- Add methods to aceess the cached residuals and jacobian computed in
the optimization process in TinySolver. Usage of such methods will
retrieve the corresponding values associated with the converged
parameter.
- Reorder the Update() call to ensure that the jacobian/residuals
associated with the converged parameter are computed and cached.
Change-Id: If82e19d67d28b057833357f2c9a75b2d0fd139af
Previously these classes in analogy with ceres::Problem's interface
had interfaces to allow bare pointers as well as unique_ptrs. This
CL changes the API to always use unique_ptr, this is less error prone
and makes the default ownership semantics clearer.
Change-Id: I7577a90761f341c7e009c248c820f0fec2e6f32d
Starting with SuiteSparse version 7.4.0 CHOLMOD has support for single
precision matrices. This allows us to have single precision and mixed
precision solves when using the SUITE_SPARSE backend.
This CL also fixes sparse_cholesky_test which was completely broken for
single precision testing.
Sample performance on my Mac.
/usr/bin/time -l ./bin/bundle_adjuster --input=../../Downloads/problem-3068-310854-pre.txt
<SNIP>
Cost:
Initial 9.099334e+07
Final 4.161838e+06
Change 8.683150e+07
Minimizer iterations 6
Successful steps 4
Unsuccessful steps 2
Time (in seconds):
Preprocessor 2.528222
Residual only evaluation 0.142804 (5)
Jacobian & residual evaluation 0.424014 (4)
Linear solver 54.083396 (5)
Minimizer 54.895752
Postprocessor 0.024564
Total 57.448539
Termination: NO_CONVERGENCE (Maximum number of iterations reached. Number of iterations: 5.)
59.04 real 341.24 user 5.49 sys
5776375808 maximum resident set size
<SNIP>
616329634071 instructions retired
929475980510 cycles elapsed
5375034560 peak memory footprint
/usr/bin/time -l ./bin/bundle_adjuster --input=../../Downloads/problem-3068-310854-pre.txt -mixed_precision_solves
<SNIP>
Cost:
Initial 9.099334e+07
Final 4.148930e+06
Change 8.684441e+07
Minimizer iterations 6
Successful steps 4
Unsuccessful steps 2
Time (in seconds):
Preprocessor 2.580217
Residual only evaluation 0.144098 (5)
Jacobian & residual evaluation 0.396723 (4)
Linear solver 23.636074 (5)
Minimizer 24.427163
Postprocessor 0.023790
Total 27.031170
Termination: NO_CONVERGENCE (Maximum number of iterations reached. Number of iterations: 5.)
28.58 real 128.53 user 2.37 sys
4818386944 maximum resident set size
<SNIP>
395186936091 instructions retired
368802808856 cycles elapsed
4327029824 peak memory footprint
Change-Id: I1f137b0dd12da8da7f9ced338dd8f20f4bbdf99d
The dependencies and the API have changed enough that
it is worth incrementing the version number.
Change-Id: I4e2911a91988d97f47320c56072ad1546e20030b
Replace ceres::String* with their more modern and performant
absl strings library equivalent and delete our string
manipulation library.
Change-Id: Iecbdba9864e0abf329778f81fdc0708f78f7594f
Ceres Solver was using an old forked version of FixedArray,
now that we are using absl, we can use the official version
that ships with it.
Change-Id: Ic88d7f6e8a49b928d611f7cbb04172452b322b01
1. Add abseil-cpp as a submodule. We are tracking the latest LTS
release, which is lts_2024_01_16.
2. Replace glog/gflags with absl::log and absl::flags.
3. Remove miniglog
4. Also take a whack at making the bazel build work with
abseil-cpp and gtest.
There are a number of TODOs in this CL that still need to be resolved.
Change-Id: I39355ed7d61375be4ebcbc8596d9cc70acc1c678
cuDSS could be used as an alternative for SuiteSparse and EigenSparse
in case if CUDA capable GPU is available.
Change-Id: I7a567093ce91363478118153e181134ed5804573
If arguments are passed to a cost function that can be used to construct
the functor, the latter will be instantiated by the cost function using
std::make_unique to ensure exception safety. This not only avoids static
analysis warnings caused by calling new but also spelling the cost
functor type name multiple times.
Also expand deduction guides for instantiating
Dynamic(Auto|Numeric)DiffCostFunction from std::unique_ptr enabled
constructor overloads.
