Add DynamicNumericDiffCostFunction.
This brings the ability to have numerically differentiated
cost functions to be added with its structure decided on
runtime rather than compile time.
And some minor cleanups.
Two things still need to be done.
a. Update the modeling docs.
b. Remove RuntimeNumericDiffCostFunction in ceres::internal
and replace its usage with DynamicNumericDiffCostFunction.
Change-Id: Ib771f093f29236c95a99df31c584d579b8e36615
diff --git a/internal/ceres/dynamic_numeric_diff_cost_function_test.cc b/internal/ceres/dynamic_numeric_diff_cost_function_test.cc
new file mode 100644
index 0000000..19f4d88
--- /dev/null
+++ b/internal/ceres/dynamic_numeric_diff_cost_function_test.cc
@@ -0,0 +1,519 @@
+// Ceres Solver - A fast non-linear least squares minimizer
+// Copyright 2013 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: 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 {
+
+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
