internal/ceres/autodiff_test.cc - ceres-solver - Git at Google

 // Ceres Solver - A fast non-linear least squares minimizer
 // Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
 // http://code.google.com/p/ceres-solver/
 //
 // Redistribution and use in source and binary forms, with or without
 // modification, are permitted provided that the following conditions are met:
 //
 // * Redistributions of source code must retain the above copyright notice,
 //   this list of conditions and the following disclaimer.
 // * Redistributions in binary form must reproduce the above copyright notice,
 //   this list of conditions and the following disclaimer in the documentation
 //   and/or other materials provided with the distribution.
 // * Neither the name of Google Inc. nor the names of its contributors may be
 //   used to endorse or promote products derived from this software without
 //   specific prior written permission.
 //
 // THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 // AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 // IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
 // ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
 // LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
 // CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
 // SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
 // INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
 // CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
 // ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
 // POSSIBILITY OF SUCH DAMAGE.
 //
 // Author: keir@google.com (Keir Mierle)

 #include "ceres/internal/autodiff.h"

 #include "gtest/gtest.h"
 #include "ceres/random.h"

 namespace ceres {
 namespace internal {

 template <typename T> inline
 T &RowMajorAccess(T *base, int rows, int cols, int i, int j) {
   return base[cols * i + j];
 }

 // Do (symmetric) finite differencing using the given function object 'b' of
 // type 'B' and scalar type 'T' with step size 'del'.
 //
 // The type B should have a signature
 //
 //   bool operator()(T const *, T *) const;
 //
 // which maps a vector of parameters to a vector of outputs.
 template <typename B, typename T, int M, int N> inline
 bool SymmetricDiff(const B& b,
                    const T par[N],
                    T del,           // step size.
                    T fun[M],
                    T jac[M * N]) {  // row-major.
   if (!b(par, fun)) {
     return false;
   }

   // Temporary parameter vector.
   T tmp_par[N];
   for (int j = 0; j < N; ++j) {
     tmp_par[j] = par[j];
   }

   // For each dimension, we do one forward step and one backward step in
   // parameter space, and store the output vector vectors in these vectors.
   T fwd_fun[M];
   T bwd_fun[M];

   for (int j = 0; j < N; ++j) {
     // Forward step.
     tmp_par[j] = par[j] + del;
     if (!b(tmp_par, fwd_fun)) {
       return false;
     }

     // Backward step.
     tmp_par[j] = par[j] - del;
     if (!b(tmp_par, bwd_fun)) {
       return false;
     }

     // Symmetric differencing:
     //   f'(a) = (f(a + h) - f(a - h)) / (2 h)
     for (int i = 0; i < M; ++i) {
       RowMajorAccess(jac, M, N, i, j) =
           (fwd_fun[i] - bwd_fun[i]) / (T(2) * del);
     }

     // Restore our temporary vector.
     tmp_par[j] = par[j];
   }

   return true;
 }

 template <typename A> inline
 void QuaternionToScaledRotation(A const q[4], A R[3 * 3]) {
   // Make convenient names for elements of q.
   A a = q[0];
   A b = q[1];
   A c = q[2];
   A d = q[3];
   // This is not to eliminate common sub-expression, but to
   // make the lines shorter so that they fit in 80 columns!
   A aa = a*a;
   A ab = a*b;
   A ac = a*c;
   A ad = a*d;
   A bb = b*b;
   A bc = b*c;
   A bd = b*d;
   A cc = c*c;
   A cd = c*d;
   A dd = d*d;
 #define R(i, j) RowMajorAccess(R, 3, 3, (i), (j))
   R(0, 0) =  aa+bb-cc-dd; R(0, 1) = A(2)*(bc-ad); R(0, 2) = A(2)*(ac+bd);  // NOLINT
   R(1, 0) = A(2)*(ad+bc); R(1, 1) =  aa-bb+cc-dd; R(1, 2) = A(2)*(cd-ab);  // NOLINT
   R(2, 0) = A(2)*(bd-ac); R(2, 1) = A(2)*(ab+cd); R(2, 2) =  aa-bb-cc+dd;  // NOLINT
 #undef R
 }

 // A structure for projecting a 3x4 camera matrix and a
 // homogeneous 3D point, to a 2D inhomogeneous point.
 struct Projective {
   // Function that takes P and X as separate vectors:
   //   P, X -> x
   template <typename A>
   bool operator()(A const P[12], A const X[4], A x[2]) const {
     A PX[3];
     for (int i = 0; i < 3; ++i) {
       PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0] +
               RowMajorAccess(P, 3, 4, i, 1) * X[1] +
               RowMajorAccess(P, 3, 4, i, 2) * X[2] +
               RowMajorAccess(P, 3, 4, i, 3) * X[3];
     }
     if (PX[2] != 0.0) {
       x[0] = PX[0] / PX[2];
       x[1] = PX[1] / PX[2];
       return true;
     }
     return false;
   }

