Fixed and simplified (quite broken) AdaptAutoDiff, now works with fixed Vectors
Frank Dellaert
parent
bded06f97f
commit
b752f8446c
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@ -27,95 +27,44 @@
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namespace gtsam {
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namespace detail {
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// By default, we assume an Identity element
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template<typename T, typename structure_category>
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struct Origin { T operator()() { return traits<T>::Identity();} };
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// but simple manifolds don't have one, so we just use the default constructor
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template<typename T>
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struct Origin<T, manifold_tag> { T operator()() { return T();} };
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} // \ detail
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/**
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* Canonical is a template that creates canonical coordinates for a given type.
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* A simple manifold type (i.e., not a Lie Group) has no concept of identity,
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* hence in that case we use the value given by the default constructor T() as
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* the origin of a "canonical coordinate" parameterization.
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*/
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template<typename T>
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struct Canonical {
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GTSAM_CONCEPT_MANIFOLD_TYPE(T)
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typedef traits<T> Traits;
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enum { dimension = Traits::dimension };
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typedef typename Traits::TangentVector TangentVector;
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typedef typename Traits::structure_category Category;
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typedef detail::Origin<T, Category> Origin;
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static TangentVector Local(const T& other) {
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return Traits::Local(Origin()(), other);
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}
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static T Retract(const TangentVector& v) {
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return Traits::Retract(Origin()(), v);
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}
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};
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/**
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* The AdaptAutoDiff class uses ceres-style autodiff to adapt a ceres-style
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* Function evaluation, i.e., a function F that defines an operator
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* template<typename T> bool operator()(const T* const, const T* const, T* predicted) const;
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* Function evaluation, i.e., a function FUNCTOR that defines an operator
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* template<typename T> bool operator()(const T* const, const T* const, T*
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* predicted) const;
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* For now only binary operators are supported.
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*/
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template<typename F, typename T, typename A1, typename A2>
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template <typename FUNCTOR, int M, int N1, int N2>
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class AdaptAutoDiff {
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typedef Eigen::Matrix<double, M, N1, Eigen::RowMajor> RowMajor1;
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typedef Eigen::Matrix<double, M, N2, Eigen::RowMajor> RowMajor2;
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static const int N = traits<T>::dimension;
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static const int M1 = traits<A1>::dimension;
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static const int M2 = traits<A2>::dimension;
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typedef Eigen::Matrix<double, M, 1> VectorT;
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typedef Eigen::Matrix<double, N1, 1> Vector1;
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typedef Eigen::Matrix<double, N2, 1> Vector2;
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typedef Eigen::Matrix<double, N, M1, Eigen::RowMajor> RowMajor1;
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typedef Eigen::Matrix<double, N, M2, Eigen::RowMajor> RowMajor2;
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typedef Canonical<T> CanonicalT;
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typedef Canonical<A1> Canonical1;
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typedef Canonical<A2> Canonical2;
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typedef typename CanonicalT::TangentVector VectorT;
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typedef typename Canonical1::TangentVector Vector1;
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typedef typename Canonical2::TangentVector Vector2;
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F f;
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FUNCTOR f;
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public:
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T operator()(const A1& a1, const A2& a2, OptionalJacobian<N, M1> H1 = boost::none,
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OptionalJacobian<N, M2> H2 = boost::none) {
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VectorT operator()(const Vector1& v1, const Vector2& v2,
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OptionalJacobian<M, N1> H1 = boost::none,
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OptionalJacobian<M, N2> H2 = boost::none) {
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using ceres::internal::AutoDiff;
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// Make arguments
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Vector1 v1 = Canonical1::Local(a1);
