internal/ceres/runtime_numeric_diff_cost_function_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)
 //
 // Based on the tests in numeric_diff_cost_function.cc.
 //
 // TODO(keir): See about code duplication.

 #include "ceres/runtime_numeric_diff_cost_function.h"

 #include <algorithm>
 #include <cmath>
 #include <string>
 #include <vector>
 #include "ceres/cost_function.h"
 #include "ceres/internal/macros.h"
 #include "ceres/internal/scoped_ptr.h"
 #include "ceres/stringprintf.h"
 #include "ceres/test_util.h"
 #include "glog/logging.h"
 #include "gtest/gtest.h"

 namespace ceres {
 namespace internal {

 const double kRelativeEps = 1e-6;

 // y1 = x1'x2      -> dy1/dx1 = x2,               dy1/dx2 = x1
 // y2 = (x1'x2)^2  -> dy2/dx1 = 2 * x2 * (x1'x2), dy2/dx2 = 2 * x1 * (x1'x2)
 // y3 = x2'x2      -> dy3/dx1 = 0,                dy3/dx2 = 2 * x2
 class TestCostFunction : public CostFunction {
  public:
   TestCostFunction() {
     set_num_residuals(3);
     mutable_parameter_block_sizes()->push_back(5);  // x1.
     mutable_parameter_block_sizes()->push_back(5);  // x2.
   }
   virtual bool Evaluate(double const* const* parameters,
                         double* residuals,
                         double** jacobians) const {
     (void) jacobians;  // Ignored.

     residuals[0] = residuals[1] = residuals[2] = 0;
     for (int i = 0; i < 5; ++i) {
       residuals[0] += parameters[0][i] * parameters[1][i];
       residuals[2] += parameters[1][i] * parameters[1][i];
     }
     residuals[1] = residuals[0] * residuals[0];
     return true;
   }
 };

 TEST(NumericDiffCostFunction, EasyCase) {
   // Try both central and forward difference.
   TestCostFunction term;
   scoped_ptr<CostFunction> cfs[2];
   cfs[0].reset(
       CreateRuntimeNumericDiffCostFunction(&term, CENTRAL, kRelativeEps));

   cfs[1].reset(
       CreateRuntimeNumericDiffCostFunction(&term, FORWARD, kRelativeEps));


   for (int c = 0; c < 2; ++c) {
     CostFunction *cost_function = cfs[c].get();

     double x1[] = { 1.0, 2.0, 3.0, 4.0, 5.0 };
     double x2[] = { 9.0, 9.0, 5.0, 5.0, 1.0 };
     double *parameters[] = { &x1[0], &x2[0] };

     double dydx1[15];  // 3 x 5, row major.
     double dydx2[15];  // 3 x 5, row major.
     double *jacobians[2] = { &dydx1[0], &dydx2[0] };

     double residuals[3] = {-1e-100, -2e-100, -3e-100 };

     ASSERT_TRUE(cost_function->Evaluate(&parameters[0],
                                         &residuals[0],
                                         &jacobians[0]));

     EXPECT_EQ(residuals[0], 67);
     EXPECT_EQ(residuals[1], 4489);
     EXPECT_EQ(residuals[2], 213);

     for (int i = 0; i < 5; ++i) {
       LOG(INFO) << "c = " << c << " i = " << i;
       const double kEps = c == 0 ? /* central */ 3e-9 : /* forward */ 2e-5;

       ExpectClose(x2[i],                    dydx1[5 * 0 + i], kEps);  // y1
       ExpectClose(x1[i],                    dydx2[5 * 0 + i], kEps);
       ExpectClose(2 * x2[i] * residuals[0], dydx1[5 * 1 + i], kEps);  // y2
       ExpectClose(2 * x1[i] * residuals[0], dydx2[5 * 1 + i], kEps);
       ExpectClose(0.0,                      dydx1[5 * 2 + i], kEps);  // y3
       ExpectClose(2 * x2[i],                dydx2[5 * 2 + i], kEps);
     }
   }
 }