   // Version that takes P and X packed in one vector:
   //
   //   (P, X) -> x
   //
   template <typename A>
   bool operator()(A const P_X[12 + 4], A x[2]) const {
     return operator()(P_X + 0, P_X + 12, x);
   }
 };

 // Test projective camera model projector.
 TEST(AutoDiff, ProjectiveCameraModel) {
   srand(5);
   double const tol = 1e-10;  // floating-point tolerance.
   double const del = 1e-4;   // finite-difference step.
   double const err = 1e-6;   // finite-difference tolerance.

   Projective b;

   // Make random P and X, in a single vector.
   double PX[12 + 4];
   for (int i = 0; i < 12 + 4; ++i) {
     PX[i] = RandDouble();
   }

   // Handy names for the P and X parts.
   double *P = PX + 0;
   double *X = PX + 12;

   // Apply the mapping, to get image point b_x.
   double b_x[2];
   b(P, X, b_x);

   // Use finite differencing to estimate the Jacobian.
   double fd_x[2];
   double fd_J[2 * (12 + 4)];
   ASSERT_TRUE((SymmetricDiff<Projective, double, 2, 12 + 4>(b, PX, del,
                                                             fd_x, fd_J)));

   for (int i = 0; i < 2; ++i) {
     ASSERT_EQ(fd_x[i], b_x[i]);
   }

   // Use automatic differentiation to compute the Jacobian.
   double ad_x1[2];
   double J_PX[2 * (12 + 4)];
   {
     double *parameters[] = { PX };
     double *jacobians[] = { J_PX };
     ASSERT_TRUE((AutoDiff<Projective, double, 12 + 4>::Differentiate(
         b, parameters, 2, ad_x1, jacobians)));

     for (int i = 0; i < 2; ++i) {
       ASSERT_NEAR(ad_x1[i], b_x[i], tol);
     }
   }

   // Use automatic differentiation (again), with two arguments.
   {
     double ad_x2[2];
     double J_P[2 * 12];
     double J_X[2 * 4];
     double *parameters[] = { P, X };
     double *jacobians[] = { J_P, J_X };
     ASSERT_TRUE((AutoDiff<Projective, double, 12, 4>::Differentiate(
         b, parameters, 2, ad_x2, jacobians)));

     for (int i = 0; i < 2; ++i) {
       ASSERT_NEAR(ad_x2[i], b_x[i], tol);
     }

     // Now compare the jacobians we got.
     for (int i = 0; i < 2; ++i) {
       for (int j = 0; j < 12 + 4; ++j) {
         ASSERT_NEAR(J_PX[(12 + 4) * i + j], fd_J[(12 + 4) * i + j], err);
       }

       for (int j = 0; j < 12; ++j) {
         ASSERT_NEAR(J_PX[(12 + 4) * i + j], J_P[12 * i + j], tol);
       }
       for (int j = 0; j < 4; ++j) {
         ASSERT_NEAR(J_PX[(12 + 4) * i + 12 + j], J_X[4 * i + j], tol);
       }
     }
   }
 }

 // Object to implement the projection by a calibrated camera.
 struct Metric {
   // The mapping is
   //
   //   q, c, X -> x = dehomg(R(q) (X - c))
   //
   // where q is a quaternion and c is the center of projection.
   //
   // This function takes three input vectors.
   template <typename A>
   bool operator()(A const q[4], A const c[3], A const X[3], A x[2]) const {
     A R[3 * 3];
     QuaternionToScaledRotation(q, R);

     // Convert the quaternion mapping all the way to projective matrix.
     A P[3 * 4];

     // Set P(:, 1:3) = R
     for (int i = 0; i < 3; ++i) {
       for (int j = 0; j < 3; ++j) {
         RowMajorAccess(P, 3, 4, i, j) = RowMajorAccess(R, 3, 3, i, j);
       }
     }

     // Set P(:, 4) = - R c
     for (int i = 0; i < 3; ++i) {
       RowMajorAccess(P, 3, 4, i, 3) =
         - (RowMajorAccess(R, 3, 3, i, 0) * c[0] +
            RowMajorAccess(R, 3, 3, i, 1) * c[1] +
            RowMajorAccess(R, 3, 3, i, 2) * c[2]);
     }

     A X1[4] = { X[0], X[1], X[2], A(1) };
     Projective p;
     return p(P, X1, x);
   }

   // A version that takes a single vector.
   template <typename A>
   bool operator()(A const q_c_X[4 + 3 + 3], A x[2]) const {
     return operator()(q_c_X, q_c_X + 4, q_c_X + 4 + 3, x);
   }
 };

 // This test is similar in structure to the previous one.
 TEST(AutoDiff, Metric) {
   srand(5);
   double const tol = 1e-10;  // floating-point tolerance.
   double const del = 1e-4;   // finite-difference step.
   double const err = 1e-5;   // finite-difference tolerance.

