internal/ceres/dynamic_autodiff_cost_function_test.cc

// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2012 Google Inc. All rights reserved.
// http://code.google.com/p/ceres-solver/
//
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//
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// Author: thadh@gmail.com (Thad Hughes)
//         mierle@gmail.com (Keir Mierle)
//         sameeragarwal@google.com (Sameer Agarwal)

#include "ceres/dynamic_autodiff_cost_function.h"

#include <cstddef>

#include "gtest/gtest.h"

namespace ceres {
namespace internal {

// 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:
  template <typename T>
  bool operator()(T const* const* parameters, T* residuals) const {
    const T* params0 = parameters[0];
    int r = 0;
    for (int i = 0; i < 10; ++i) {
      residuals[r++] = T(i) - params0[i];
      residuals[r++] = params0[i] - T(i);
    }

    T c_residual(0.0);
    for (int i = 0; i < 10; ++i) {
      c_residual += pow(params0[i], 2) - T(8) * params0[i];
    }

    const T* params1 = parameters[1];
    for (int i = 0; i < 5; ++i) {
      c_residual += params1[i];
    }
    residuals[r++] = c_residual;
    return true;
  }
};

TEST(DynamicAutodiffCostFunctionTest, TestResiduals) {
  vector<double> param_block_0(10, 0.0);
  vector<double> param_block_1(5, 0.0);
  DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = &param_block_0[0];
  parameter_blocks[1] = &param_block_1[0];
  EXPECT_TRUE(cost_function.Evaluate(&parameter_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(DynamicAutodiffCostFunctionTest, 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);
  DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = &param_block_0[0];
  parameter_blocks[1] = &param_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_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
    // Check "B" Jacobian.
    EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
    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_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
    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]);
  }

  // Check "C" Jacobian for second parameter block.
  for (int p = 0; p < 5; ++p) {
    EXPECT_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
    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(DynamicAutodiffCostFunctionTest, JacobianWithFirstParameterBlockConstant) {
  // 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);
  DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = &param_block_0[0];
  parameter_blocks[1] = &param_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_EQ(1.0, jacobian_vect[1][20 * 5 + p]);
    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(DynamicAutodiffCostFunctionTest, JacobianWithSecondParameterBlockConstant) {
  // 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);
  DynamicAutoDiffCostFunction<MyCostFunctor, 3> 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] = &param_block_0[0];
  parameter_blocks[1] = &param_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_EQ(-1.0, jacobian_vect[0][2*p * 10 + p]);
    // Check "B" Jacobian.
    EXPECT_EQ(+1.0, jacobian_vect[0][(2*p+1) * 10 + p]);
    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_EQ(4 * p - 8, jacobian_vect[0][20 * 10 + p]);
    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]);
  }
}

}  // namespace internal
}  // namespace ceres