1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
|
// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2010, 2011, 2012 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: keir@google.com (Keir Mierle)
#include "ceres/solver_impl.h"
#include <iostream> // NOLINT
#include <numeric>
#include "ceres/evaluator.h"
#include "ceres/gradient_checking_cost_function.h"
#include "ceres/levenberg_marquardt.h"
#include "ceres/linear_solver.h"
#include "ceres/map_util.h"
#include "ceres/minimizer.h"
#include "ceres/parameter_block.h"
#include "ceres/problem_impl.h"
#include "ceres/program.h"
#include "ceres/residual_block.h"
#include "ceres/schur_ordering.h"
#include "ceres/stringprintf.h"
#include "ceres/iteration_callback.h"
#include "ceres/problem.h"
namespace ceres {
namespace internal {
namespace {
void EvaluateCostAndResiduals(ProblemImpl* problem_impl,
double* cost,
vector<double>* residuals) {
CHECK_NOTNULL(cost);
Program* program = CHECK_NOTNULL(problem_impl)->mutable_program();
if (residuals != NULL) {
residuals->resize(program->NumResiduals());
program->Evaluate(cost, &(*residuals)[0]);
} else {
program->Evaluate(cost, NULL);
}
}
// Callback for updating the user's parameter blocks. Updates are only
// done if the step is successful.
class StateUpdatingCallback : public IterationCallback {
public:
StateUpdatingCallback(Program* program, double* parameters)
: program_(program), parameters_(parameters) {}
CallbackReturnType operator()(const IterationSummary& summary) {
if (summary.step_is_successful) {
program_->StateVectorToParameterBlocks(parameters_);
program_->CopyParameterBlockStateToUserState();
}
return SOLVER_CONTINUE;
}
private:
Program* program_;
double* parameters_;
};
// Callback for logging the state of the minimizer to STDERR or STDOUT
// depending on the user's preferences and logging level.
class LoggingCallback : public IterationCallback {
public:
explicit LoggingCallback(bool log_to_stdout)
: log_to_stdout_(log_to_stdout) {}
~LoggingCallback() {}
CallbackReturnType operator()(const IterationSummary& summary) {
const char* kReportRowFormat =
"% 4d: f:% 8e d:% 3.2e g:% 3.2e h:% 3.2e "
"rho:% 3.2e mu:% 3.2e li:% 3d";
string output = StringPrintf(kReportRowFormat,
summary.iteration,
summary.cost,
summary.cost_change,
summary.gradient_max_norm,
summary.step_norm,
summary.relative_decrease,
summary.mu,
summary.linear_solver_iterations);
if (log_to_stdout_) {
cout << output << endl;
} else {
VLOG(1) << output;
}
return SOLVER_CONTINUE;
}
private:
const bool log_to_stdout_;
};
} // namespace
void SolverImpl::Minimize(const Solver::Options& options,
Program* program,
Evaluator* evaluator,
LinearSolver* linear_solver,
double* initial_parameters,
double* final_parameters,
Solver::Summary* summary) {
Minimizer::Options minimizer_options(options);
LoggingCallback logging_callback(options.minimizer_progress_to_stdout);
if (options.logging_type != SILENT) {
minimizer_options.callbacks.push_back(&logging_callback);
}
StateUpdatingCallback updating_callback(program, initial_parameters);
if (options.update_state_every_iteration) {
minimizer_options.callbacks.push_back(&updating_callback);
}
LevenbergMarquardt levenberg_marquardt;
time_t start_minimizer_time_seconds = time(NULL);
levenberg_marquardt.Minimize(minimizer_options,
evaluator,
linear_solver,
initial_parameters,
final_parameters,
summary);
summary->minimizer_time_in_seconds =
time(NULL) - start_minimizer_time_seconds;
}
void SolverImpl::Solve(const Solver::Options& original_options,
Problem* problem,
Solver::Summary* summary) {
Solver::Options options(original_options);
#ifndef CERES_USE_OPENMP
if (options.num_threads > 1) {
LOG(WARNING)