   Metric b;

   // Make random parameter vector.
   double qcX[4 + 3 + 3];
   for (int i = 0; i < 4 + 3 + 3; ++i)
     qcX[i] = RandDouble();

   // Handy names.
   double *q = qcX;
   double *c = qcX + 4;
   double *X = qcX + 4 + 3;

   // Compute projection, b_x.
   double b_x[2];
   ASSERT_TRUE(b(q, c, X, b_x));

   // Finite differencing estimate of Jacobian.
   double fd_x[2];
   double fd_J[2 * (4 + 3 + 3)];
   ASSERT_TRUE((SymmetricDiff<Metric, double, 2, 4 + 3 + 3>(b, qcX, del,
                                                            fd_x, fd_J)));

   for (int i = 0; i < 2; ++i) {
     ASSERT_NEAR(fd_x[i], b_x[i], tol);
   }

   // Automatic differentiation.
   double ad_x[2];
   double J_q[2 * 4];
   double J_c[2 * 3];
   double J_X[2 * 3];
   double *parameters[] = { q, c, X };
   double *jacobians[] = { J_q, J_c, J_X };
   ASSERT_TRUE((AutoDiff<Metric, double, 4, 3, 3>::Differentiate(
       b, parameters, 2, ad_x, jacobians)));

   for (int i = 0; i < 2; ++i) {
     ASSERT_NEAR(ad_x[i], b_x[i], tol);
   }

   // Compare the pieces.
   for (int i = 0; i < 2; ++i) {
     for (int j = 0; j < 4; ++j) {
       ASSERT_NEAR(J_q[4 * i + j], fd_J[(4 + 3 + 3) * i + j], err);
     }
     for (int j = 0; j < 3; ++j) {
       ASSERT_NEAR(J_c[3 * i + j], fd_J[(4 + 3 + 3) * i + j + 4], err);
     }
     for (int j = 0; j < 3; ++j) {
       ASSERT_NEAR(J_X[3 * i + j], fd_J[(4 + 3 + 3) * i + j + 4 + 3], err);
     }
   }
 }

 struct VaryingResidualFunctor {
   template <typename T>
   bool operator()(const T x[2], T* y) const {
     for (int i = 0; i < num_residuals; ++i) {
       y[i] = T(i) * x[0] * x[1] * x[1];
     }
     return true;
   }

   int num_residuals;
 };

 TEST(AutoDiff, VaryingNumberOfResidualsForOneCostFunctorType) {
   double x[2] = { 1.0, 5.5 };
   double *parameters[] = { x };
   const int kMaxResiduals = 10;
   double J_x[2 * kMaxResiduals];
   double residuals[kMaxResiduals];
   double *jacobians[] = { J_x };

   // Use a single functor, but tweak it to produce different numbers of
   // residuals.
   VaryingResidualFunctor functor;

   for (int num_residuals = 1; num_residuals < kMaxResiduals; ++num_residuals) {
     // Tweak the number of residuals to produce.
     functor.num_residuals = num_residuals;

     // Run autodiff with the new number of residuals.
     ASSERT_TRUE((AutoDiff<VaryingResidualFunctor, double, 2>::Differentiate(
         functor, parameters, num_residuals, residuals, jacobians)));

     const double kTolerance = 1e-14;
     for (int i = 0; i < num_residuals; ++i) {
       EXPECT_NEAR(J_x[2 * i + 0], i * x[1] * x[1], kTolerance) << "i: " << i;
       EXPECT_NEAR(J_x[2 * i + 1], 2 * i * x[0] * x[1], kTolerance)
           << "i: " << i;
     }
   }
 }

 struct Residual1Param {
   template <typename T>
   bool operator()(const T* x0, T* y) const {
     y[0] = *x0;
     return true;
   }
 };

 struct Residual2Param {
   template <typename T>
   bool operator()(const T* x0, const T* x1, T* y) const {
     y[0] = *x0 + pow(*x1, 2);
     return true;
   }
 };

 struct Residual3Param {
   template <typename T>
   bool operator()(const T* x0, const T* x1, const T* x2, T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3);
     return true;
   }
 };

