mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-30 09:00:37 +08:00
Sameer Agarwal
2fc0ed6143
The interface for NumericDiffCostFunction and AutoDiffCostFunction are not comparable. They both accept variadic functors. The change is backward compatible, as it still supports numeric differentiation of CostFunction objects. Some refactoring of documentation and code in auto_diff_cost_function and its relatives was also done to make things consistent. Change-Id: Ib5f230a1d4a85738eb187803b9c1cd7166bb3b92
304 lines
11 KiB
C++
304 lines
11 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2010, 2011, 2012 Google Inc. All rights reserved.
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// http://code.google.com/p/ceres-solver/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: keir@google.com (Keir Mierle)
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#include "ceres/numeric_diff_cost_function.h"
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#include <algorithm>
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#include <cmath>
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#include <string>
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#include <vector>
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#include "ceres/internal/macros.h"
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#include "ceres/internal/scoped_ptr.h"
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#include "ceres/sized_cost_function.h"
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#include "ceres/stringprintf.h"
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#include "ceres/test_util.h"
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#include "ceres/types.h"
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#include "glog/logging.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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// y1 = x1'x2 -> dy1/dx1 = x2, dy1/dx2 = x1
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// y2 = (x1'x2)^2 -> dy2/dx1 = 2 * x2 * (x1'x2), dy2/dx2 = 2 * x1 * (x1'x2)
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// y3 = x2'x2 -> dy3/dx1 = 0, dy3/dx2 = 2 * x2
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struct EasyFunctor {
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bool operator()(const double* x1, const double* x2, double* residuals) const {
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residuals[0] = residuals[1] = residuals[2] = 0;
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for (int i = 0; i < 5; ++i) {
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residuals[0] += x1[i] * x2[i];
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residuals[2] += x2[i] * x2[i];
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}
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residuals[1] = residuals[0] * residuals[0];
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return true;
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}
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};
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class EasyCostFunction : public SizedCostFunction<3, 5, 5> {
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public:
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virtual bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const {
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(void) jacobians; // Ignored.
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return EasyFunctor()(parameters[0], parameters[1], residuals);
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}
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};
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TEST(NumericDiffCostFunction, EasyCase) {
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// Try both central and forward difference.
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internal::scoped_ptr<CostFunction> cfs[4];
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cfs[0].reset(
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new NumericDiffCostFunction<EasyCostFunction,
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CENTRAL,
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3, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new EasyCostFunction, TAKE_OWNERSHIP));
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cfs[1].reset(
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new NumericDiffCostFunction<EasyCostFunction,
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FORWARD,
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3, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new EasyCostFunction, TAKE_OWNERSHIP));
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cfs[2].reset(
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new NumericDiffCostFunction< EasyFunctor,
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CENTRAL,
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3, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new EasyFunctor));
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cfs[3].reset(
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new NumericDiffCostFunction< EasyFunctor,
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FORWARD,
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3, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new EasyFunctor));
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for (int c = 0; c < 4; ++c) {
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CostFunction *cost_function = cfs[c].get();
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double x1[] = { 1.0, 2.0, 3.0, 4.0, 5.0 };
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double x2[] = { 9.0, 9.0, 5.0, 5.0, 1.0 };
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double *parameters[] = { &x1[0], &x2[0] };
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double dydx1[15]; // 3 x 5, row major.
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double dydx2[15]; // 3 x 5, row major.
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double *jacobians[2] = { &dydx1[0], &dydx2[0] };
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double residuals[3] = {-1e-100, -2e-100, -3e-100 };
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ASSERT_TRUE(cost_function->Evaluate(¶meters[0],
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&residuals[0],
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&jacobians[0]));
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EXPECT_EQ(residuals[0], 67);
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EXPECT_EQ(residuals[1], 4489);
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EXPECT_EQ(residuals[2], 213);
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for (int i = 0; i < 5; ++i) {
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LOG(INFO) << "c = " << c << " i = " << i;
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const double kEps = c == 0 ? /* central */ 3e-9 : /* forward */ 2e-5;
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ExpectClose(x2[i], dydx1[5 * 0 + i], kEps); // y1
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ExpectClose(x1[i], dydx2[5 * 0 + i], kEps);
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ExpectClose(2 * x2[i] * residuals[0], dydx1[5 * 1 + i], kEps); // y2
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ExpectClose(2 * x1[i] * residuals[0], dydx2[5 * 1 + i], kEps);
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ExpectClose(0.0, dydx1[5 * 2 + i], kEps); // y3
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ExpectClose(2 * x2[i], dydx2[5 * 2 + i], kEps);
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}
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}
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}
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// y1 = sin(x1'x2)
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// y2 = exp(-x1'x2 / 10)
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//
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// dy1/dx1 = x2 * cos(x1'x2), dy1/dx2 = x1 * cos(x1'x2)
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// dy2/dx1 = -x2 * exp(-x1'x2 / 10) / 10, dy2/dx2 = -x2 * exp(-x1'x2 / 10) / 10
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struct TranscendentalFunctor {
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bool operator()(const double* x1, const double* x2, double* residuals) const {
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double x1x2 = 0;
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for (int i = 0; i < 5; ++i) {
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x1x2 += x1[i] * x2[i];
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}
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residuals[0] = sin(x1x2);
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residuals[1] = exp(-x1x2 / 10);
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return true;
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}
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};
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class TranscendentalTestCostFunction : public SizedCostFunction<2, 5, 5> {
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public:
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virtual bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const {
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(void) jacobians; // Ignored.