<< "OpenMP support is not compiled into this binary; "
<< "only options.num_threads=1 is supported. Switching"
<< "to single threaded mode.";
options.num_threads = 1;
}
if (options.num_linear_solver_threads > 1) {
LOG(WARNING)
<< "OpenMP support is not compiled into this binary; "
<< "only options.num_linear_solver_threads=1 is supported. Switching"
<< "to single threaded mode.";
options.num_linear_solver_threads = 1;
}
#endif
// Reset the summary object to its default values;
*CHECK_NOTNULL(summary) = Solver::Summary();
summary->linear_solver_type_given = options.linear_solver_type;
summary->num_eliminate_blocks_given = original_options.num_eliminate_blocks;
summary->num_threads_given = original_options.num_threads;
summary->num_linear_solver_threads_given =
original_options.num_linear_solver_threads;
summary->ordering_type = original_options.ordering_type;
ProblemImpl* problem_impl = CHECK_NOTNULL(problem)->problem_impl_.get();
summary->num_parameter_blocks = problem_impl->NumParameterBlocks();
summary->num_parameters = problem_impl->NumParameters();
summary->num_residual_blocks = problem_impl->NumResidualBlocks();
summary->num_residuals = problem_impl->NumResiduals();
summary->num_threads_used = options.num_threads;
// Evaluate the initial cost and residual vector (if needed). The
// initial cost needs to be computed on the original unpreprocessed
// problem, as it is used to determine the value of the "fixed" part
// of the objective function after the problem has undergone
// reduction. Also the initial residuals are in the order in which
// the user added the ResidualBlocks to the optimization problem.
EvaluateCostAndResiduals(problem_impl,
&summary->initial_cost,
options.return_initial_residuals
? &summary->initial_residuals
: NULL);
// If the user requests gradient checking, construct a new
// ProblemImpl by wrapping the CostFunctions of problem_impl inside
// GradientCheckingCostFunction and replacing problem_impl with
// gradient_checking_problem_impl.
scoped_ptr<ProblemImpl> gradient_checking_problem_impl;
if (options.check_gradients) {
VLOG(1) << "Checking Gradients";
gradient_checking_problem_impl.reset(
CreateGradientCheckingProblemImpl(
problem_impl,
options.numeric_derivative_relative_step_size,
options.gradient_check_relative_precision));
// From here on, problem_impl will point to the GradientChecking version.
problem_impl = gradient_checking_problem_impl.get();
}
// Create the three objects needed to minimize: the transformed program, the
// evaluator, and the linear solver.
scoped_ptr<Program> reduced_program(
CreateReducedProgram(&options, problem_impl, &summary->error));
if (reduced_program == NULL) {
return;
}
summary->num_parameter_blocks_reduced = reduced_program->NumParameterBlocks();
summary->num_parameters_reduced = reduced_program->NumParameters();
summary->num_residual_blocks_reduced = reduced_program->NumResidualBlocks();
summary->num_residuals_reduced = reduced_program->NumResiduals();
scoped_ptr<LinearSolver>
linear_solver(CreateLinearSolver(&options, &summary->error));
summary->linear_solver_type_used = options.linear_solver_type;
summary->preconditioner_type = options.preconditioner_type;
summary->num_eliminate_blocks_used = options.num_eliminate_blocks;
summary->num_linear_solver_threads_used = options.num_linear_solver_threads;
if (linear_solver == NULL) {
return;
}
if (!MaybeReorderResidualBlocks(options,
reduced_program.get(),
&summary->error)) {
return;
}
scoped_ptr<Evaluator> evaluator(
CreateEvaluator(options, reduced_program.get(), &summary->error));
if (evaluator == NULL) {
return;
}
// The optimizer works on contiguous parameter vectors; allocate some.