 struct Residual4Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4);
     return true;
   }
 };

 struct Residual5Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   const T* x4,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5);
     return true;
   }
 };

 struct Residual6Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   const T* x4,
                   const T* x5,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
         pow(*x5, 6);
     return true;
   }
 };

 struct Residual7Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   const T* x4,
                   const T* x5,
                   const T* x6,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
         pow(*x5, 6) + pow(*x6, 7);
     return true;
   }
 };

 struct Residual8Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   const T* x4,
                   const T* x5,
                   const T* x6,
                   const T* x7,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
         pow(*x5, 6) + pow(*x6, 7) + pow(*x7, 8);
     return true;
   }
 };

 struct Residual9Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   const T* x4,
                   const T* x5,
                   const T* x6,
                   const T* x7,
                   const T* x8,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
         pow(*x5, 6) + pow(*x6, 7) + pow(*x7, 8) + pow(*x8, 9);
     return true;
   }
 };

 struct Residual10Param {
   template <typename T>
   bool operator()(const T* x0,
                   const T* x1,
                   const T* x2,
                   const T* x3,
                   const T* x4,
                   const T* x5,
                   const T* x6,
                   const T* x7,
                   const T* x8,
                   const T* x9,
                   T* y) const {
     y[0] = *x0 + pow(*x1, 2) + pow(*x2, 3) + pow(*x3, 4) + pow(*x4, 5) +
         pow(*x5, 6) + pow(*x6, 7) + pow(*x7, 8) + pow(*x8, 9) + pow(*x9, 10);
     return true;
   }
 };

 TEST(AutoDiff, VariadicAutoDiff) {
   double x[10];
   double residual = 0;
   double* parameters[10];
   double jacobian_values[10];
   double* jacobians[10];

   for (int i = 0; i < 10; ++i) {
     x[i] = 2.0;
     parameters[i] = x + i;
     jacobians[i] = jacobian_values + i;
   }

   {
     Residual1Param functor;
     int num_variables = 1;
     EXPECT_TRUE((AutoDiff<Residual1Param, double, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual2Param functor;
     int num_variables = 2;
     EXPECT_TRUE((AutoDiff<Residual2Param, double, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual3Param functor;
     int num_variables = 3;
     EXPECT_TRUE((AutoDiff<Residual3Param, double, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual4Param functor;
     int num_variables = 4;
     EXPECT_TRUE((AutoDiff<Residual4Param, double, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual5Param functor;
     int num_variables = 5;
     EXPECT_TRUE((AutoDiff<Residual5Param, double, 1, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual6Param functor;
     int num_variables = 6;
     EXPECT_TRUE((AutoDiff<Residual6Param,
                  double,
                  1, 1, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual7Param functor;
     int num_variables = 7;
     EXPECT_TRUE((AutoDiff<Residual7Param,
                  double,
                  1, 1, 1, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual8Param functor;
     int num_variables = 8;
     EXPECT_TRUE((AutoDiff<
                  Residual8Param,
                  double, 1, 1, 1, 1, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual9Param functor;
     int num_variables = 9;
     EXPECT_TRUE((AutoDiff<
                  Residual9Param,
                  double,
                  1, 1, 1, 1, 1, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }

   {
     Residual10Param functor;
     int num_variables = 10;
     EXPECT_TRUE((AutoDiff<
                  Residual10Param,
                  double,
                  1, 1, 1, 1, 1, 1, 1, 1, 1, 1>::Differentiate(
                      functor, parameters, 1, &residual, jacobians)));
     EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
     for (int i = 0; i < num_variables; ++i) {
       EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
     }
   }
 }

 // This is fragile test that triggers the alignment bug on
 // i686-apple-darwin10-llvm-g++-4.2 (GCC) 4.2.1. It is quite possible,
 // that other combinations of operating system + compiler will
 // re-arrange the operations in this test.
 //
 // But this is the best (and only) way we know of to trigger this
 // problem for now. A more robust solution that guarantees the
 // alignment of Eigen types used for automatic differentiation would
 // be nice.
 TEST(AutoDiff, AlignedAllocationTest) {
   // This int is needed to allocate 16 bits on the stack, so that the
   // next allocation is not aligned by default.
   char y = 0;

   // This is needed to prevent the compiler from optimizing y out of
   // this function.
   y += 1;

   typedef Jet<double, 2> JetT;
   FixedArray<JetT, (256 * 7) / sizeof(JetT)> x(3);

   // Need this to makes sure that x does not get optimized out.
   x[0] = x[0] + JetT(1.0);
 }

