| // Ceres Solver - A fast non-linear least squares minimizer |
| // Copyright 2015 Google Inc. All rights reserved. |
| // http://ceres-solver.org/ |
| // |
| // 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: sameeragarwal@google.com (Sameer Agarwal) |
| // mierle@gmail.com (Keir Mierle) |
| |
| #include <cstddef> |
| |
| #include "ceres/dynamic_numeric_diff_cost_function.h" |
| #include "ceres/internal/scoped_ptr.h" |
| #include "gtest/gtest.h" |
| |
| namespace ceres { |
| namespace internal { |
| |
| using std::vector; |
| |
| const double kTolerance = 1e-6; |
| |
| // Takes 2 parameter blocks: |
| // parameters[0] is size 10. |
| // parameters[1] is size 5. |
| // Emits 21 residuals: |
| // A: i - parameters[0][i], for i in [0,10) -- this is 10 residuals |
| // B: parameters[0][i] - i, for i in [0,10) -- this is another 10. |
| // C: sum(parameters[0][i]^2 - 8*parameters[0][i]) + sum(parameters[1][i]) |
| class MyCostFunctor { |
| public: |
| bool operator()(double const* const* parameters, double* residuals) const { |
| const double* params0 = parameters[0]; |
| int r = 0; |
| for (int i = 0; i < 10; ++i) { |
| residuals[r++] = i - params0[i]; |
| residuals[r++] = params0[i] - i; |
| } |
| |
| double c_residual = 0.0; |
| for (int i = 0; i < 10; ++i) { |
| c_residual += pow(params0[i], 2) - 8.0 * params0[i]; |
| } |
| |
| const double* params1 = parameters[1]; |
| for (int i = 0; i < 5; ++i) { |
| c_residual += params1[i]; |
| } |
| residuals[r++] = c_residual; |
| return true; |
| } |
| }; |
| |
| TEST(DynamicNumericdiffCostFunctionTest, TestResiduals) { |
| vector<double> param_block_0(10, 0.0); |
| vector<double> param_block_1(5, 0.0); |
| DynamicNumericDiffCostFunction<MyCostFunctor> cost_function( |
| new MyCostFunctor()); |
| cost_function.AddParameterBlock(param_block_0.size()); |
| cost_function.AddParameterBlock(param_block_1.size()); |
| cost_function.SetNumResiduals(21); |
| |
| // Test residual computation. |
| vector<double> residuals(21, -100000); |
| vector<double*> parameter_blocks(2); |
| parameter_blocks[0] = ¶m_block_0[0]; |
| parameter_blocks[1] = ¶m_block_1[0]; |
| EXPECT_TRUE(cost_function.Evaluate(¶meter_blocks[0], |
| residuals.data(), |
| NULL)); |
| for (int r = 0; r < 10; ++r) { |
| EXPECT_EQ(1.0 * r, residuals.at(r * 2)); |
| EXPECT_EQ(-1.0 * r, residuals.at(r * 2 + 1)); |
| } |
| EXPECT_EQ(0, residuals.at(20)); |
| } |
| |
| |
| TEST(DynamicNumericdiffCostFunctionTest, TestJacobian) { |
| // Test the residual counting. |
| vector<double> param_block_0(10, 0.0); |
| for (int i = 0; i < 10; ++i) { |
| param_block_0[i] = 2 * i; |
| } |
| vector<double> param_block_1(5, 0.0); |
| DynamicNumericDiffCostFunction<MyCostFunctor> cost_function( |
| new MyCostFunctor()); |
| cost_function.AddParameterBlock(param_block_0.size()); |
| cost_function.AddParameterBlock(param_block_1.size()); |
| cost_function.SetNumResiduals(21); |
| |
| // Prepare the residuals. |
| vector<double> residuals(21, -100000); |
| |
| // Prepare the parameters. |
| vector<double*> parameter_blocks(2); |
| parameter_blocks[0] = ¶m_block_0[0]; |
| parameter_blocks[1] = ¶m_block_1[0]; |
| |
| // Prepare the jacobian. |