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Vector2 v2 = Canonical2::Local(a2);
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bool success;
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VectorT result;
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if (H1 || H2) {
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// Get derivatives with AutoDiff
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double *parameters[] = { v1.data(), v2.data() };
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double rowMajor1[N * M1], rowMajor2[N * M2]; // on the stack
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const double* parameters[] = {v1.data(), v2.data()};
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double rowMajor1[M * N1], rowMajor2[M * N2]; // on the stack
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double* jacobians[] = {rowMajor1, rowMajor2};
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success = AutoDiff<F, double, 9, 3>::Differentiate(f, parameters, 2,
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result.data(), jacobians);
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success = AutoDiff<FUNCTOR, double, N1, N2>::Differentiate(
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f, parameters, M, result.data(), jacobians);
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// Convert from row-major to columnn-major
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// TODO: if this is a bottleneck (probably not!) fix Autodiff to be Column-Major
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// TODO: if this is a bottleneck (probably not!) fix Autodiff to be
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// Column-Major
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*H1 = Eigen::Map<RowMajor1>(rowMajor1);
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*H2 = Eigen::Map<RowMajor2>(rowMajor2);
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@ -126,9 +75,8 @@ public:
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if (!success)
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throw std::runtime_error(
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"AdaptAutoDiff: function call resulted in failure");
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return CanonicalT::Retract(result);
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return result;
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}
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};
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}
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} // namespace gtsam
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@ -37,9 +37,9 @@ namespace gtsam {
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// Special version of Cal3Bundler so that default constructor = 0,0,0
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struct Cal3Bundler0 : public Cal3Bundler {
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Cal3Bundler0(double f = 0, double k1 = 0, double k2 = 0, double u0 = 0, double v0 = 0) :
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Cal3Bundler(f, k1, k2, u0, v0) {
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}
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Cal3Bundler0(double f = 0, double k1 = 0, double k2 = 0, double u0 = 0,
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double v0 = 0)
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: Cal3Bundler(f, k1, k2, u0, v0) {}
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Cal3Bundler0 retract(const Vector& d) const {
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return Cal3Bundler0(fx() + d(0), k1() + d(1), k2() + d(2), u0(), v0());
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}
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@ -53,7 +53,6 @@ struct traits<Cal3Bundler0> : public internal::Manifold<Cal3Bundler0> {};
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// With that, camera below behaves like Snavely's 9-dim vector
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typedef PinholeCamera<Cal3Bundler0> Camera;
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}
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using namespace std;
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@ -69,57 +68,11 @@ TEST(AdaptAutoDiff, Rotation) {
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EXPECT(assert_equal(expected, actual));
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}
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/* ************************************************************************* */
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// Canonical<T> sets up Local/Retract around the default-constructed value
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// The tests below check this for all types that play a role in SFM
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TEST(AdaptAutoDiff, Canonical) {
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typedef Canonical<Point2> Chart1;
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EXPECT(Chart1::Local(Point2(1, 0))==Vector2(1, 0));
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EXPECT(Chart1::Retract(Vector2(1, 0))==Point2(1, 0));
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Vector2 v2(1, 0);
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typedef Canonical<Vector2> Chart2;
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EXPECT(assert_equal(v2, Chart2::Local(Vector2(1, 0))));
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EXPECT(Chart2::Retract(v2)==Vector2(1, 0));
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typedef Canonical<double> Chart3;
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Eigen::Matrix<double, 1, 1> v1;
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v1 << 1;
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EXPECT(Chart3::Local(1)==v1);
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EXPECT_DOUBLES_EQUAL(Chart3::Retract(v1), 1, 1e-9);
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typedef Canonical<Point3> Chart4;
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Point3 point(1, 2, 3);
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Vector3 v3(1, 2, 3);
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EXPECT(assert_equal(v3, Chart4::Local(point)));
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EXPECT(assert_equal(Chart4::Retract(v3), point));
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typedef Canonical<Pose3> Chart5;
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Pose3 pose(Rot3::identity(), point);
