mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 16:40:38 +08:00
Sameer Agarwal
2dd907744d
ResidualBlock evaluation has logic to ensure that CostFunction should always fill out the residual and jacobian arrays completely by using a special value to pre-populate these arrays. This works for CostFunctions with analytical Jacobians but not for AutoDiffCostFunction and NumericDiffCostFunction Jacobians. There is no way to fix this for NumericDiffCostFunctions without introducing significant performance penalties but the residual evaluation fails, which should be enough to catch such errors. For AutoDiffCostFunction the way the Jets are default initialized was sidestepping this check. So now, the Jet that is used to capture the output residuals is now initialized with kImpossibleValue, which will ensure that if the user forgets to fill all output fields, it triggers an evaluation error. This change required that ceres::internal::kImpossibleValue be moved out of array_utils.h/cc to types.h. Change-Id: I35bb0946cf0785a5d43c7b5459a2272848fb2a9b
390 lines
16 KiB
C++
390 lines
16 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2015 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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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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// tbennun@gmail.com (Tal Ben-Nun)
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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/array_utils.h"
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#include "ceres/numeric_diff_test_utils.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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TEST(NumericDiffCostFunction, EasyCaseFunctorCentralDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.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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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, CENTRAL);
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}
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TEST(NumericDiffCostFunction, EasyCaseFunctorForwardDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.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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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, FORWARD);
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}
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TEST(NumericDiffCostFunction, EasyCaseFunctorRidders) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor,
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RIDDERS,
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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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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, RIDDERS);
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}
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TEST(NumericDiffCostFunction, EasyCaseCostFunctionCentralDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.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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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, CENTRAL);
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}
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TEST(NumericDiffCostFunction, EasyCaseCostFunctionForwardDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.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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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, FORWARD);
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}
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TEST(NumericDiffCostFunction, EasyCaseCostFunctionRidders) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<EasyCostFunction,
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RIDDERS,
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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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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, RIDDERS);
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}
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TEST(NumericDiffCostFunction,
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TranscendentalCaseFunctorCentralDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.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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TranscendentalFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, CENTRAL);
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}
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TEST(NumericDiffCostFunction,
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TranscendentalCaseFunctorForwardDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.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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TranscendentalFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, FORWARD);
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}
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TEST(NumericDiffCostFunction,
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TranscendentalCaseFunctorRidders) {
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NumericDiffOptions options;
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// Using a smaller initial step size to overcome oscillatory function
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// behavior.
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options.ridders_relative_initial_step_size = 1e-3;
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<TranscendentalFunctor,
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RIDDERS,
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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, TAKE_OWNERSHIP, 2, options));
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TranscendentalFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, RIDDERS);
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}
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TEST(NumericDiffCostFunction,
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TranscendentalCaseCostFunctionCentralDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<TranscendentalCostFunction,
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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 TranscendentalCostFunction, TAKE_OWNERSHIP));
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TranscendentalFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, CENTRAL);
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}
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TEST(NumericDiffCostFunction,
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TranscendentalCaseCostFunctionForwardDifferences) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<TranscendentalCostFunction,
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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 TranscendentalCostFunction, TAKE_OWNERSHIP));
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TranscendentalFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, FORWARD);
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}
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TEST(NumericDiffCostFunction,
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TranscendentalCaseCostFunctionRidders) {
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NumericDiffOptions options;
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// Using a smaller initial step size to overcome oscillatory function
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// behavior.
