Fix an exact equality test causing breakage in gradient_checker_test.
Also clang-format the file to fix some accumulated cruft.
Change-Id: Icf41e3f864a4fe925426477893602b50ff2131f4
diff --git a/internal/ceres/gradient_checker_test.cc b/internal/ceres/gradient_checker_test.cc
index 3d40a4d..85d58b9 100644
--- a/internal/ceres/gradient_checker_test.cc
+++ b/internal/ceres/gradient_checker_test.cc
@@ -1,5 +1,5 @@
// Ceres Solver - A fast non-linear least squares minimizer
-// Copyright 2015 Google Inc. All rights reserved.
+// Copyright 2016 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// Redistribution and use in source and binary forms, with or without
@@ -58,7 +58,7 @@
// version, they are both block vectors, of course.
class GoodTestTerm : public CostFunction {
public:
- GoodTestTerm(int arity, int const *dim) : arity_(arity), return_value_(true) {
+ GoodTestTerm(int arity, int const* dim) : arity_(arity), return_value_(true) {
// Make 'arity' random vectors.
a_.resize(arity_);
for (int j = 0; j < arity_; ++j) {
@@ -98,7 +98,7 @@
if (jacobians[j]) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
// See comments before class.
- jacobians[j][u] = - f * a_[j][u];
+ jacobians[j][u] = -f * a_[j][u];
}
}
}
@@ -107,9 +107,7 @@
return true;
}
- void SetReturnValue(bool return_value) {
- return_value_ = return_value;
- }
+ void SetReturnValue(bool return_value) { return_value_ = return_value; }
private:
int arity_;
@@ -119,7 +117,7 @@
class BadTestTerm : public CostFunction {
public:
- BadTestTerm(int arity, int const *dim) : arity_(arity) {
+ BadTestTerm(int arity, int const* dim) : arity_(arity) {
// Make 'arity' random vectors.
a_.resize(arity_);
for (int j = 0; j < arity_; ++j) {
@@ -156,7 +154,7 @@
if (jacobians[j]) {
for (int u = 0; u < parameter_block_sizes()[j]; ++u) {
// See comments before class.
- jacobians[j][u] = - f * a_[j][u] + 0.001;
+ jacobians[j][u] = -f * a_[j][u] + 0.001;
}
}
}
@@ -172,10 +170,10 @@
const double kTolerance = 1e-6;
-void CheckDimensions(
- const GradientChecker::ProbeResults& results,
- const std::vector<int>& parameter_sizes,
- const std::vector<int>& local_parameter_sizes, int residual_size) {
+void CheckDimensions(const GradientChecker::ProbeResults& results,
+ const std::vector<int>& parameter_sizes,
+ const std::vector<int>& local_parameter_sizes,
+ int residual_size) {
CHECK_EQ(parameter_sizes.size(), local_parameter_sizes.size());
int num_parameters = parameter_sizes.size();
ASSERT_EQ(residual_size, results.residuals.size());
@@ -187,7 +185,8 @@
EXPECT_EQ(residual_size, results.local_jacobians.at(i).rows());
EXPECT_EQ(local_parameter_sizes[i], results.local_jacobians.at(i).cols());
EXPECT_EQ(residual_size, results.local_numeric_jacobians.at(i).rows());
- EXPECT_EQ(local_parameter_sizes[i], results.local_numeric_jacobians.at(i).cols());
+ EXPECT_EQ(local_parameter_sizes[i],
+ results.local_numeric_jacobians.at(i).cols());
EXPECT_EQ(residual_size, results.jacobians.at(i).rows());
EXPECT_EQ(parameter_sizes[i], results.jacobians.at(i).cols());
EXPECT_EQ(residual_size, results.numeric_jacobians.at(i).rows());
@@ -221,9 +220,9 @@
GoodTestTerm good_term(num_parameters, parameter_sizes.data());
GradientChecker good_gradient_checker(&good_term, NULL, numeric_diff_options);
EXPECT_TRUE(good_gradient_checker.Probe(parameters.get(), kTolerance, NULL));
- EXPECT_TRUE(good_gradient_checker.Probe(parameters.get(), kTolerance,
- &results))
- << results.error_log;
+ EXPECT_TRUE(
+ good_gradient_checker.Probe(parameters.get(), kTolerance, &results))
+ << results.error_log;
// Check that results contain sensible data.
ASSERT_EQ(results.return_value, true);
@@ -235,9 +234,9 @@
// Test that if the cost function return false, Probe should return false.
good_term.SetReturnValue(false);
EXPECT_FALSE(good_gradient_checker.Probe(parameters.get(), kTolerance, NULL));
- EXPECT_FALSE(good_gradient_checker.Probe(parameters.get(), kTolerance,
- &results))
- << results.error_log;
+ EXPECT_FALSE(
+ good_gradient_checker.Probe(parameters.get(), kTolerance, &results))
+ << results.error_log;
// Check that results contain sensible data.