 // y1 = sin(x1'x2)
 // y2 = exp(-x1'x2 / 10)
 //
 // dy1/dx1 =  x2 * cos(x1'x2),            dy1/dx2 =  x1 * cos(x1'x2)
 // dy2/dx1 = -x2 * exp(-x1'x2 / 10) / 10, dy2/dx2 = -x2 * exp(-x1'x2 / 10) / 10
 class TranscendentalTestCostFunction : public CostFunction {
  public:
   TranscendentalTestCostFunction() {
     set_num_residuals(2);
     mutable_parameter_block_sizes()->push_back(5);  // x1.
     mutable_parameter_block_sizes()->push_back(5);  // x2.
   }
   virtual bool Evaluate(double const* const* parameters,
                         double* residuals,
                         double** jacobians) const {
     (void) jacobians;  // Ignored.

     double x1x2 = 0;
     for (int i = 0; i < 5; ++i) {
       x1x2 += parameters[0][i] * parameters[1][i];
     }
     residuals[0] = sin(x1x2);
     residuals[1] = exp(-x1x2 / 10);
     return true;
   }
 };

 TEST(NumericDiffCostFunction, TransendentalOperationsInCostFunction) {
   // Try both central and forward difference.
   TranscendentalTestCostFunction term;
   scoped_ptr<CostFunction> cfs[2];
   cfs[0].reset(
       CreateRuntimeNumericDiffCostFunction(&term, CENTRAL, kRelativeEps));

   cfs[1].reset(
       CreateRuntimeNumericDiffCostFunction(&term, FORWARD, kRelativeEps));

   for (int c = 0; c < 2; ++c) {
     CostFunction *cost_function = cfs[c].get();

     struct {
       double x1[5];
       double x2[5];
     } kTests[] = {
       { { 1.0, 2.0, 3.0, 4.0, 5.0 },  // No zeros.
         { 9.0, 9.0, 5.0, 5.0, 1.0 },
       },
       { { 0.0, 2.0, 3.0, 0.0, 5.0 },  // Some zeros x1.
         { 9.0, 9.0, 5.0, 5.0, 1.0 },
       },
       { { 1.0, 2.0, 3.0, 1.0, 5.0 },  // Some zeros x2.
         { 0.0, 9.0, 0.0, 5.0, 0.0 },
       },
       { { 0.0, 0.0, 0.0, 0.0, 0.0 },  // All zeros x1.
         { 9.0, 9.0, 5.0, 5.0, 1.0 },
       },
       { { 1.0, 2.0, 3.0, 4.0, 5.0 },  // All zeros x2.
         { 0.0, 0.0, 0.0, 0.0, 0.0 },
       },
       { { 0.0, 0.0, 0.0, 0.0, 0.0 },  // All zeros.
         { 0.0, 0.0, 0.0, 0.0, 0.0 },
       },
     };
     for (int k = 0; k < CERES_ARRAYSIZE(kTests); ++k) {
       double *x1 = &(kTests[k].x1[0]);
       double *x2 = &(kTests[k].x2[0]);
       double *parameters[] = { x1, x2 };

       double dydx1[10];
       double dydx2[10];
       double *jacobians[2] = { &dydx1[0], &dydx2[0] };

       double residuals[2];

       ASSERT_TRUE(cost_function->Evaluate(&parameters[0],
                                           &residuals[0],
                                           &jacobians[0]));
       LOG(INFO) << "Ran evaluate for test k=" << k << " c=" << c;

       double x1x2 = 0;
       for (int i = 0; i < 5; ++i) {
         x1x2 += x1[i] * x2[i];
       }

       for (int i = 0; i < 5; ++i) {
         const double kEps = (c == 0 ? /* central */ 3e-9 : /* forward */ 2e-5);

         ExpectClose( x2[i] * cos(x1x2),              dydx1[5 * 0 + i], kEps);  // NOLINT
         ExpectClose( x1[i] * cos(x1x2),              dydx2[5 * 0 + i], kEps);  // NOLINT
         ExpectClose(-x2[i] * exp(-x1x2 / 10.) / 10., dydx1[5 * 1 + i], kEps);
         ExpectClose(-x1[i] * exp(-x1x2 / 10.) / 10., dydx2[5 * 1 + i], kEps);
       }
     }
   }
 }

 }  // 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)
	//
	// Based on the tests in numeric_diff_cost_function.cc.
	//
	// TODO(keir): See about code duplication.