Finally, make CostFunction default move constructible and assignable but
only through derived classes. This in turn allows derived classes to be
movable without relying on custom implementations of corresponding
operators.
Change-Id: Idee8b9871d862bc9f9f8b5a8d0bedc52863e93c0
Move Bessel functions availability checks from configuration time to
inclusion time to be more robust and allow the use of ABI compatible
compilers (e.g., Ceres is compiled using Clang but is used in a project
compiled using GCC.)
Since libc++ does not yet implement special math functions, we fallback
to their POSIX implementation if available. However, then only the
deprecated BesselJ{0,1,n} are provided.
Fixes#814
Change-Id: Ic3e62452b36e90cb22644cc8e553e3dd1881193f
This eliminates an entire vector and computation of a square root
followed by a squaring.
Thanks to @rlabbe for pointing this out.
Change-Id: I0de117b31b9332c61e687f18466d7cb2e2ac611e
Previously, MSVC warning C4996 was suppressed unconditionally in the
entire code base which made it difficult identifying and fixing specific
problems, particularly those in the public interface.
Prefer now to disable warnings at the specific location they occur. This
approach, however, reveals an inconsistency in how Ceres handles POSIX
functions which are declared deprecated by MSVC. Specifically, Bessel
functions use the underscore form whereas the read function does not. To
simplify the logic, we revert to POSIX compatible functions.
C++23 also deprecates std::numeric_limits<T>::has_denorm which MSVC
warns about. Here, we disable the deprecation warning locally to avoid
the warning leaking into the user code.
Fixes#1013
Change-Id: Ida8457cc8dd8770b4384a7c49d16f213b02cdec4
1. Add a version history
2. Update copyright years across the code base
3. Run format_all.sh
4. Update version strings from 2.1.0 to 2.2.0 in the docs and
elsewhere.
Change-Id: I46d8d479d54bd6002d532785e67342106e73c9ac
Converting fixed size vectors to dynamic ones allows to avoid
segmentation faults in Eigen's packet math if the corresponding
expressions are invoked within GMock matchers.
Fixes#996
Change-Id: I7da5599883825ab0e580678d3d55de19095b41b1
In https://ceres-solver-review.git.corp.google.com/c/ceres-solver/+/23802
the computation of the norm of a quaternion
scale = 1/sqrt(q[0] * q[0] + q[1] * q[1] + q[2] * q[2] + q[3] * q[3]);
was replaced by
scale = 1/hypot(q[0], q[1], hypot(q[2], q[3]));
while this appear to be a more accurate computation because of the
use of hypot which can handle over and underflow it introduces a
bug for the case where q[2] = q[3] = 0.
While the hypot(q[2], q[3]) == 0 as scalars, if q[2] and q[3] are
jets, then the derivative will be NaN. Which means that even though
q[0] or q[1] is non-zero and the norm of the quaternion is non-zero,
and the resulting derivative is finite, this way of computing the
scale will produce nans in the derivative of scale.
The following quaternion will replicate the problem described above.
using Jet = ceres::Jet<double, 4>;
std::array<Jet, 4> quaternion = {Jet(1.0, 0), Jet(0.0, 1), Jet(0.0, 2), Jet(0.0, 3)};
This CL reverts the change to QuaternionRotatePoint and
adds a test for it.
Thanks to Jonathan Taylor for reproducing this bug.
Change-Id: I0fbbcc77d6945a38563d82efba4429f4b5278cd5
Benchmark for evaluation of residuals and evaluation of both residuals
and jacobian.
For each input file (in BAL format) specified on the command line two
sets of benchmarks are created for different number of threads.
BAL data is preloaded before starting benchmark.
Usage:
./bin/evaluation_benchmark [flags] input_1.txt ... input_N.txt
Change-Id: I543df65f483e3b186f52785b68bd5e2b3e3552a3
Conversions function include Euler Angles to / from Rotation Matrices
and Quaternions. They are generalized for any Euler convention that can
be specified in the arguments. Algorithm is from "Euler angle
conversion", Ken Shoemake, Graphics Gems IV
Change-Id: I7f9ddc0b8d686efca16299d2ba374295744376ce
* Added CudaCgnrSolver, a new CUDA-accelerated CGNR.
* To use CudaCgnrSolver, the user must select CGNR as the linear_solver
and CUDA_SPARSE as the sparse_linear_algebra_library.
* Updated ConjugateGradientSolver to work with an array of pointers to
scratch to support CudaVectors as scratch.
