329 lines
11 KiB
C++
329 lines
11 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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#include "ceres/residual_block.h"
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#include "gtest/gtest.h"
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#include "ceres/parameter_block.h"
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#include "ceres/sized_cost_function.h"
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#include "ceres/internal/eigen.h"
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#include "ceres/local_parameterization.h"
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namespace ceres {
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namespace internal {
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using std::vector;
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// Trivial cost function that accepts three arguments.
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class TernaryCostFunction: public CostFunction {
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public:
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TernaryCostFunction(int num_residuals,
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int32 parameter_block1_size,
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int32 parameter_block2_size,
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int32 parameter_block3_size) {
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set_num_residuals(num_residuals);
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mutable_parameter_block_sizes()->push_back(parameter_block1_size);
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mutable_parameter_block_sizes()->push_back(parameter_block2_size);
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mutable_parameter_block_sizes()->push_back(parameter_block3_size);
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}
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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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for (int i = 0; i < num_residuals(); ++i) {
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residuals[i] = i;
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}
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if (jacobians) {
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for (int k = 0; k < 3; ++k) {
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if (jacobians[k] != NULL) {
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MatrixRef jacobian(jacobians[k],
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num_residuals(),
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parameter_block_sizes()[k]);
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jacobian.setConstant(k);
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}
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}
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}
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return true;
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}
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};
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TEST(ResidualBlock, EvaluteWithNoLossFunctionOrLocalParameterizations) {
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double scratch[64];
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// Prepare the parameter blocks.
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double values_x[2];
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ParameterBlock x(values_x, 2, -1);
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double values_y[3];
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ParameterBlock y(values_y, 3, -1);
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double values_z[4];
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ParameterBlock z(values_z, 4, -1);
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vector<ParameterBlock*> parameters;
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parameters.push_back(&x);
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parameters.push_back(&y);
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parameters.push_back(&z);
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TernaryCostFunction cost_function(3, 2, 3, 4);
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// Create the object under tests.
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ResidualBlock residual_block(&cost_function, NULL, parameters, -1);
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// Verify getters.
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EXPECT_EQ(&cost_function, residual_block.cost_function());
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EXPECT_EQ(NULL, residual_block.loss_function());
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EXPECT_EQ(parameters[0], residual_block.parameter_blocks()[0]);
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EXPECT_EQ(parameters[1], residual_block.parameter_blocks()[1]);
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EXPECT_EQ(parameters[2], residual_block.parameter_blocks()[2]);
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EXPECT_EQ(3, residual_block.NumScratchDoublesForEvaluate());
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// Verify cost-only evaluation.
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double cost;
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residual_block.Evaluate(true, &cost, NULL, NULL, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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// Verify cost and residual evaluation.
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double residuals[3];
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residual_block.Evaluate(true, &cost, residuals, NULL, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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EXPECT_EQ(0.0, residuals[0]);
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EXPECT_EQ(1.0, residuals[1]);
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EXPECT_EQ(2.0, residuals[2]);
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// Verify cost, residual, and jacobian evaluation.
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cost = 0.0;
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VectorRef(residuals, 3).setConstant(0.0);
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Matrix jacobian_rx(3, 2);
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Matrix jacobian_ry(3, 3);
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Matrix jacobian_rz(3, 4);
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jacobian_rx.setConstant(-1.0);
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jacobian_ry.setConstant(-1.0);
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jacobian_rz.setConstant(-1.0);
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double *jacobian_ptrs[3] = {
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jacobian_rx.data(),
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jacobian_ry.data(),
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jacobian_rz.data()
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};
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residual_block.Evaluate(true, &cost, residuals, jacobian_ptrs, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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EXPECT_EQ(0.0, residuals[0]);
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EXPECT_EQ(1.0, residuals[1]);
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EXPECT_EQ(2.0, residuals[2]);
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EXPECT_TRUE((jacobian_rx.array() == 0.0).all()) << "\n" << jacobian_rx;
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EXPECT_TRUE((jacobian_ry.array() == 1.0).all()) << "\n" << jacobian_ry;
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EXPECT_TRUE((jacobian_rz.array() == 2.0).all()) << "\n" << jacobian_rz;
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// Verify cost, residual, and partial jacobian evaluation.
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cost = 0.0;
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VectorRef(residuals, 3).setConstant(0.0);
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jacobian_rx.setConstant(-1.0);
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jacobian_ry.setConstant(-1.0);
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jacobian_rz.setConstant(-1.0);
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jacobian_ptrs[1] = NULL; // Don't compute the jacobian for y.
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residual_block.Evaluate(true, &cost, residuals, jacobian_ptrs, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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EXPECT_EQ(0.0, residuals[0]);
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EXPECT_EQ(1.0, residuals[1]);
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EXPECT_EQ(2.0, residuals[2]);
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EXPECT_TRUE((jacobian_rx.array() == 0.0).all()) << "\n" << jacobian_rx;
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EXPECT_TRUE((jacobian_ry.array() == -1.0).all()) << "\n" << jacobian_ry;
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EXPECT_TRUE((jacobian_rz.array() == 2.0).all()) << "\n" << jacobian_rz;
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}
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// Trivial cost function that accepts three arguments.
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class LocallyParameterizedCostFunction: public SizedCostFunction<3, 2, 3, 4> {
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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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for (int i = 0; i < num_residuals(); ++i) {
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residuals[i] = i;
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}
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if (jacobians) {
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for (int k = 0; k < 3; ++k) {
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// The jacobians here are full sized, but they are transformed in the
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// evaluator into the "local" jacobian. In the tests, the "subset
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// constant" parameterization is used, which should pick out columns
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// from these jacobians. Put values in the jacobian that make this
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// obvious; in particular, make the jacobians like this:
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//
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// 0 1 2 3 4 ...