| vector<vector<double> > jacobian_vect(2); |
| jacobian_vect[0].resize(21 * 10, -100000); |
| jacobian_vect[1].resize(21 * 5, -100000); |
| vector<double*> jacobian; |
| jacobian.push_back(jacobian_vect[0].data()); |
| jacobian.push_back(jacobian_vect[1].data()); |
| |
| // Test jacobian computation. |
| EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(), |
| residuals.data(), |
| jacobian.data())); |
| |
| for (int r = 0; r < 10; ++r) { |
| EXPECT_EQ(-1.0 * r, residuals.at(r * 2)); |
| EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1)); |
| } |
| EXPECT_EQ(420, residuals.at(20)); |
| for (int p = 0; p < 10; ++p) { |
| // Check "A" Jacobian. |
| EXPECT_NEAR(-1.0, jacobian_vect[0][2*p * 10 + p], kTolerance); |
| // Check "B" Jacobian. |
| EXPECT_NEAR(+1.0, jacobian_vect[0][(2*p+1) * 10 + p], kTolerance); |
| jacobian_vect[0][2*p * 10 + p] = 0.0; |
| jacobian_vect[0][(2*p+1) * 10 + p] = 0.0; |
| } |
| |
| // Check "C" Jacobian for first parameter block. |
| for (int p = 0; p < 10; ++p) { |
| EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance); |
| jacobian_vect[0][20 * 10 + p] = 0.0; |
| } |
| for (int i = 0; i < jacobian_vect[0].size(); ++i) { |
| EXPECT_NEAR(0.0, jacobian_vect[0][i], kTolerance); |
| } |
| |
| // Check "C" Jacobian for second parameter block. |
| for (int p = 0; p < 5; ++p) { |
| EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance); |
| jacobian_vect[1][20 * 5 + p] = 0.0; |
| } |
| for (int i = 0; i < jacobian_vect[1].size(); ++i) { |
| EXPECT_NEAR(0.0, jacobian_vect[1][i], kTolerance); |
| } |
| } |
| |
| TEST(DynamicNumericdiffCostFunctionTest, JacobianWithFirstParameterBlockConstant) { // NOLINT |
| // Test the residual counting. |
| vector<double> param_block_0(10, 0.0); |
| for (int i = 0; i < 10; ++i) { |
| param_block_0[i] = 2 * i; |
| } |
| vector<double> param_block_1(5, 0.0); |
| DynamicNumericDiffCostFunction<MyCostFunctor> cost_function( |
| new MyCostFunctor()); |
| cost_function.AddParameterBlock(param_block_0.size()); |
| cost_function.AddParameterBlock(param_block_1.size()); |
| cost_function.SetNumResiduals(21); |
| |
| // Prepare the residuals. |
| vector<double> residuals(21, -100000); |
| |
| // Prepare the parameters. |
| vector<double*> parameter_blocks(2); |
| parameter_blocks[0] = ¶m_block_0[0]; |
| parameter_blocks[1] = ¶m_block_1[0]; |
| |
| // Prepare the jacobian. |
| vector<vector<double> > jacobian_vect(2); |
| jacobian_vect[0].resize(21 * 10, -100000); |
| jacobian_vect[1].resize(21 * 5, -100000); |
| vector<double*> jacobian; |
| jacobian.push_back(NULL); |
| jacobian.push_back(jacobian_vect[1].data()); |
| |
| // Test jacobian computation. |
| EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(), |
| residuals.data(), |
| jacobian.data())); |
| |
| for (int r = 0; r < 10; ++r) { |
| EXPECT_EQ(-1.0 * r, residuals.at(r * 2)); |
| EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1)); |
| } |
| EXPECT_EQ(420, residuals.at(20)); |
| |
| // Check "C" Jacobian for second parameter block. |
| for (int p = 0; p < 5; ++p) { |
| EXPECT_NEAR(1.0, jacobian_vect[1][20 * 5 + p], kTolerance); |
| jacobian_vect[1][20 * 5 + p] = 0.0; |
| } |
| for (int i = 0; i < jacobian_vect[1].size(); ++i) { |