	#include "ceres/runtime_numeric_diff_cost_function.h"

	#include <algorithm>
	#include <cmath>
	#include <string>
	#include <vector>
	#include "ceres/cost_function.h"
	#include "ceres/internal/macros.h"
	#include "ceres/internal/scoped_ptr.h"
	#include "ceres/stringprintf.h"
	#include "ceres/test_util.h"
	#include "glog/logging.h"
	#include "gtest/gtest.h"

	namespace ceres {
	namespace internal {

	const double kRelativeEps = 1e-6;

	// y1 = x1'x2 -> dy1/dx1 = x2, dy1/dx2 = x1
	// y2 = (x1'x2)^2 -> dy2/dx1 = 2 * x2 * (x1'x2), dy2/dx2 = 2 * x1 * (x1'x2)
	// y3 = x2'x2 -> dy3/dx1 = 0, dy3/dx2 = 2 * x2
	class TestCostFunction : public CostFunction {
	public:
	TestCostFunction() {
	set_num_residuals(3);
	mutable_parameter_block_sizes()->push_back(5); // x1.
	mutable_parameter_block_sizes()->push_back(5); // x2.
	}
	virtual bool Evaluate(double const* const* parameters,
	double* residuals,
	double** jacobians) const {
	(void) jacobians; // Ignored.

	residuals[0] = residuals[1] = residuals[2] = 0;
	for (int i = 0; i < 5; ++i) {
	residuals[0] += parameters[0][i] * parameters[1][i];
	residuals[2] += parameters[1][i] * parameters[1][i];
	}
	residuals[1] = residuals[0] * residuals[0];
	return true;
	}
	};

	TEST(NumericDiffCostFunction, EasyCase) {
	// Try both central and forward difference.
	TestCostFunction term;
	scoped_ptr<CostFunction> cfs[2];
	cfs[0].reset(
	CreateRuntimeNumericDiffCostFunction(&term, CENTRAL, kRelativeEps));

	cfs[1].reset(
	CreateRuntimeNumericDiffCostFunction(&term, FORWARD, kRelativeEps));


	for (int c = 0; c < 2; ++c) {
	CostFunction *cost_function = cfs[c].get();

	double x1[] = { 1.0, 2.0, 3.0, 4.0, 5.0 };
	double x2[] = { 9.0, 9.0, 5.0, 5.0, 1.0 };
	double *parameters[] = { &x1[0], &x2[0] };

	double dydx1[15]; // 3 x 5, row major.
	double dydx2[15]; // 3 x 5, row major.
	double *jacobians[2] = { &dydx1[0], &dydx2[0] };

	double residuals[3] = {-1e-100, -2e-100, -3e-100 };

	ASSERT_TRUE(cost_function->Evaluate(&parameters[0],
	&residuals[0],
	&jacobians[0]));

	EXPECT_EQ(residuals[0], 67);
	EXPECT_EQ(residuals[1], 4489);
	EXPECT_EQ(residuals[2], 213);

	for (int i = 0; i < 5; ++i) {
	LOG(INFO) << "c = " << c << " i = " << i;
	const double kEps = c == 0 ? /* central / 3e-9 : / forward */ 2e-5;

	ExpectClose(x2[i], dydx1[5 * 0 + i], kEps); // y1
	ExpectClose(x1[i], dydx2[5 * 0 + i], kEps);
	ExpectClose(2 * x2[i] * residuals[0], dydx1[5 * 1 + i], kEps); // y2
	ExpectClose(2 * x1[i] * residuals[0], dydx2[5 * 1 + i], kEps);
	ExpectClose(0.0, dydx1[5 * 2 + i], kEps); // y3
	ExpectClose(2 * x2[i], dydx2[5 * 2 + i], kEps);
	}
	}
	}