 }  // namespace internal
 }  // namespace ceres
	// Ceres Solver - A fast non-linear least squares minimizer
	// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
	// http://code.google.com/p/ceres-solver/
	//
	// Redistribution and use in source and binary forms, with or without
	// modification, are permitted provided that the following conditions are met:
	//
	// * Redistributions of source code must retain the above copyright notice,
	// this list of conditions and the following disclaimer.
	// * Redistributions in binary form must reproduce the above copyright notice,
	// this list of conditions and the following disclaimer in the documentation
	// and/or other materials provided with the distribution.
	// * Neither the name of Google Inc. nor the names of its contributors may be
	// used to endorse or promote products derived from this software without
	// specific prior written permission.
	//
	// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
	// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
	// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
	// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
	// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
	// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
	// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
	// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
	// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
	// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
	// POSSIBILITY OF SUCH DAMAGE.
	//
	// Author: keir@google.com (Keir Mierle)

	#include "ceres/internal/autodiff.h"

	#include "gtest/gtest.h"
	#include "ceres/random.h"

	namespace ceres {
	namespace internal {

	template <typename T> inline
	T &RowMajorAccess(T *base, int rows, int cols, int i, int j) {
	return base[cols * i + j];
	}

	// Do (symmetric) finite differencing using the given function object 'b' of
	// type 'B' and scalar type 'T' with step size 'del'.
	//
	// The type B should have a signature
	//
	// bool operator()(T const , T ) const;
	//
	// which maps a vector of parameters to a vector of outputs.
	template <typename B, typename T, int M, int N> inline
	bool SymmetricDiff(const B& b,
	const T par[N],
	T del, // step size.
	T fun[M],
	T jac[M * N]) { // row-major.
	if (!b(par, fun)) {
	return false;
	}

	// Temporary parameter vector.
	T tmp_par[N];
	for (int j = 0; j < N; ++j) {
	tmp_par[j] = par[j];
	}

	// For each dimension, we do one forward step and one backward step in
	// parameter space, and store the output vector vectors in these vectors.
	T fwd_fun[M];
	T bwd_fun[M];

	for (int j = 0; j < N; ++j) {
	// Forward step.
	tmp_par[j] = par[j] + del;
	if (!b(tmp_par, fwd_fun)) {
	return false;
	}

	// Backward step.
	tmp_par[j] = par[j] - del;
	if (!b(tmp_par, bwd_fun)) {
	return false;
	}

	// Symmetric differencing:
	// f'(a) = (f(a + h) - f(a - h)) / (2 h)
	for (int i = 0; i < M; ++i) {
	RowMajorAccess(jac, M, N, i, j) =
	(fwd_fun[i] - bwd_fun[i]) / (T(2) * del);
	}

	// Restore our temporary vector.
	tmp_par[j] = par[j];
	}

	return true;
	}

	template <typename A> inline
	void QuaternionToScaledRotation(A const q[4], A R[3 * 3]) {
	// Make convenient names for elements of q.
	A a = q[0];
	A b = q[1];
	A c = q[2];
	A d = q[3];
	// This is not to eliminate common sub-expression, but to
	// make the lines shorter so that they fit in 80 columns!
	A aa = a*a;
	A ab = a*b;
	A ac = a*c;
	A ad = a*d;
	A bb = b*b;
	A bc = b*c;
	A bd = b*d;
	A cc = c*c;
	A cd = c*d;
	A dd = d*d;
	#define R(i, j) RowMajorAccess(R, 3, 3, (i), (j))
	R(0, 0) = aa+bb-cc-dd; R(0, 1) = A(2)(bc-ad); R(0, 2) = A(2)(ac+bd); // NOLINT
	R(1, 0) = A(2)(ad+bc); R(1, 1) = aa-bb+cc-dd; R(1, 2) = A(2)(cd-ab); // NOLINT
	R(2, 0) = A(2)(bd-ac); R(2, 1) = A(2)(ab+cd); R(2, 2) = aa-bb-cc+dd; // NOLINT
	#undef R
	}

	// A structure for projecting a 3x4 camera matrix and a
	// homogeneous 3D point, to a 2D inhomogeneous point.
	struct Projective {
	// Function that takes P and X as separate vectors:
	// P, X -> x
	template <typename A>
	bool operator()(A const P[12], A const X[4], A x[2]) const {
	A PX[3];
	for (int i = 0; i < 3; ++i) {
	PX[i] = RowMajorAccess(P, 3, 4, i, 0) * X[0] +
	RowMajorAccess(P, 3, 4, i, 1) * X[1] +
	RowMajorAccess(P, 3, 4, i, 2) * X[2] +
	RowMajorAccess(P, 3, 4, i, 3) * X[3];
	}
	if (PX[2] != 0.0) {
	x[0] = PX[0] / PX[2];
	x[1] = PX[1] / PX[2];
	return true;
	}
	return false;
	}

