost/math/constants/constants.hpp>
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static double error = 1e-9, epsilon = 0.001;
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static const Matrix I3 = eye(3);
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/* ************************************************************************* */
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TEST( Rot3, chart)
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{
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Matrix R = (Matrix(3, 3) << 0, 1, 0, 1, 0, 0, 0, 0, -1);
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Rot3 rot3(R);
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CHECK_CHART_CONCEPT(rot3);
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}
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/* ************************************************************************* */
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TEST( Rot3, constructor)
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{
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@ -19,6 +19,7 @@
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#include <gtsam/navigation/MagFactor.h>
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#include <gtsam/base/Testable.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/base/LieScalar.h>
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#include <CppUnitLite/TestHarness.h>
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* @brief Expressions for Block Automatic Differentiation
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*/
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#pragma once
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#include <gtsam_unstable/nonlinear/Expression.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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#include <gtsam/base/Testable.h>
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@ -0,0 +1,53 @@
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file expressionTesting.h
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* @date September 18, 2014
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* @author Frank Dellaert
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* @author Paul Furgale
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* @brief Test harness methods for expressions.
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*/
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#pragma once
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#include "Expression.h"
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#include "ExpressionFactor.h"
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#include <gtsam/base/Matrix.h>
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#include <gtsam/base/Testable.h>
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#include <CppUnitLite/TestResult.h>
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#include <CppUnitLite/Test.h>
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#include <CppUnitLite/Failure.h>
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#include <gtsam/base/numericalDerivative.h>
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namespace gtsam {
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template<typename T>
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void testExpressionJacobians(TestResult& result_,
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const std::string& name_,
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const gtsam::Expression<T>& expression,
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const gtsam::Values& values,
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double nd_step,
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double tolerance) {
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// Create factor
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size_t size = traits::dimension<T>::value;
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ExpressionFactor<T> f(noiseModel::Unit::Create(size), expression.value(values), expression);
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testFactorJacobians(result_, name_, f, values, nd_step, tolerance);
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}
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} // namespace gtsam
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/// \brief Check the Jacobians produced by an expression against finite differences.
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/// \param expression The expression to test.
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/// \param values Values filled in for testing the Jacobians.
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/// \param numerical_derivative_step The step to use when computing the finite difference Jacobians
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/// \param tolerance The numerical tolerance to use when comparing Jacobians.
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#define EXPECT_CORRECT_EXPRESSION_JACOBIANS(expression, values, numerical_derivative_step, tolerance) \
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{ gtsam::testExpressionJacobians(result_, name_, expression, values, numerical_derivative_step, tolerance); }
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#include <gtsam_unstable/nonlinear/ceres_example.h>
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#undef CHECK
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#include <CppUnitLite/TestHarness.h>
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#include <boost/assign/list_of.hpp>
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@ -19,6 +19,7 @@
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#include <gtsam_unstable/slam/expressions.h>
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#include <gtsam_unstable/nonlinear/ExpressionFactor.h>
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#include <gtsam_unstable/nonlinear/ExpressionTesting.h>
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#include <gtsam/slam/GeneralSFMFactor.h>
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#include <gtsam/slam/ProjectionFactor.h>
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#include <gtsam/slam/PriorFactor.h>
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EXPECT( assert_equal(expected, *jf,1e-9));
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}
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TEST(ExpressionFactor, tree_finite_differences) {
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// Create some values
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Values values;
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values.insert(1, Pose3());
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values.insert(2, Point3(0, 0, 1));
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values.insert(3, Cal3_S2());
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// Create leaves
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Pose3_ x(1);
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Point3_ p(2);
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Cal3_S2_ K(3);
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// Create expression tree
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Point3_ p_cam(x, &Pose3::transform_to, p);
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Point2_ xy_hat(PinholeCamera<Cal3_S2>::project_to_camera, p_cam);
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Point2_ uv_hat(K, &Cal3_S2::uncalibrate, xy_hat);
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const double fd_step = 1e-5;
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const double tolerance = 1e-5;
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EXPECT_CORRECT_EXPRESSION_JACOBIANS(uv_hat, values, fd_step, tolerance);
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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