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return TranscendentalFunctor()(parameters[0], parameters[1], residuals);
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}
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};
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TEST(NumericDiffCostFunction, TransendentalOperationsInCostFunction) {
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// Try both central and forward difference.
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internal::scoped_ptr<CostFunction> cfs[4];
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cfs[0].reset(
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new NumericDiffCostFunction<TranscendentalTestCostFunction,
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CENTRAL,
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2, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new TranscendentalTestCostFunction, TAKE_OWNERSHIP));
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cfs[1].reset(
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new NumericDiffCostFunction<TranscendentalTestCostFunction,
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FORWARD,
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2, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new TranscendentalTestCostFunction, TAKE_OWNERSHIP));
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cfs[2].reset(
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new NumericDiffCostFunction<TranscendentalFunctor,
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CENTRAL,
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2, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new TranscendentalFunctor));
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cfs[3].reset(
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new NumericDiffCostFunction<TranscendentalFunctor,
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FORWARD,
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2, /* number of residuals */
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5, /* size of x1 */
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5 /* size of x2 */>(
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new TranscendentalFunctor));
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for (int c = 0; c < 4; ++c) {
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CostFunction *cost_function = cfs[c].get();
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struct {
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double x1[5];
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double x2[5];
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} kTests[] = {
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{ { 1.0, 2.0, 3.0, 4.0, 5.0 }, // No zeros.
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{ 9.0, 9.0, 5.0, 5.0, 1.0 },
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},
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{ { 0.0, 2.0, 3.0, 0.0, 5.0 }, // Some zeros x1.
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{ 9.0, 9.0, 5.0, 5.0, 1.0 },
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},
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{ { 1.0, 2.0, 3.0, 1.0, 5.0 }, // Some zeros x2.
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{ 0.0, 9.0, 0.0, 5.0, 0.0 },
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},
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{ { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros x1.
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{ 9.0, 9.0, 5.0, 5.0, 1.0 },
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},
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{ { 1.0, 2.0, 3.0, 4.0, 5.0 }, // All zeros x2.
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{ 0.0, 0.0, 0.0, 0.0, 0.0 },
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},
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{ { 0.0, 0.0, 0.0, 0.0, 0.0 }, // All zeros.
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{ 0.0, 0.0, 0.0, 0.0, 0.0 },
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},
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};
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for (int k = 0; k < CERES_ARRAYSIZE(kTests); ++k) {
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double *x1 = &(kTests[k].x1[0]);
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double *x2 = &(kTests[k].x2[0]);
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double *parameters[] = { x1, x2 };
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double dydx1[10];
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double dydx2[10];
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double *jacobians[2] = { &dydx1[0], &dydx2[0] };
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double residuals[2];
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ASSERT_TRUE(cost_function->Evaluate(¶meters[0],
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&residuals[0],
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&jacobians[0]));
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LOG(INFO) << "Ran evaluate for test k=" << k << " c=" << c;
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double x1x2 = 0;
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for (int i = 0; i < 5; ++i) {
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x1x2 += x1[i] * x2[i];
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}
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for (int i = 0; i < 5; ++i) {
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const double kEps = c == 0 ? /* central */ 3e-9 : /* forward */ 2e-5;
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ExpectClose( x2[i] * cos(x1x2), dydx1[5 * 0 + i], kEps);
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ExpectClose( x1[i] * cos(x1x2), dydx2[5 * 0 + i], kEps);
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ExpectClose(-x2[i] * exp(-x1x2 / 10.) / 10., dydx1[5 * 1 + i], kEps);
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ExpectClose(-x1[i] * exp(-x1x2 / 10.) / 10., dydx2[5 * 1 + i], kEps);
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}
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}
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}
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}
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template<int num_rows, int num_cols>
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class SizeTestingCostFunction : public SizedCostFunction<num_rows, num_cols> {
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public:
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virtual bool Evaluate(double const* const* parameters,
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double* residuals,
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double** jacobians) const {
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return true;
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}
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};
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// As described in
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// http://forum.kde.org/viewtopic.php?f=74&t=98536#p210774
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// Eigen3 has restrictions on the Row/Column major storage of vectors,
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// depending on their dimensions. This test ensures that the correct
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// templates are instantiated for various shapes of the Jacobian
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// matrix.
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TEST(NumericDiffCostFunction, EigenRowMajorColMajorTest) {
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scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<SizeTestingCostFunction<1,1>, CENTRAL, 1, 1>(
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new SizeTestingCostFunction<1,1>, ceres::TAKE_OWNERSHIP));
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cost_function.reset(
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new NumericDiffCostFunction<SizeTestingCostFunction<2,1>, CENTRAL, 2, 1>(
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new SizeTestingCostFunction<2,1>, ceres::TAKE_OWNERSHIP));
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cost_function.reset(
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new NumericDiffCostFunction<SizeTestingCostFunction<1,2>, CENTRAL, 1, 2>(
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new SizeTestingCostFunction<1,2>, ceres::TAKE_OWNERSHIP));
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cost_function.reset(
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new NumericDiffCostFunction<SizeTestingCostFunction<2,2>, CENTRAL, 2, 2>(
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new SizeTestingCostFunction<2,2>, ceres::TAKE_OWNERSHIP));
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}
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} // namespace internal
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} // namespace ceres
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