| EXPECT_EQ(0.0, jacobian_vect[1][i]); |
| } |
| } |
| |
| TEST(DynamicNumericdiffCostFunctionTest, JacobianWithSecondParameterBlockConstant) { // NOLINT |
| // Test the residual counting. |
| vector<double> param_block_0(10, 0.0); |
| for (int i = 0; i < 10; ++i) { |
| param_block_0[i] = 2 * i; |
| } |
| vector<double> param_block_1(5, 0.0); |
| DynamicNumericDiffCostFunction<MyCostFunctor> cost_function( |
| new MyCostFunctor()); |
| cost_function.AddParameterBlock(param_block_0.size()); |
| cost_function.AddParameterBlock(param_block_1.size()); |
| cost_function.SetNumResiduals(21); |
| |
| // Prepare the residuals. |
| vector<double> residuals(21, -100000); |
| |
| // Prepare the parameters. |
| vector<double*> parameter_blocks(2); |
| parameter_blocks[0] = ¶m_block_0[0]; |
| parameter_blocks[1] = ¶m_block_1[0]; |
| |
| // Prepare the jacobian. |
| vector<vector<double> > jacobian_vect(2); |
| jacobian_vect[0].resize(21 * 10, -100000); |
| jacobian_vect[1].resize(21 * 5, -100000); |
| vector<double*> jacobian; |
| jacobian.push_back(jacobian_vect[0].data()); |
| jacobian.push_back(NULL); |
| |
| // Test jacobian computation. |
| EXPECT_TRUE(cost_function.Evaluate(parameter_blocks.data(), |
| residuals.data(), |
| jacobian.data())); |
| |
| for (int r = 0; r < 10; ++r) { |
| EXPECT_EQ(-1.0 * r, residuals.at(r * 2)); |
| EXPECT_EQ(+1.0 * r, residuals.at(r * 2 + 1)); |
| } |
| EXPECT_EQ(420, residuals.at(20)); |
| for (int p = 0; p < 10; ++p) { |
| // Check "A" Jacobian. |
| EXPECT_NEAR(-1.0, jacobian_vect[0][2*p * 10 + p], kTolerance); |
| // Check "B" Jacobian. |
| EXPECT_NEAR(+1.0, jacobian_vect[0][(2*p+1) * 10 + p], kTolerance); |
| jacobian_vect[0][2*p * 10 + p] = 0.0; |
| jacobian_vect[0][(2*p+1) * 10 + p] = 0.0; |
| } |
| |
| // Check "C" Jacobian for first parameter block. |
| for (int p = 0; p < 10; ++p) { |
| EXPECT_NEAR(4 * p - 8, jacobian_vect[0][20 * 10 + p], kTolerance); |
| jacobian_vect[0][20 * 10 + p] = 0.0; |
| } |
| for (int i = 0; i < jacobian_vect[0].size(); ++i) { |
| EXPECT_EQ(0.0, jacobian_vect[0][i]); |
| } |
| } |
| |
| // Takes 3 parameter blocks: |
| // parameters[0] (x) is size 1. |
| // parameters[1] (y) is size 2. |
| // parameters[2] (z) is size 3. |
| // Emits 7 residuals: |
| // A: x[0] (= sum_x) |
| // B: y[0] + 2.0 * y[1] (= sum_y) |
| // C: z[0] + 3.0 * z[1] + 6.0 * z[2] (= sum_z) |
| // D: sum_x * sum_y |
| // E: sum_y * sum_z |
| // F: sum_x * sum_z |
| // G: sum_x * sum_y * sum_z |
| class MyThreeParameterCostFunctor { |
| public: |
| template <typename T> |
| bool operator()(T const* const* parameters, T* residuals) const { |
| const T* x = parameters[0]; |
| const T* y = parameters[1]; |
| const T* z = parameters[2]; |
| |
| T sum_x = x[0]; |
| T sum_y = y[0] + 2.0 * y[1]; |
| T sum_z = z[0] + 3.0 * z[1] + 6.0 * z[2]; |
| |
| residuals[0] = sum_x; |
| residuals[1] = sum_y; |
| residuals[2] = sum_z; |
| residuals[3] = sum_x * sum_y; |
| residuals[4] = sum_y * sum_z; |
| residuals[5] = sum_x * sum_z; |
| residuals[6] = sum_x * sum_y * sum_z; |
| return true; |
| } |
| }; |
| |
| class ThreeParameterCostFunctorTest : public ::testing::Test { |
| protected: |
| virtual void SetUp() { |