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options.ridders_relative_initial_step_size = 1e-3;
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<TranscendentalCostFunction,
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RIDDERS,
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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 TranscendentalCostFunction, TAKE_OWNERSHIP, 2, options));
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TranscendentalFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, RIDDERS);
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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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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor, CENTRAL, ceres::DYNAMIC, 1, 1>(
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new EasyFunctor, TAKE_OWNERSHIP, 1));
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor, CENTRAL, ceres::DYNAMIC, 1, 1>(
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new EasyFunctor, TAKE_OWNERSHIP, 2));
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor, CENTRAL, ceres::DYNAMIC, 1, 2>(
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new EasyFunctor, TAKE_OWNERSHIP, 1));
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor, CENTRAL, ceres::DYNAMIC, 1, 2>(
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new EasyFunctor, TAKE_OWNERSHIP, 2));
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor, CENTRAL, ceres::DYNAMIC, 2, 1>(
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new EasyFunctor, TAKE_OWNERSHIP, 1));
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor, CENTRAL, ceres::DYNAMIC, 2, 1>(
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new EasyFunctor, TAKE_OWNERSHIP, 2));
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}
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TEST(NumericDiffCostFunction,
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EasyCaseFunctorCentralDifferencesAndDynamicNumResiduals) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<EasyFunctor,
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CENTRAL,
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ceres::DYNAMIC,
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5, /* size of x1 */
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5 /* size of x2 */>(
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new EasyFunctor, TAKE_OWNERSHIP, 3));
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EasyFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function, CENTRAL);
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}
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TEST(NumericDiffCostFunction, ExponentialFunctorRidders) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<ExponentialFunctor,
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RIDDERS,
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1, /* number of residuals */
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1 /* size of x1 */>(
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new ExponentialFunctor));
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ExponentialFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function);
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}
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TEST(NumericDiffCostFunction, ExponentialCostFunctionRidders) {
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internal::scoped_ptr<CostFunction> cost_function;
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cost_function.reset(
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new NumericDiffCostFunction<ExponentialCostFunction,
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RIDDERS,
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1, /* number of residuals */
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1 /* size of x1 */>(
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new ExponentialCostFunction));
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ExponentialFunctor functor;
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function);
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}
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TEST(NumericDiffCostFunction, RandomizedFunctorRidders) {
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internal::scoped_ptr<CostFunction> cost_function;
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NumericDiffOptions options;
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// Larger initial step size is chosen to produce robust results in the
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// presence of random noise.
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options.ridders_relative_initial_step_size = 10.0;
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cost_function.reset(
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new NumericDiffCostFunction<RandomizedFunctor,
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RIDDERS,
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1, /* number of residuals */
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1 /* size of x1 */>(
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new RandomizedFunctor(kNoiseFactor, kRandomSeed), TAKE_OWNERSHIP,
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1, options));
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RandomizedFunctor functor (kNoiseFactor, kRandomSeed);
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function);
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}
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TEST(NumericDiffCostFunction, RandomizedCostFunctionRidders) {
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internal::scoped_ptr<CostFunction> cost_function;
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NumericDiffOptions options;
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// Larger initial step size is chosen to produce robust results in the
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// presence of random noise.
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options.ridders_relative_initial_step_size = 10.0;
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cost_function.reset(
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new NumericDiffCostFunction<RandomizedCostFunction,
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RIDDERS,
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1, /* number of residuals */
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1 /* size of x1 */>(
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new RandomizedCostFunction(kNoiseFactor, kRandomSeed),
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TAKE_OWNERSHIP, 1, options));
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RandomizedFunctor functor (kNoiseFactor, kRandomSeed);
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functor.ExpectCostFunctionEvaluationIsNearlyCorrect(*cost_function);
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}
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struct OnlyFillsOneOutputFunctor {
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bool operator()(const double* x, double* output) const {
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output[0] = x[0];
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return true;
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}
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};
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TEST(NumericDiffCostFunction, PartiallyFilledResidualShouldFailEvaluation) {
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double parameter = 1.0;
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double jacobian[2];
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double residuals[2];
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double* parameters[] = {¶meter};
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double* jacobians[] = {jacobian};
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scoped_ptr<CostFunction> cost_function(
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new NumericDiffCostFunction<OnlyFillsOneOutputFunctor, CENTRAL, 2, 1>(
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new OnlyFillsOneOutputFunctor));
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InvalidateArray(2, jacobian);
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InvalidateArray(2, residuals);
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EXPECT_TRUE(cost_function->Evaluate(parameters, residuals, jacobians));
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EXPECT_FALSE(IsArrayValid(2, residuals));
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InvalidateArray(2, residuals);
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EXPECT_TRUE(cost_function->Evaluate(parameters, residuals, NULL));
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// We are only testing residuals here, because the Jacobians are
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// computed using finite differencing from the residuals, so unless
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// we introduce a validation step after every evaluation of
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// residuals inside NumericDiffCostFunction, there is no way of
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// ensuring that the Jacobian array is invalid.
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EXPECT_FALSE(IsArrayValid(2, residuals));
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}
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} // namespace internal
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} // namespace ceres
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