ASSERT_EQ(results.return_value, false);
@@ -254,8 +253,8 @@
BadTestTerm bad_term(num_parameters, parameter_sizes.data());
GradientChecker bad_gradient_checker(&bad_term, NULL, numeric_diff_options);
EXPECT_FALSE(bad_gradient_checker.Probe(parameters.get(), kTolerance, NULL));
- EXPECT_FALSE(bad_gradient_checker.Probe(parameters.get(), kTolerance,
- &results));
+ EXPECT_FALSE(
+ bad_gradient_checker.Probe(parameters.get(), kTolerance, &results));
// Check that results contain sensible data.
ASSERT_EQ(results.return_value, true);
@@ -279,7 +278,6 @@
}
}
-
/**
* Helper cost function that multiplies the parameters by the given jacobians
* and adds a constant offset.
@@ -291,7 +289,8 @@
set_num_residuals(residuals_offset_.size());
}
- virtual bool Evaluate(double const* const* parameter_ptrs, double* residuals_ptr,
+ virtual bool Evaluate(double const* const* parameter_ptrs,
+ double* residuals_ptr,
double** residual_J_params) const {
CHECK_GE(residual_J_params_.size(), 0.0);
VectorRef residuals(residuals_ptr, residual_J_params_[0].rows());
@@ -308,8 +307,8 @@
// Return Jacobian.
if (residual_J_params != NULL && residual_J_params[i] != NULL) {
Eigen::Map<Matrix> residual_J_param_out(residual_J_params[i],
- residual_J_param.rows(),
- residual_J_param.cols());
+ residual_J_param.rows(),
+ residual_J_param.cols());
if (jacobian_offsets_.count(i) != 0) {
residual_J_param_out = residual_J_param + jacobian_offsets_.at(i);
} else {
@@ -414,8 +413,8 @@
Matrix residual_expected = residual_offset + j0 * param0 + j1 * param1;
- EXPECT_TRUE(j1_out == j0);
- EXPECT_TRUE(j2_out == j1);
+ ExpectMatricesClose(j1_out, j0, std::numeric_limits<double>::epsilon());
+ ExpectMatricesClose(j2_out, j1, std::numeric_limits<double>::epsilon());
ExpectMatricesClose(residual, residual_expected, kTolerance);
// Create local parameterization.
@@ -433,7 +432,9 @@
Eigen::Matrix<double, 3, 2, Eigen::RowMajor> global_J_local_out;
parameterization.ComputeJacobian(x.data(), global_J_local_out.data());
- EXPECT_TRUE(global_J_local_out == global_J_local);
+ ExpectMatricesClose(global_J_local_out,
+ global_J_local,
+ std::numeric_limits<double>::epsilon());
Eigen::Vector3d x_plus_delta;
parameterization.Plus(x.data(), delta.data(), x_plus_delta.data());
@@ -446,8 +447,8 @@
parameterizations[1] = NULL;
NumericDiffOptions numeric_diff_options;
GradientChecker::ProbeResults results;
- GradientChecker gradient_checker(&cost_function, ¶meterizations,
- numeric_diff_options);
+ GradientChecker gradient_checker(
+ &cost_function, ¶meterizations, numeric_diff_options);
Problem::Options problem_options;
problem_options.cost_function_ownership = DO_NOT_TAKE_OWNERSHIP;
@@ -457,8 +458,8 @@
Eigen::Vector2d param1_solver;
problem.AddParameterBlock(param0_solver.data(), 3, ¶meterization);
problem.AddParameterBlock(param1_solver.data(), 2);
- problem.AddResidualBlock(&cost_function, NULL, param0_solver.data(),
- param1_solver.data());
+ problem.AddResidualBlock(
+ &cost_function, NULL, param0_solver.data(), param1_solver.data());
Solver::Options solver_options;
solver_options.check_gradients = true;
solver_options.initial_trust_region_radius = 1e10;
@@ -468,20 +469,25 @@
// First test case: everything is correct.
EXPECT_TRUE(gradient_checker.Probe(parameters.data(), kTolerance, NULL));
EXPECT_TRUE(gradient_checker.Probe(parameters.data(), kTolerance, &results))
- << results.error_log;
+ << results.error_log;
// Check that results contain correct data.