Vector initial_parameters(reduced_program->NumParameters());
Vector optimized_parameters(reduced_program->NumParameters());
// Collect the discontiguous parameters into a contiguous state vector.
reduced_program->ParameterBlocksToStateVector(&initial_parameters[0]);
// Run the optimization.
Minimize(options,
reduced_program.get(),
evaluator.get(),
linear_solver.get(),
initial_parameters.data(),
optimized_parameters.data(),
summary);
// If the user aborted mid-optimization or the optimization
// terminated because of a numerical failure, then return without
// updating user state.
if (summary->termination_type == USER_ABORT ||
summary->termination_type == NUMERICAL_FAILURE) {
return;
}
// Push the contiguous optimized parameters back to the user's parameters.
reduced_program->StateVectorToParameterBlocks(&optimized_parameters[0]);
reduced_program->CopyParameterBlockStateToUserState();
// Return the final cost and residuals for the original problem.
EvaluateCostAndResiduals(problem->problem_impl_.get(),
&summary->final_cost,
options.return_final_residuals
? &summary->final_residuals
: NULL);
// Stick a fork in it, we're done.
return;
}
// Strips varying parameters and residuals, maintaining order, and updating
// num_eliminate_blocks.
bool SolverImpl::RemoveFixedBlocksFromProgram(Program* program,
int* num_eliminate_blocks,
string* error) {
int original_num_eliminate_blocks = *num_eliminate_blocks;
vector<ParameterBlock*>* parameter_blocks =
program->mutable_parameter_blocks();
// Mark all the parameters as unused. Abuse the index member of the parameter
// blocks for the marking.
for (int i = 0; i < parameter_blocks->size(); ++i) {
(*parameter_blocks)[i]->set_index(-1);
}
// Filter out residual that have all-constant parameters, and mark all the
// parameter blocks that appear in residuals.
{
vector<ResidualBlock*>* residual_blocks =
program->mutable_residual_blocks();
int j = 0;
for (int i = 0; i < residual_blocks->size(); ++i) {
ResidualBlock* residual_block = (*residual_blocks)[i];
int num_parameter_blocks = residual_block->NumParameterBlocks();
// Determine if the residual block is fixed, and also mark varying
// parameters that appear in the residual block.
bool all_constant = true;
for (int k = 0; k < num_parameter_blocks; k++) {
ParameterBlock* parameter_block = residual_block->parameter_blocks()[k];
if (!parameter_block->IsConstant()) {
all_constant = false;
parameter_block->set_index(1);
}
}
if (!all_constant) {
(*residual_blocks)[j++] = (*residual_blocks)[i];
}
}
residual_blocks->resize(j);
}
// Filter out unused or fixed parameter blocks, and update
// num_eliminate_blocks as necessary.
{
vector<ParameterBlock*>* parameter_blocks =
program->mutable_parameter_blocks();
int j = 0;
for (int i = 0; i < parameter_blocks->size(); ++i) {
ParameterBlock* parameter_block = (*parameter_blocks)[i];
if (parameter_block->index() == 1) {
(*parameter_blocks)[j++] = parameter_block;
} else if (i < original_num_eliminate_blocks) {
(*num_eliminate_blocks)--;
}
}
parameter_blocks->resize(j);
}
CHECK(((program->NumResidualBlocks() == 0) &&
(program->NumParameterBlocks() == 0)) ||
((program->NumResidualBlocks() != 0) &&
(program->NumParameterBlocks() != 0)))
<< "Congratulations, you found a bug in Ceres. Please report it.";
return true;
}
Program* SolverImpl::CreateReducedProgram(Solver::Options* options,
ProblemImpl* problem_impl,
string* error) {
Program* original_program = problem_impl->mutable_program();
scoped_ptr<Program> transformed_program(new Program(*original_program));
if (options->ordering_type == USER &&
!ApplyUserOrdering(*problem_impl,
options->ordering,
transformed_program.get(),
error)) {
return NULL;
}
if (options->ordering_type == SCHUR && options->num_eliminate_blocks != 0) {
*error = "Can't specify SCHUR ordering and num_eliminate_blocks "
"at the same time; SCHUR ordering determines "
"num_eliminate_blocks automatically.";
return NULL;
}
if (options->ordering_type == SCHUR && options->ordering.size() != 0) {
*error = "Can't specify SCHUR ordering type and the ordering "
"vector at the same time; SCHUR ordering determines "
"a suitable parameter ordering automatically.";