	// Version that takes P and X packed in one vector:
	//
	// (P, X) -> x
	//
	template <typename A>
	bool operator()(A const P_X[12 + 4], A x[2]) const {
	return operator()(P_X + 0, P_X + 12, x);
	}
	};

	// Test projective camera model projector.
	TEST(AutoDiff, ProjectiveCameraModel) {
	srand(5);
	double const tol = 1e-10; // floating-point tolerance.
	double const del = 1e-4; // finite-difference step.
	double const err = 1e-6; // finite-difference tolerance.

	Projective b;

	// Make random P and X, in a single vector.
	double PX[12 + 4];
	for (int i = 0; i < 12 + 4; ++i) {
	PX[i] = RandDouble();
	}

	// Handy names for the P and X parts.
	double *P = PX + 0;
	double *X = PX + 12;

	// Apply the mapping, to get image point b_x.
	double b_x[2];
	b(P, X, b_x);

	// Use finite differencing to estimate the Jacobian.
	double fd_x[2];
	double fd_J[2 * (12 + 4)];
	ASSERT_TRUE((SymmetricDiff<Projective, double, 2, 12 + 4>(b, PX, del,
	fd_x, fd_J)));

	for (int i = 0; i < 2; ++i) {
	ASSERT_EQ(fd_x[i], b_x[i]);
	}

	// Use automatic differentiation to compute the Jacobian.
	double ad_x1[2];
	double J_PX[2 * (12 + 4)];
	{
	double *parameters[] = { PX };
	double *jacobians[] = { J_PX };
	ASSERT_TRUE((AutoDiff<Projective, double, 12 + 4>::Differentiate(
	b, parameters, 2, ad_x1, jacobians)));

	for (int i = 0; i < 2; ++i) {
	ASSERT_NEAR(ad_x1[i], b_x[i], tol);
	}
	}

	// Use automatic differentiation (again), with two arguments.
	{
	double ad_x2[2];
	double J_P[2 * 12];
	double J_X[2 * 4];
	double *parameters[] = { P, X };
	double *jacobians[] = { J_P, J_X };
	ASSERT_TRUE((AutoDiff<Projective, double, 12, 4>::Differentiate(
	b, parameters, 2, ad_x2, jacobians)));

	for (int i = 0; i < 2; ++i) {
	ASSERT_NEAR(ad_x2[i], b_x[i], tol);
	}

	// Now compare the jacobians we got.
	for (int i = 0; i < 2; ++i) {
	for (int j = 0; j < 12 + 4; ++j) {
	ASSERT_NEAR(J_PX[(12 + 4) * i + j], fd_J[(12 + 4) * i + j], err);
	}

	for (int j = 0; j < 12; ++j) {
	ASSERT_NEAR(J_PX[(12 + 4) * i + j], J_P[12 * i + j], tol);
	}
	for (int j = 0; j < 4; ++j) {
	ASSERT_NEAR(J_PX[(12 + 4) * i + 12 + j], J_X[4 * i + j], tol);
	}
	}
	}
	}

	// Object to implement the projection by a calibrated camera.
	struct Metric {
	// The mapping is
	//
	// q, c, X -> x = dehomg(R(q) (X - c))
	//
	// where q is a quaternion and c is the center of projection.
	//
	// This function takes three input vectors.
	template <typename A>
	bool operator()(A const q[4], A const c[3], A const X[3], A x[2]) const {
	A R[3 * 3];
	QuaternionToScaledRotation(q, R);

	// Convert the quaternion mapping all the way to projective matrix.
	A P[3 * 4];

	// Set P(:, 1:3) = R
	for (int i = 0; i < 3; ++i) {
	for (int j = 0; j < 3; ++j) {
	RowMajorAccess(P, 3, 4, i, j) = RowMajorAccess(R, 3, 3, i, j);
	}
	}

	// Set P(:, 4) = - R c
	for (int i = 0; i < 3; ++i) {
	RowMajorAccess(P, 3, 4, i, 3) =
	- (RowMajorAccess(R, 3, 3, i, 0) * c[0] +
	RowMajorAccess(R, 3, 3, i, 1) * c[1] +
	RowMajorAccess(R, 3, 3, i, 2) * c[2]);
	}

	A X1[4] = { X[0], X[1], X[2], A(1) };
	Projective p;
	return p(P, X1, x);
	}

	// A version that takes a single vector.
	template <typename A>
	bool operator()(A const q_c_X[4 + 3 + 3], A x[2]) const {
	return operator()(q_c_X, q_c_X + 4, q_c_X + 4 + 3, x);
	}
	};

	// This test is similar in structure to the previous one.
	TEST(AutoDiff, Metric) {
	srand(5);
	double const tol = 1e-10; // floating-point tolerance.
	double const del = 1e-4; // finite-difference step.
	double const err = 1e-5; // finite-difference tolerance.