ASSERT_EQ(results.return_value, true);
- ASSERT_TRUE(results.residuals == residual);
+ ExpectMatricesClose(
+ results.residuals, residual, std::numeric_limits<double>::epsilon());
CheckDimensions(results, parameter_sizes, local_parameter_sizes, 3);
- ExpectMatricesClose(results.local_jacobians.at(0), j0 * global_J_local,
- kTolerance);
- EXPECT_TRUE(results.local_jacobians.at(1) == j1);
- ExpectMatricesClose(results.local_numeric_jacobians.at(0),
- j0 * global_J_local, kTolerance);
+ ExpectMatricesClose(
+ results.local_jacobians.at(0), j0 * global_J_local, kTolerance);
+ ExpectMatricesClose(results.local_jacobians.at(1),
+ j1,
+ std::numeric_limits<double>::epsilon());
+ ExpectMatricesClose(
+ results.local_numeric_jacobians.at(0), j0 * global_J_local, kTolerance);
ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance);
- EXPECT_TRUE(results.jacobians.at(0) == j0);
- EXPECT_TRUE(results.jacobians.at(1) == j1);
+ ExpectMatricesClose(
+ results.jacobians.at(0), j0, std::numeric_limits<double>::epsilon());
+ ExpectMatricesClose(
+ results.jacobians.at(1), j1, std::numeric_limits<double>::epsilon());
ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance);
ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance);
EXPECT_GE(results.maximum_relative_error, 0.0);
@@ -502,22 +508,27 @@
cost_function.SetJacobianOffset(0, j0_offset);
EXPECT_FALSE(gradient_checker.Probe(parameters.data(), kTolerance, NULL));
EXPECT_FALSE(gradient_checker.Probe(parameters.data(), kTolerance, &results))
- << results.error_log;
+ << results.error_log;
// Check that results contain correct data.
ASSERT_EQ(results.return_value, true);
- ASSERT_TRUE(results.residuals == residual);
+ ExpectMatricesClose(
+ results.residuals, residual, std::numeric_limits<double>::epsilon());
CheckDimensions(results, parameter_sizes, local_parameter_sizes, 3);
ASSERT_EQ(results.local_jacobians.size(), 2);
ASSERT_EQ(results.local_numeric_jacobians.size(), 2);
ExpectMatricesClose(results.local_jacobians.at(0),
- (j0 + j0_offset) * global_J_local, kTolerance);
- EXPECT_TRUE(results.local_jacobians.at(1) == j1);
- ExpectMatricesClose(results.local_numeric_jacobians.at(0),
- j0 * global_J_local, kTolerance);
+ (j0 + j0_offset) * global_J_local,
+ kTolerance);
+ ExpectMatricesClose(results.local_jacobians.at(1),
+ j1,
+ std::numeric_limits<double>::epsilon());
+ ExpectMatricesClose(
+ results.local_numeric_jacobians.at(0), j0 * global_J_local, kTolerance);
ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance);
ExpectMatricesClose(results.jacobians.at(0), j0 + j0_offset, kTolerance);
- EXPECT_TRUE(results.jacobians.at(1) == j1);
+ ExpectMatricesClose(
+ results.jacobians.at(1), j1, std::numeric_limits<double>::epsilon());
ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance);
ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance);
EXPECT_GT(results.maximum_relative_error, 0.0);
@@ -536,23 +547,28 @@
// Verify that the gradient checker does not treat this as an error.
EXPECT_TRUE(gradient_checker.Probe(parameters.data(), kTolerance, &results))
- << results.error_log;
+ << results.error_log;
// Check that results contain correct data.
ASSERT_EQ(results.return_value, true);
- ASSERT_TRUE(results.residuals == residual);
+ ExpectMatricesClose(
+ results.residuals, residual, std::numeric_limits<double>::epsilon());
CheckDimensions(results, parameter_sizes, local_parameter_sizes, 3);
ASSERT_EQ(results.local_jacobians.size(), 2);
ASSERT_EQ(results.local_numeric_jacobians.size(), 2);
ExpectMatricesClose(results.local_jacobians.at(0),
(j0 + j0_offset) * parameterization.global_J_local,
kTolerance);
- EXPECT_TRUE(results.local_jacobians.at(1) == j1);
+ ExpectMatricesClose(results.local_jacobians.at(1),
+ j1,
+ std::numeric_limits<double>::epsilon());
ExpectMatricesClose(results.local_numeric_jacobians.at(0),
- j0 * parameterization.global_J_local, kTolerance);
+ j0 * parameterization.global_J_local,
+ kTolerance);
ExpectMatricesClose(results.local_numeric_jacobians.at(1), j1, kTolerance);
ExpectMatricesClose(results.jacobians.at(0), j0 + j0_offset, kTolerance);
- EXPECT_TRUE(results.jacobians.at(1) == j1);
+ ExpectMatricesClose(
+ results.jacobians.at(1), j1, std::numeric_limits<double>::epsilon());
ExpectMatricesClose(results.numeric_jacobians.at(0), j0, kTolerance);
ExpectMatricesClose(results.numeric_jacobians.at(1), j1, kTolerance);
EXPECT_GE(results.maximum_relative_error, 0.0);