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Vector v6(6);
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v6 << 0, 0, 0, 1, 2, 3;
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EXPECT(assert_equal(v6, Chart5::Local(pose)));
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EXPECT(assert_equal(Chart5::Retract(v6), pose));
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typedef Canonical<Cal3Bundler0> Chart6;
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Cal3Bundler0 cal0;
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Vector z3 = Vector3::Zero();
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EXPECT(assert_equal(z3, Chart6::Local(cal0)));
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EXPECT(assert_equal(Chart6::Retract(z3), cal0));
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typedef Canonical<Camera> Chart7;
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Camera camera(Pose3(), cal0);
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Vector z9 = Vector9::Zero();
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EXPECT(assert_equal(z9, Chart7::Local(camera)));
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EXPECT(assert_equal(Chart7::Retract(z9), camera));
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}
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/* ************************************************************************* */
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// Some Ceres Snippets copied for testing
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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template<typename T> inline T &RowMajorAccess(T *base, int rows, int cols,
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int i, int j) {
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template <typename T>
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inline T& RowMajorAccess(T* base, int rows, int cols, int i, int j) {
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return base[cols * i + j];
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}
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@ -137,10 +90,10 @@ struct Projective {
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bool operator()(A const P[12], A const X[4], A x[2]) const {
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A PX[3];
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for (int i = 0; i < 3; ++i) {
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PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0]
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+ RowMajorAccess(P, 3, 4, i, 1) * X[1]
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+ RowMajorAccess(P, 3, 4, i, 2) * X[2]
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+ RowMajorAccess(P, 3, 4, i, 3) * X[3];
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PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0] +
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RowMajorAccess(P, 3, 4, i, 1) * X[1] +
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RowMajorAccess(P, 3, 4, i, 2) * X[2] +
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RowMajorAccess(P, 3, 4, i, 3) * X[3];
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}
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if (PX[2] != 0.0) {
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x[0] = PX[0] / PX[2];
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@ -169,8 +122,8 @@ TEST(AdaptAutoDiff, AutoDiff) {
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Projective projective;
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// Make arguments
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typedef Eigen::Matrix<double, 3, 4, Eigen::RowMajor> M;
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M P;
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typedef Eigen::Matrix<double, 3, 4, Eigen::RowMajor> RowMajorMatrix34;
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RowMajorMatrix34 P;
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P << 1, 0, 0, 0, 0, 1, 0, 5, 0, 0, 1, 0;
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Vector4 X(10, 0, 5, 1);
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@ -180,16 +133,18 @@ TEST(AdaptAutoDiff, AutoDiff) {
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EXPECT(assert_equal(expected, actual, 1e-9));
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// Get expected derivatives
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Matrix E1 = numericalDerivative21<Vector2, M, Vector4>(Projective(), P, X);
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Matrix E2 = numericalDerivative22<Vector2, M, Vector4>(Projective(), P, X);
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Matrix E1 = numericalDerivative21<Vector2, RowMajorMatrix34, Vector4>(
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Projective(), P, X);
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Matrix E2 = numericalDerivative22<Vector2, RowMajorMatrix34, Vector4>(
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Projective(), P, X);
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// Get derivatives with AutoDiff
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Vector2 actual2;
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MatrixRowMajor H1(2, 12), H2(2, 4);
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double* parameters[] = {P.data(), X.data()};
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double* jacobians[] = {H1.data(), H2.data()};
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CHECK(
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(AutoDiff<Projective, double, 12, 4>::Differentiate( projective, parameters, 2, actual2.data(), jacobians)));
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CHECK((AutoDiff<Projective, double, 12, 4>::Differentiate(
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projective, parameters, 2, actual2.data(), jacobians)));
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EXPECT(assert_equal(E1, H1, 1e-8));
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EXPECT(assert_equal(E2, H2, 1e-8));
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}
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@ -211,9 +166,11 @@ namespace example {
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Camera camera(Pose3(Rot3::rodriguez(0.1, 0.2, 0.3), Point3(0, 5, 0)),
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Cal3Bundler0(1, 0, 0));
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Point3 point(10, 0, -5); // negative Z-axis convention of Snavely!