return NULL;
}
int num_eliminate_blocks = options->num_eliminate_blocks;
if (!RemoveFixedBlocksFromProgram(transformed_program.get(),
&num_eliminate_blocks,
error)) {
return NULL;
}
if (transformed_program->NumParameterBlocks() == 0) {
LOG(WARNING) << "No varying parameter blocks to optimize; "
<< "bailing early.";
return transformed_program.release();
}
if (options->ordering_type == SCHUR) {
vector<ParameterBlock*> schur_ordering;
num_eliminate_blocks = ComputeSchurOrdering(*transformed_program,
&schur_ordering);
CHECK_EQ(schur_ordering.size(), transformed_program->NumParameterBlocks())
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
// Replace the transformed program's ordering with the schur ordering.
swap(*transformed_program->mutable_parameter_blocks(), schur_ordering);
}
options->num_eliminate_blocks = num_eliminate_blocks;
CHECK_GE(options->num_eliminate_blocks, 0)
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
// Since the transformed program is the "active" program, and it is mutated,
// update the parameter offsets and indices.
transformed_program->SetParameterOffsetsAndIndex();
return transformed_program.release();
}
LinearSolver* SolverImpl::CreateLinearSolver(Solver::Options* options,
string* error) {
#ifdef CERES_NO_SUITESPARSE
if (options->linear_solver_type == SPARSE_NORMAL_CHOLESKY) {
*error = "Can't use SPARSE_NORMAL_CHOLESKY because SuiteSparse was not "
"enabled when Ceres was built.";
return NULL;
}
#endif // CERES_NO_SUITESPARSE
if (options->linear_solver_max_num_iterations <= 0) {
*error = "Solver::Options::linear_solver_max_num_iterations is 0.";
return NULL;
}
if (options->linear_solver_min_num_iterations <= 0) {
*error = "Solver::Options::linear_solver_min_num_iterations is 0.";
return NULL;
}
if (options->linear_solver_min_num_iterations >
options->linear_solver_max_num_iterations) {
*error = "Solver::Options::linear_solver_min_num_iterations > "
"Solver::Options::linear_solver_max_num_iterations.";
return NULL;
}
LinearSolver::Options linear_solver_options;
linear_solver_options.constant_sparsity = true;
linear_solver_options.min_num_iterations =
options->linear_solver_min_num_iterations;
linear_solver_options.max_num_iterations =
options->linear_solver_max_num_iterations;
linear_solver_options.type = options->linear_solver_type;
linear_solver_options.preconditioner_type = options->preconditioner_type;
#ifdef CERES_NO_SUITESPARSE
if (linear_solver_options.preconditioner_type == SCHUR_JACOBI) {
*error = "SCHUR_JACOBI preconditioner not suppored. Please build Ceres "
"with SuiteSparse support";
return NULL;
}
if (linear_solver_options.preconditioner_type == CLUSTER_JACOBI) {
*error = "CLUSTER_JACOBI preconditioner not suppored. Please build Ceres "
"with SuiteSparse support";
return NULL;
}
if (linear_solver_options.preconditioner_type == CLUSTER_TRIDIAGONAL) {
*error = "CLUSTER_TRIDIAGONAL preconditioner not suppored. Please build "
"Ceres with SuiteSparse support";
return NULL;
}
#endif
linear_solver_options.num_threads = options->num_linear_solver_threads;
linear_solver_options.num_eliminate_blocks =
options->num_eliminate_blocks;
if ((linear_solver_options.num_eliminate_blocks == 0) &&
IsSchurType(linear_solver_options.type)) {
#ifndef CERES_NO_SUITESPARSE
LOG(INFO) << "No elimination block remaining "
<< "switching to SPARSE_NORMAL_CHOLESKY.";
linear_solver_options.type = SPARSE_NORMAL_CHOLESKY;
#else
LOG(INFO) << "No elimination block remaining switching to DENSE_QR.";
linear_solver_options.type = DENSE_QR;
#endif // CERES_NO_SUITESPARSE
}
#ifdef CERES_NO_SUITESPARSE
if (linear_solver_options.type == SPARSE_SCHUR) {
*error = "Can't use SPARSE_SCHUR because SuiteSparse was not "
"enabled when Ceres was built.";
return NULL;
}
#endif // CERES_NO_SUITESPARSE
// The matrix used for storing the dense Schur complement has a
// single lock guarding the whole matrix. Running the
// SchurComplementSolver with multiple threads leads to maximum
// contention and slowdown. If the problem is large enough to
// benefit from a multithreaded schur eliminator, you should be
// using a SPARSE_SCHUR solver anyways.