	Metric b;

	// Make random parameter vector.
	double qcX[4 + 3 + 3];
	for (int i = 0; i < 4 + 3 + 3; ++i)
	qcX[i] = RandDouble();

	// Handy names.
	double *q = qcX;
	double *c = qcX + 4;
	double *X = qcX + 4 + 3;

	// Compute projection, b_x.
	double b_x[2];
	ASSERT_TRUE(b(q, c, X, b_x));

	// Finite differencing estimate of Jacobian.
	double fd_x[2];
	double fd_J[2 * (4 + 3 + 3)];
	ASSERT_TRUE((SymmetricDiff<Metric, double, 2, 4 + 3 + 3>(b, qcX, del,
	fd_x, fd_J)));

	for (int i = 0; i < 2; ++i) {
	ASSERT_NEAR(fd_x[i], b_x[i], tol);
	}

	// Automatic differentiation.
	double ad_x[2];
	double J_q[2 * 4];
	double J_c[2 * 3];
	double J_X[2 * 3];
	double *parameters[] = { q, c, X };
	double *jacobians[] = { J_q, J_c, J_X };
	ASSERT_TRUE((AutoDiff<Metric, double, 4, 3, 3>::Differentiate(
	b, parameters, 2, ad_x, jacobians)));

	for (int i = 0; i < 2; ++i) {
	ASSERT_NEAR(ad_x[i], b_x[i], tol);
	}

	// Compare the pieces.
	for (int i = 0; i < 2; ++i) {
	for (int j = 0; j < 4; ++j) {
	ASSERT_NEAR(J_q[4 * i + j], fd_J[(4 + 3 + 3) * i + j], err);
	}
	for (int j = 0; j < 3; ++j) {
	ASSERT_NEAR(J_c[3 * i + j], fd_J[(4 + 3 + 3) * i + j + 4], err);
	}
	for (int j = 0; j < 3; ++j) {
	ASSERT_NEAR(J_X[3 * i + j], fd_J[(4 + 3 + 3) * i + j + 4 + 3], err);
	}
	}
	}

	struct VaryingResidualFunctor {
	template <typename T>
	bool operator()(const T x[2], T* y) const {
	for (int i = 0; i < num_residuals; ++i) {
	y[i] = T(i) * x[0] * x[1] * x[1];
	}
	return true;
	}

	int num_residuals;
	};

	TEST(AutoDiff, VaryingNumberOfResidualsForOneCostFunctorType) {
	double x[2] = { 1.0, 5.5 };
	double *parameters[] = { x };
	const int kMaxResiduals = 10;
	double J_x[2 * kMaxResiduals];
	double residuals[kMaxResiduals];
	double *jacobians[] = { J_x };

	// Use a single functor, but tweak it to produce different numbers of
	// residuals.
	VaryingResidualFunctor functor;

	for (int num_residuals = 1; num_residuals < kMaxResiduals; ++num_residuals) {
	// Tweak the number of residuals to produce.
	functor.num_residuals = num_residuals;

	// Run autodiff with the new number of residuals.
	ASSERT_TRUE((AutoDiff<VaryingResidualFunctor, double, 2>::Differentiate(
	functor, parameters, num_residuals, residuals, jacobians)));

	const double kTolerance = 1e-14;
	for (int i = 0; i < num_residuals; ++i) {
	EXPECT_NEAR(J_x[2 * i + 0], i * x[1] * x[1], kTolerance) << "i: " << i;
	EXPECT_NEAR(J_x[2 * i + 1], 2 * i * x[0] * x[1], kTolerance)
	<< "i: " << i;
	}
	}
	}

	struct Residual1Param {
	template <typename T>
	bool operator()(const T* x0, T* y) const {
	y[0] = *x0;
	return true;
	}
	};

	struct Residual2Param {
	template <typename T>
	bool operator()(const T* x0, const T* x1, T* y) const {
	y[0] = x0 + pow(x1, 2);
	return true;
	}
	};

	struct Residual3Param {
	template <typename T>
	bool operator()(const T* x0, const T* x1, const T* x2, T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(*x2, 3);
	return true;
	}
	};

	struct Residual4Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4);
	return true;
	}
	};

	struct Residual5Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	const T* x4,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4) + pow(*x4, 5);
	return true;
	}
	};