* Moved CUDA initialization to run in Solver::Solve as needed.
Some performance comparisons on an Ubuntu 20.04 desktop with an
Intel i9-9940X CPU @ 3.30GHz, and an nVidia Quadro RTX 6000,
all configurations run with 24 threads, and 10 iterations.
=================================================
CGNR + CUDA_SPARSE + IDENTITY Preconditioner
problem-1778-993923-pre.txt
=================================================
Cost:
Initial 2.563973e+08
Final 1.724755e+06
Change 2.546725e+08
Minimizer iterations 11
Successful steps 7
Unsuccessful steps 4
Time (in seconds):
Preprocessor 4.020158
Residual only evaluation 1.567092 (10)
Jacobian & residual evaluation 7.847130 (7)
Linear solver 31.688898 (10)
Minimizer 46.834987
Postprocessor 0.353974
Total 51.209120
=================================================
SPARSE_SCHUR (CPU) + SUITE_SPARSE + AMD
problem-1778-993923-pre.txt
=================================================
Cost:
Initial 2.563973e+08
Final 1.651617e+06
Change 2.547457e+08
Minimizer iterations 11
Successful steps 11
Unsuccessful steps 0
Time (in seconds):
Preprocessor 35.812003
Residual only evaluation 1.658980 (10)
Jacobian & residual evaluation 12.218799 (11)
Linear solver 76.409992 (10)
Minimizer 98.809773
Postprocessor 0.372712
Total 134.994489
=================================================
ITERATIVE_SCHUR (CPU) + JACOBI Preconditioner
problem-1778-993923-pre.txt
=================================================
Cost:
Initial 2.563973e+08
Final 1.684447e+06
Change 2.547128e+08
Minimizer iterations 11
Successful steps 8
Unsuccessful steps 3
Time (in seconds):
Preprocessor 15.331614
Residual only evaluation 1.606114 (10)
Jacobian & residual evaluation 8.502166 (8)
Linear solver 351.910080 (10)
Minimizer 368.797327
Postprocessor 0.363536
Total 384.492478
=================================================
CGNR + CUDA_SPARSE + IDENTITY Preconditioner
problem-13682-4456117-pre.txt
=================================================
Cost:
Initial 1.126372e+09
Final 2.269329e+07
Change 1.103678e+09
Minimizer iterations 11
Successful steps 7
Unsuccessful steps 4
Time (in seconds):
Preprocessor 19.140087
Residual only evaluation 8.721920 (10)
Jacobian & residual evaluation 41.955923 (7)
Linear solver 214.121861 (10)
Minimizer 296.636890
Postprocessor 1.971827
Total 317.748804
Change-Id: I3a09f31aa6903f661e91f595afd39d427583e856
Add an option to use schur power series expansion for initialization
of pcg solution in ITERATIVE_SCHUR linear solver.
Change-Id: Ifb8bce02bc5f5ceebc74f961eefd3f6dd2ffab4a
Implementation of "Power Bundle Adjustment for Large-Scale 3D
Reconstruction" by Weber et. al. added in the form of preconditioner.
Change-Id: Ie85526a5fc46f74256f6dfe9173c3571f7160f3a
This PR changes the Sphere and Line Manifold formulations so that their
tangent spaces represent traveled angles (for the sphere and the line
direction vector) and traveled distance (for the line origin). These
magnitudes were previously halved according to "Hartley & Zisserman
(2nd Edition)", but in the majority of the state of the art this is not
done, following the convention that magnitudes in the tangent space of
the unit sphere represent geodesic distances traveled on that manifold.
The same scale factor appears in the Quaternion Manifold implementation
and will be studied in a further PR.
This PR also adds an additional case in the Sphere Minus operator when
hy_norm == 0. The value of y_minus_x was fixed to 0 but actually its
last term can also be Pi depending on y_last.
Finally, new unit tests for the Plus and Minus operator are added, along
with new tests for the 2D Sphere (a.k.a. Circle) Manifold.
Change-Id: I9456f1675b20da49bede5d6759aabf3cdfb26eae
Since Eigen does not allow to have a RowMajor column vector (see
https://gitlab.com/libeigen/eigen/-/issues/416), the storage order
must be set to ColMajor in that case. This fix adds that special
case when generating 2D sphere manifolds.
Change-Id: I594932e0dafc878e0b348f72524478588e61b34d
Sergiu Deitsch