if ((linear_solver_options.num_threads > 1) &&
(linear_solver_options.type == DENSE_SCHUR)) {
LOG(WARNING) << "Warning: Solver::Options::num_linear_solver_threads = "
<< options->num_linear_solver_threads
<< " with DENSE_SCHUR will result in poor performance; "
<< "switching to single-threaded.";
linear_solver_options.num_threads = 1;
}
options->linear_solver_type = linear_solver_options.type;
options->num_linear_solver_threads = linear_solver_options.num_threads;
return LinearSolver::Create(linear_solver_options);
}
bool SolverImpl::ApplyUserOrdering(const ProblemImpl& problem_impl,
vector<double*>& ordering,
Program* program,
string* error) {
if (ordering.size() != program->NumParameterBlocks()) {
*error = StringPrintf("User specified ordering does not have the same "
"number of parameters as the problem. The problem"
"has %d blocks while the ordering has %ld blocks.",
program->NumParameterBlocks(),
ordering.size());
return false;
}
// Ensure that there are no duplicates in the user's ordering.
{
vector<double*> ordering_copy(ordering);
sort(ordering_copy.begin(), ordering_copy.end());
if (unique(ordering_copy.begin(), ordering_copy.end())
!= ordering_copy.end()) {
*error = "User specified ordering contains duplicates.";
return false;
}
}
vector<ParameterBlock*>* parameter_blocks =
program->mutable_parameter_blocks();
fill(parameter_blocks->begin(),
parameter_blocks->end(),
static_cast<ParameterBlock*>(NULL));
const ProblemImpl::ParameterMap& parameter_map = problem_impl.parameter_map();
for (int i = 0; i < ordering.size(); ++i) {
ProblemImpl::ParameterMap::const_iterator it =
parameter_map.find(ordering[i]);
if (it == parameter_map.end()) {
*error = StringPrintf("User specified ordering contains a pointer "
"to a double that is not a parameter block in the "
"problem. The invalid double is at position %d "
" in options.ordering.", i);
return false;
}
(*parameter_blocks)[i] = it->second;
}
return true;
}
// Find the minimum index of any parameter block to the given residual.
// Parameter blocks that have indices greater than num_eliminate_blocks are
// considered to have an index equal to num_eliminate_blocks.
int MinParameterBlock(const ResidualBlock* residual_block,
int num_eliminate_blocks) {
int min_parameter_block_position = num_eliminate_blocks;
for (int i = 0; i < residual_block->NumParameterBlocks(); ++i) {
ParameterBlock* parameter_block = residual_block->parameter_blocks()[i];
DCHECK_NE(parameter_block->index(), -1)
<< "Did you forget to call Program::SetParameterOffsetsAndIndex()?";
min_parameter_block_position = std::min(parameter_block->index(),
min_parameter_block_position);
}
return min_parameter_block_position;
}
// Reorder the residuals for program, if necessary, so that the residuals
// involving each E block occur together. This is a necessary condition for the
// Schur eliminator, which works on these "row blocks" in the jacobian.