	// y1 = sin(x1'x2)
	// y2 = exp(-x1'x2 / 10)
	//
	// dy1/dx1 = x2 * cos(x1'x2), dy1/dx2 = x1 * cos(x1'x2)
	// dy2/dx1 = -x2 * exp(-x1'x2 / 10) / 10, dy2/dx2 = -x2 * exp(-x1'x2 / 10) / 10
	class TranscendentalTestCostFunction : public CostFunction {
	public:
	TranscendentalTestCostFunction() {
	set_num_residuals(2);
	mutable_parameter_block_sizes()->push_back(5); // x1.
	mutable_parameter_block_sizes()->push_back(5); // x2.
	}
	virtual bool Evaluate(double const* const* parameters,
	double* residuals,
	double** jacobians) const {
	(void) jacobians; // Ignored.

	double x1x2 = 0;
	for (int i = 0; i < 5; ++i) {
	x1x2 += parameters[0][i] * parameters[1][i];
	}
	residuals[0] = sin(x1x2);
	residuals[1] = exp(-x1x2 / 10);
	return true;
	}
	};

	TEST(NumericDiffCostFunction, TransendentalOperationsInCostFunction) {
	// Try both central and forward difference.
	TranscendentalTestCostFunction term;
	scoped_ptr<CostFunction> cfs[2];
	cfs[0].reset(
	CreateRuntimeNumericDiffCostFunction(&term, CENTRAL, kRelativeEps));

	cfs[1].reset(
	CreateRuntimeNumericDiffCostFunction(&term, FORWARD, kRelativeEps));

	for (int c = 0; c < 2; ++c) {
	CostFunction *cost_function = cfs[c].get();

	struct {
	double x1[5];
	double x2[5];
	} kTests[] = {
	{ { 1.0, 2.0, 3.0, 4.0, 5.0 }, // No zeros.
	{ 9.0, 9.0, 5.0, 5.0, 1.0 },
	},
	{ { 0.0, 2.0, 3.0, 0.0, 5.0 }, // Some zeros x1.
	{ 9.0, 9.0, 5.0, 5.0, 1.0 },
	},
	{ { 1.0, 2.0, 3.0, 1.0, 5.0 }, // Some zeros x2.
	{ 0.0, 9.0, 0.0, 5.0, 0.0 },
	},
	{ { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros x1.
	{ 9.0, 9.0, 5.0, 5.0, 1.0 },
	},
	{ { 1.0, 2.0, 3.0, 4.0, 5.0 }, // All zeros x2.
	{ 0.0, 0.0, 0.0, 0.0, 0.0 },
	},
	{ { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros.
	{ 0.0, 0.0, 0.0, 0.0, 0.0 },
	},
	};
	for (int k = 0; k < CERES_ARRAYSIZE(kTests); ++k) {
	double *x1 = &(kTests[k].x1[0]);
	double *x2 = &(kTests[k].x2[0]);
	double *parameters[] = { x1, x2 };

	double dydx1[10];
	double dydx2[10];
	double *jacobians[2] = { &dydx1[0], &dydx2[0] };

	double residuals[2];

	ASSERT_TRUE(cost_function->Evaluate(&parameters[0],
	&residuals[0],
	&jacobians[0]));
	LOG(INFO) << "Ran evaluate for test k=" << k << " c=" << c;

	double x1x2 = 0;
	for (int i = 0; i < 5; ++i) {
	x1x2 += x1[i] * x2[i];
	}

	for (int i = 0; i < 5; ++i) {
	const double kEps = (c == 0 ? /* central / 3e-9 : / forward */ 2e-5);

	ExpectClose( x2[i] * cos(x1x2), dydx1[5 * 0 + i], kEps); // NOLINT
	ExpectClose( x1[i] * cos(x1x2), dydx2[5 * 0 + i], kEps); // NOLINT
	ExpectClose(-x2[i] * exp(-x1x2 / 10.) / 10., dydx1[5 * 1 + i], kEps);
	ExpectClose(-x1[i] * exp(-x1x2 / 10.) / 10., dydx2[5 * 1 + i], kEps);
	}
	}
	}
	}

	} // namespace internal
	} // namespace ceres