| // Prepare the parameters. |
| x_.resize(1); |
| x_[0] = 0.0; |
| |
| y_.resize(2); |
| y_[0] = 1.0; |
| y_[1] = 3.0; |
| |
| z_.resize(3); |
| z_[0] = 2.0; |
| z_[1] = 4.0; |
| z_[2] = 6.0; |
| |
| parameter_blocks_.resize(3); |
| parameter_blocks_[0] = &x_[0]; |
| parameter_blocks_[1] = &y_[0]; |
| parameter_blocks_[2] = &z_[0]; |
| |
| // Prepare the cost function. |
| typedef DynamicNumericDiffCostFunction<MyThreeParameterCostFunctor> |
| DynamicMyThreeParameterCostFunction; |
| DynamicMyThreeParameterCostFunction * cost_function = |
| new DynamicMyThreeParameterCostFunction( |
| new MyThreeParameterCostFunctor()); |
| cost_function->AddParameterBlock(1); |
| cost_function->AddParameterBlock(2); |
| cost_function->AddParameterBlock(3); |
| cost_function->SetNumResiduals(7); |
| |
| cost_function_.reset(cost_function); |
| |
| // Setup jacobian data. |
| jacobian_vect_.resize(3); |
| jacobian_vect_[0].resize(7 * x_.size(), -100000); |
| jacobian_vect_[1].resize(7 * y_.size(), -100000); |
| jacobian_vect_[2].resize(7 * z_.size(), -100000); |
| |
| // Prepare the expected residuals. |
| const double sum_x = x_[0]; |
| const double sum_y = y_[0] + 2.0 * y_[1]; |
| const double sum_z = z_[0] + 3.0 * z_[1] + 6.0 * z_[2]; |
| |
| expected_residuals_.resize(7); |
| expected_residuals_[0] = sum_x; |
| expected_residuals_[1] = sum_y; |
| expected_residuals_[2] = sum_z; |
| expected_residuals_[3] = sum_x * sum_y; |
| expected_residuals_[4] = sum_y * sum_z; |
| expected_residuals_[5] = sum_x * sum_z; |
| expected_residuals_[6] = sum_x * sum_y * sum_z; |
| |
| // Prepare the expected jacobian entries. |
| expected_jacobian_x_.resize(7); |
| expected_jacobian_x_[0] = 1.0; |
| expected_jacobian_x_[1] = 0.0; |
| expected_jacobian_x_[2] = 0.0; |
| expected_jacobian_x_[3] = sum_y; |
| expected_jacobian_x_[4] = 0.0; |
| expected_jacobian_x_[5] = sum_z; |
| expected_jacobian_x_[6] = sum_y * sum_z; |
| |
| expected_jacobian_y_.resize(14); |
| expected_jacobian_y_[0] = 0.0; |
| expected_jacobian_y_[1] = 0.0; |
| expected_jacobian_y_[2] = 1.0; |
| expected_jacobian_y_[3] = 2.0; |
| expected_jacobian_y_[4] = 0.0; |
| expected_jacobian_y_[5] = 0.0; |
| expected_jacobian_y_[6] = sum_x; |
| expected_jacobian_y_[7] = 2.0 * sum_x; |
| expected_jacobian_y_[8] = sum_z; |
| expected_jacobian_y_[9] = 2.0 * sum_z; |
| expected_jacobian_y_[10] = 0.0; |
| expected_jacobian_y_[11] = 0.0; |
| expected_jacobian_y_[12] = sum_x * sum_z; |
| expected_jacobian_y_[13] = 2.0 * sum_x * sum_z; |
| |
| expected_jacobian_z_.resize(21); |
| expected_jacobian_z_[0] = 0.0; |
| expected_jacobian_z_[1] = 0.0; |
| expected_jacobian_z_[2] = 0.0; |
| expected_jacobian_z_[3] = 0.0; |
| expected_jacobian_z_[4] = 0.0; |
| expected_jacobian_z_[5] = 0.0; |
| expected_jacobian_z_[6] = 1.0; |
| expected_jacobian_z_[7] = 3.0; |
| expected_jacobian_z_[8] = 6.0; |
| expected_jacobian_z_[9] = 0.0; |
| expected_jacobian_z_[10] = 0.0; |
| expected_jacobian_z_[11] = 0.0; |
| expected_jacobian_z_[12] = sum_y; |
| expected_jacobian_z_[13] = 3.0 * sum_y; |
| expected_jacobian_z_[14] = 6.0 * sum_y; |
| expected_jacobian_z_[15] = sum_x; |
| expected_jacobian_z_[16] = 3.0 * sum_x; |