bool SolverImpl::MaybeReorderResidualBlocks(const Solver::Options& options,
Program* program,
string* error) {
// Only Schur types require the lexicographic reordering.
if (!IsSchurType(options.linear_solver_type)) {
return true;
}
CHECK_NE(0, options.num_eliminate_blocks)
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
// Create a histogram of the number of residuals for each E block. There is an
// extra bucket at the end to catch all non-eliminated F blocks.
vector<int> residual_blocks_per_e_block(options.num_eliminate_blocks + 1);
vector<ResidualBlock*>* residual_blocks = program->mutable_residual_blocks();
vector<int> min_position_per_residual(residual_blocks->size());
for (int i = 0; i < residual_blocks->size(); ++i) {
ResidualBlock* residual_block = (*residual_blocks)[i];
int position = MinParameterBlock(residual_block,
options.num_eliminate_blocks);
min_position_per_residual[i] = position;
DCHECK_LE(position, options.num_eliminate_blocks);
residual_blocks_per_e_block[position]++;
}
// Run a cumulative sum on the histogram, to obtain offsets to the start of
// each histogram bucket (where each bucket is for the residuals for that
// E-block).
vector<int> offsets(options.num_eliminate_blocks + 1);
std::partial_sum(residual_blocks_per_e_block.begin(),
residual_blocks_per_e_block.end(),
offsets.begin());
CHECK_EQ(offsets.back(), residual_blocks->size())
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
CHECK(find(residual_blocks_per_e_block.begin(),
residual_blocks_per_e_block.end() - 1, 0) !=
residual_blocks_per_e_block.end())
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
// Fill in each bucket with the residual blocks for its corresponding E block.
// Each bucket is individually filled from the back of the bucket to the front
// of the bucket. The filling order among the buckets is dictated by the
// residual blocks. This loop uses the offsets as counters; subtracting one
// from each offset as a residual block is placed in the bucket. When the
// filling is finished, the offset pointerts should have shifted down one
// entry (this is verified below).
vector<ResidualBlock*> reordered_residual_blocks(
(*residual_blocks).size(), static_cast<ResidualBlock*>(NULL));
for (int i = 0; i < residual_blocks->size(); ++i) {
int bucket = min_position_per_residual[i];
// Decrement the cursor, which should now point at the next empty position.
offsets[bucket]--;
// Sanity.
CHECK(reordered_residual_blocks[offsets[bucket]] == NULL)
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
reordered_residual_blocks[offsets[bucket]] = (*residual_blocks)[i];
}
// Sanity check #1: The difference in bucket offsets should match the
// histogram sizes.
for (int i = 0; i < options.num_eliminate_blocks; ++i) {
CHECK_EQ(residual_blocks_per_e_block[i], offsets[i + 1] - offsets[i])
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
}
// Sanity check #2: No NULL's left behind.
for (int i = 0; i < reordered_residual_blocks.size(); ++i) {
CHECK(reordered_residual_blocks[i] != NULL)
<< "Congratulations, you found a Ceres bug! Please report this error "
<< "to the developers.";
}
// Now that the residuals are collected by E block, swap them in place.
swap(*program->mutable_residual_blocks(), reordered_residual_blocks);
return true;
}
Evaluator* SolverImpl::CreateEvaluator(const Solver::Options& options,
Program* program,
string* error) {
Evaluator::Options evaluator_options;
evaluator_options.linear_solver_type = options.linear_solver_type;
evaluator_options.num_eliminate_blocks = options.num_eliminate_blocks;
evaluator_options.num_threads = options.num_threads;
return Evaluator::Create(evaluator_options, program, error);
}
} // namespace internal
} // namespace ceres
|