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Vector9 P = Canonical<Camera>::Local(camera);
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Vector3 X = Canonical<Point3>::Local(point);
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Point2 expectedMeasurement(1.2431567, 1.2525694);
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Vector9 P = Camera().localCoordinates(camera);
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Vector3 X = Point3::Logmap(point);
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Vector2 expectedMeasurement(1.2431567, 1.2525694);
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Matrix E1 = numericalDerivative21<Vector2, Vector9, Vector3>(adapted, P, X);
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Matrix E2 = numericalDerivative22<Vector2, Vector9, Vector3>(adapted, P, X);
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}
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/* ************************************************************************* */
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@ -234,11 +191,7 @@ TEST(AdaptAutoDiff, AutoDiff2) {
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// Apply the mapping, to get image point b_x.
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Vector2 actual = adapted(P, X);
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EXPECT(assert_equal(expectedMeasurement.vector(), actual, 1e-6));
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// Get expected derivatives
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Matrix E1 = numericalDerivative21<Vector2, Vector9, Vector3>(adapted, P, X);
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Matrix E2 = numericalDerivative22<Vector2, Vector9, Vector3>(adapted, P, X);
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EXPECT(assert_equal(expectedMeasurement, actual, 1e-6));
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// Instantiate function
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SnavelyProjection snavely;
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@ -259,21 +212,17 @@ TEST(AdaptAutoDiff, AutoDiff2) {
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TEST(AdaptAutoDiff, AdaptAutoDiff) {
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using namespace example;
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typedef AdaptAutoDiff<SnavelyProjection, Point2, Camera, Point3> Adaptor;
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typedef AdaptAutoDiff<SnavelyProjection, 2, 9, 3> Adaptor;
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Adaptor snavely;
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// Apply the mapping, to get image point b_x.
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Point2 actual = snavely(camera, point);
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Vector2 actual = snavely(P, X);
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EXPECT(assert_equal(expectedMeasurement, actual, 1e-6));
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// // Get expected derivatives
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Matrix E1 = numericalDerivative21<Point2, Camera, Point3>(Adaptor(), camera, point);
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Matrix E2 = numericalDerivative22<Point2, Camera, Point3>(Adaptor(), camera, point);
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// Get derivatives with AutoDiff, not gives RowMajor results!
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Matrix29 H1;
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Matrix23 H2;
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Point2 actual2 = snavely(camera, point, H1, H2);
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Vector2 actual2 = snavely(P, X, H1, H2);
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EXPECT(assert_equal(expectedMeasurement, actual2, 1e-6));
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EXPECT(assert_equal(E1, H1, 1e-8));
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EXPECT(assert_equal(E2, H2, 1e-8));
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@ -282,15 +231,15 @@ TEST(AdaptAutoDiff, AdaptAutoDiff) {
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/* ************************************************************************* */
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// Test AutoDiff wrapper in an expression
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TEST(AdaptAutoDiff, SnavelyExpression) {
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Expression<Camera> P(1);
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Expression<Point3> X(2);
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typedef AdaptAutoDiff<SnavelyProjection, Point2, Camera, Point3> Adaptor;
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Expression<Point2> expression(Adaptor(), P, X);
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Expression<Vector9> P(1);
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Expression<Vector3> X(2);
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typedef AdaptAutoDiff<SnavelyProjection, 2, 9, 3> Adaptor;
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Expression<Vector2> expression(Adaptor(), P, X);
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EXPECT_LONGS_EQUAL(
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sizeof(internal::BinaryExpression<Vector2, Vector9, Vector3>::Record),
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expression.traceSize());
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#ifdef GTSAM_USE_QUATERNIONS
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EXPECT_LONGS_EQUAL(384,expression.traceSize()); // TODO(frank): should be zero
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#else
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EXPECT_LONGS_EQUAL(sizeof(internal::BinaryExpression<Point2, Camera, Point3>::Record),
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expression.traceSize()); // TODO(frank): should be zero
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EXPECT_LONGS_EQUAL(336, expression.traceSize());
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#endif
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set<Key> expected = list_of(1)(2);
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EXPECT(expected == expression.keys());
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@ -302,4 +251,3 @@ int main() {
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return TestRegistry::runAllTests(tr);
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}
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/* ************************************************************************* */
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