| expected_jacobian_z_[17] = 6.0 * sum_x; |
| expected_jacobian_z_[18] = sum_x * sum_y; |
| expected_jacobian_z_[19] = 3.0 * sum_x * sum_y; |
| expected_jacobian_z_[20] = 6.0 * sum_x * sum_y; |
| } |
| |
| protected: |
| vector<double> x_; |
| vector<double> y_; |
| vector<double> z_; |
| |
| vector<double*> parameter_blocks_; |
| |
| scoped_ptr<CostFunction> cost_function_; |
| |
| vector<vector<double> > jacobian_vect_; |
| |
| vector<double> expected_residuals_; |
| |
| vector<double> expected_jacobian_x_; |
| vector<double> expected_jacobian_y_; |
| vector<double> expected_jacobian_z_; |
| }; |
| |
| TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterResiduals) { |
| vector<double> residuals(7, -100000); |
| EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(), |
| residuals.data(), |
| NULL)); |
| for (int i = 0; i < 7; ++i) { |
| EXPECT_EQ(expected_residuals_[i], residuals[i]); |
| } |
| } |
| |
| TEST_F(ThreeParameterCostFunctorTest, TestThreeParameterJacobian) { |
| vector<double> residuals(7, -100000); |
| |
| vector<double*> jacobian; |
| jacobian.push_back(jacobian_vect_[0].data()); |
| jacobian.push_back(jacobian_vect_[1].data()); |
| jacobian.push_back(jacobian_vect_[2].data()); |
| |
| EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(), |
| residuals.data(), |
| jacobian.data())); |
| |
| for (int i = 0; i < 7; ++i) { |
| EXPECT_EQ(expected_residuals_[i], residuals[i]); |
| } |
| |
| for (int i = 0; i < 7; ++i) { |
| EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance); |
| } |
| |
| for (int i = 0; i < 14; ++i) { |
| EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance); |
| } |
| |
| for (int i = 0; i < 21; ++i) { |
| EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance); |
| } |
| } |
| |
| TEST_F(ThreeParameterCostFunctorTest, |
| ThreeParameterJacobianWithFirstAndLastParameterBlockConstant) { |
| vector<double> residuals(7, -100000); |
| |
| vector<double*> jacobian; |
| jacobian.push_back(NULL); |
| jacobian.push_back(jacobian_vect_[1].data()); |
| jacobian.push_back(NULL); |
| |
| EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(), |
| residuals.data(), |
| jacobian.data())); |
| |
| for (int i = 0; i < 7; ++i) { |
| EXPECT_EQ(expected_residuals_[i], residuals[i]); |
| } |
| |
| for (int i = 0; i < 14; ++i) { |
| EXPECT_NEAR(expected_jacobian_y_[i], jacobian[1][i], kTolerance); |
| } |
| } |
| |
| TEST_F(ThreeParameterCostFunctorTest, |
| ThreeParameterJacobianWithSecondParameterBlockConstant) { |
| vector<double> residuals(7, -100000); |
| |
| vector<double*> jacobian; |
| jacobian.push_back(jacobian_vect_[0].data()); |
| jacobian.push_back(NULL); |
| jacobian.push_back(jacobian_vect_[2].data()); |
| |
| EXPECT_TRUE(cost_function_->Evaluate(parameter_blocks_.data(), |
| residuals.data(), |
| jacobian.data())); |
| |
| for (int i = 0; i < 7; ++i) { |
| EXPECT_EQ(expected_residuals_[i], residuals[i]); |
| } |
| |
| for (int i = 0; i < 7; ++i) { |
| EXPECT_NEAR(expected_jacobian_x_[i], jacobian[0][i], kTolerance); |
| } |
| |
| for (int i = 0; i < 21; ++i) { |
| EXPECT_NEAR(expected_jacobian_z_[i], jacobian[2][i], kTolerance); |
| } |
| } |
| |
| } // namespace internal |
| } // namespace ceres |