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// 0 1 2 3 4 ...
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// 0 1 2 3 4 ...
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//
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if (jacobians[k] != NULL) {
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MatrixRef jacobian(jacobians[k],
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num_residuals(),
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parameter_block_sizes()[k]);
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for (int j = 0; j < k + 2; ++j) {
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jacobian.col(j).setConstant(j);
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}
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}
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}
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}
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return true;
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}
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};
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TEST(ResidualBlock, EvaluteWithLocalParameterizations) {
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double scratch[64];
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// Prepare the parameter blocks.
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double values_x[2];
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ParameterBlock x(values_x, 2, -1);
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double values_y[3];
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ParameterBlock y(values_y, 3, -1);
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double values_z[4];
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ParameterBlock z(values_z, 4, -1);
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vector<ParameterBlock*> parameters;
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parameters.push_back(&x);
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parameters.push_back(&y);
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parameters.push_back(&z);
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// Make x have the first component fixed.
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vector<int> x_fixed;
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x_fixed.push_back(0);
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SubsetParameterization x_parameterization(2, x_fixed);
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x.SetParameterization(&x_parameterization);
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// Make z have the last and last component fixed.
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vector<int> z_fixed;
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z_fixed.push_back(2);
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SubsetParameterization z_parameterization(4, z_fixed);
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z.SetParameterization(&z_parameterization);
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LocallyParameterizedCostFunction cost_function;
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// Create the object under tests.
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ResidualBlock residual_block(&cost_function, NULL, parameters, -1);
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// Verify getters.
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EXPECT_EQ(&cost_function, residual_block.cost_function());
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EXPECT_EQ(NULL, residual_block.loss_function());
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EXPECT_EQ(parameters[0], residual_block.parameter_blocks()[0]);
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EXPECT_EQ(parameters[1], residual_block.parameter_blocks()[1]);
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EXPECT_EQ(parameters[2], residual_block.parameter_blocks()[2]);
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EXPECT_EQ(3*(2 + 4) + 3, residual_block.NumScratchDoublesForEvaluate());
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// Verify cost-only evaluation.
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double cost;
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residual_block.Evaluate(true, &cost, NULL, NULL, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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// Verify cost and residual evaluation.
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double residuals[3];
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residual_block.Evaluate(true, &cost, residuals, NULL, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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EXPECT_EQ(0.0, residuals[0]);
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EXPECT_EQ(1.0, residuals[1]);
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EXPECT_EQ(2.0, residuals[2]);
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// Verify cost, residual, and jacobian evaluation.
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cost = 0.0;
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VectorRef(residuals, 3).setConstant(0.0);
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Matrix jacobian_rx(3, 1); // Since the first element is fixed.
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Matrix jacobian_ry(3, 3);
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Matrix jacobian_rz(3, 3); // Since the third element is fixed.
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jacobian_rx.setConstant(-1.0);
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jacobian_ry.setConstant(-1.0);
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jacobian_rz.setConstant(-1.0);
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double *jacobian_ptrs[3] = {
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jacobian_rx.data(),
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jacobian_ry.data(),
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jacobian_rz.data()
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};
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residual_block.Evaluate(true, &cost, residuals, jacobian_ptrs, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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EXPECT_EQ(0.0, residuals[0]);
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EXPECT_EQ(1.0, residuals[1]);
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EXPECT_EQ(2.0, residuals[2]);
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Matrix expected_jacobian_rx(3, 1);
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expected_jacobian_rx << 1.0, 1.0, 1.0;
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Matrix expected_jacobian_ry(3, 3);
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expected_jacobian_ry << 0.0, 1.0, 2.0,
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0.0, 1.0, 2.0,
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0.0, 1.0, 2.0;
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Matrix expected_jacobian_rz(3, 3);
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expected_jacobian_rz << 0.0, 1.0, /* 2.0, */ 3.0, // 3rd parameter constant.
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0.0, 1.0, /* 2.0, */ 3.0,
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0.0, 1.0, /* 2.0, */ 3.0;
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EXPECT_EQ(expected_jacobian_rx, jacobian_rx)
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<< "\nExpected:\n" << expected_jacobian_rx
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<< "\nActual:\n" << jacobian_rx;
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EXPECT_EQ(expected_jacobian_ry, jacobian_ry)
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<< "\nExpected:\n" << expected_jacobian_ry
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<< "\nActual:\n" << jacobian_ry;
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EXPECT_EQ(expected_jacobian_rz, jacobian_rz)
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<< "\nExpected:\n " << expected_jacobian_rz
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<< "\nActual:\n" << jacobian_rz;
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// Verify cost, residual, and partial jacobian evaluation.
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cost = 0.0;
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VectorRef(residuals, 3).setConstant(0.0);
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jacobian_rx.setConstant(-1.0);
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jacobian_ry.setConstant(-1.0);
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jacobian_rz.setConstant(-1.0);
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jacobian_ptrs[1] = NULL; // Don't compute the jacobian for y.
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residual_block.Evaluate(true, &cost, residuals, jacobian_ptrs, scratch);
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EXPECT_EQ(0.5 * (0*0 + 1*1 + 2*2), cost);
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EXPECT_EQ(0.0, residuals[0]);
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EXPECT_EQ(1.0, residuals[1]);
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EXPECT_EQ(2.0, residuals[2]);
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EXPECT_EQ(expected_jacobian_rx, jacobian_rx);
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EXPECT_TRUE((jacobian_ry.array() == -1.0).all()) << "\n" << jacobian_ry;
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EXPECT_EQ(expected_jacobian_rz, jacobian_rz);
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}
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} // namespace internal
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} // namespace ceres
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