	struct Residual6Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	const T* x4,
	const T* x5,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4) + pow(*x4, 5) +
	pow(*x5, 6);
	return true;
	}
	};

	struct Residual7Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	const T* x4,
	const T* x5,
	const T* x6,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4) + pow(*x4, 5) +
	pow(x5, 6) + pow(x6, 7);
	return true;
	}
	};

	struct Residual8Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	const T* x4,
	const T* x5,
	const T* x6,
	const T* x7,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4) + pow(*x4, 5) +
	pow(x5, 6) + pow(x6, 7) + pow(*x7, 8);
	return true;
	}
	};

	struct Residual9Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	const T* x4,
	const T* x5,
	const T* x6,
	const T* x7,
	const T* x8,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4) + pow(*x4, 5) +
	pow(x5, 6) + pow(x6, 7) + pow(x7, 8) + pow(x8, 9);
	return true;
	}
	};

	struct Residual10Param {
	template <typename T>
	bool operator()(const T* x0,
	const T* x1,
	const T* x2,
	const T* x3,
	const T* x4,
	const T* x5,
	const T* x6,
	const T* x7,
	const T* x8,
	const T* x9,
	T* y) const {
	y[0] = x0 + pow(x1, 2) + pow(x2, 3) + pow(x3, 4) + pow(*x4, 5) +
	pow(x5, 6) + pow(x6, 7) + pow(x7, 8) + pow(x8, 9) + pow(*x9, 10);
	return true;
	}
	};

	TEST(AutoDiff, VariadicAutoDiff) {
	double x[10];
	double residual = 0;
	double* parameters[10];
	double jacobian_values[10];
	double* jacobians[10];

	for (int i = 0; i < 10; ++i) {
	x[i] = 2.0;
	parameters[i] = x + i;
	jacobians[i] = jacobian_values + i;
	}

	{
	Residual1Param functor;
	int num_variables = 1;
	EXPECT_TRUE((AutoDiff<Residual1Param, double, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual2Param functor;
	int num_variables = 2;
	EXPECT_TRUE((AutoDiff<Residual2Param, double, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual3Param functor;
	int num_variables = 3;
	EXPECT_TRUE((AutoDiff<Residual3Param, double, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual4Param functor;
	int num_variables = 4;
	EXPECT_TRUE((AutoDiff<Residual4Param, double, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual5Param functor;
	int num_variables = 5;
	EXPECT_TRUE((AutoDiff<Residual5Param, double, 1, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual6Param functor;
	int num_variables = 6;
	EXPECT_TRUE((AutoDiff<Residual6Param,
	double,
	1, 1, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual7Param functor;
	int num_variables = 7;
	EXPECT_TRUE((AutoDiff<Residual7Param,
	double,
	1, 1, 1, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual8Param functor;
	int num_variables = 8;
	EXPECT_TRUE((AutoDiff<
	Residual8Param,
	double, 1, 1, 1, 1, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual9Param functor;
	int num_variables = 9;
	EXPECT_TRUE((AutoDiff<
	Residual9Param,
	double,
	1, 1, 1, 1, 1, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}

	{
	Residual10Param functor;
	int num_variables = 10;
	EXPECT_TRUE((AutoDiff<
	Residual10Param,
	double,
	1, 1, 1, 1, 1, 1, 1, 1, 1, 1>::Differentiate(
	functor, parameters, 1, &residual, jacobians)));
	EXPECT_EQ(residual, pow(2, num_variables + 1) - 2);
	for (int i = 0; i < num_variables; ++i) {
	EXPECT_EQ(jacobian_values[i], (i + 1) * pow(2, i));
	}
	}
	}

	// This is fragile test that triggers the alignment bug on
	// i686-apple-darwin10-llvm-g++-4.2 (GCC) 4.2.1. It is quite possible,
	// that other combinations of operating system + compiler will
	// re-arrange the operations in this test.
	//
	// But this is the best (and only) way we know of to trigger this
	// problem for now. A more robust solution that guarantees the
	// alignment of Eigen types used for automatic differentiation would
	// be nice.
	TEST(AutoDiff, AlignedAllocationTest) {
	// This int is needed to allocate 16 bits on the stack, so that the
	// next allocation is not aligned by default.
	char y = 0;

	// This is needed to prevent the compiler from optimizing y out of
	// this function.
	y += 1;

	typedef Jet<double, 2> JetT;
	FixedArray<JetT, (256 * 7) / sizeof(JetT)> x(3);

	// Need this to makes sure that x does not get optimized out.
	x[0] = x[0] + JetT(1.0);
	}

	} // namespace internal
	} // namespace ceres