303 lines
11 KiB
C++
303 lines
11 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
|
|
// Copyright 2015 Google Inc. All rights reserved.
|
|
// http://ceres-solver.org/
|
|
//
|
|
// 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/small_blas.h"
|
|
|
|
#include <limits>
|
|
#include "gtest/gtest.h"
|
|
#include "ceres/internal/eigen.h"
|
|
|
|
namespace ceres {
|
|
namespace internal {
|
|
|
|
const double kTolerance = 3.0 * std::numeric_limits<double>::epsilon();
|
|
|
|
TEST(BLAS, MatrixMatrixMultiply) {
|
|
const int kRowA = 3;
|
|
const int kColA = 5;
|
|
Matrix A(kRowA, kColA);
|
|
A.setOnes();
|
|
|
|
const int kRowB = 5;
|
|
const int kColB = 7;
|
|
Matrix B(kRowB, kColB);
|
|
B.setOnes();
|
|
|
|
for (int row_stride_c = kRowA; row_stride_c < 3 * kRowA; ++row_stride_c) {
|
|
for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
|
|
Matrix C(row_stride_c, col_stride_c);
|
|
C.setOnes();
|
|
|
|
Matrix C_plus = C;
|
|
Matrix C_minus = C;
|
|
Matrix C_assign = C;
|
|
|
|
Matrix C_plus_ref = C;
|
|
Matrix C_minus_ref = C;
|
|
Matrix C_assign_ref = C;
|
|
for (int start_row_c = 0; start_row_c + kRowA < row_stride_c; ++start_row_c) {
|
|
for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
|
|
C_plus_ref.block(start_row_c, start_col_c, kRowA, kColB) +=
|
|
A * B;
|
|
|
|
MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(
|
|
A.data(), kRowA, kColA,
|
|
B.data(), kRowB, kColB,
|
|
C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
|
|
|
|
EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
|
|
<< "C += A * B \n"
|
|
<< "row_stride_c : " << row_stride_c << "\n"
|
|
<< "col_stride_c : " << col_stride_c << "\n"
|
|
<< "start_row_c : " << start_row_c << "\n"
|
|
<< "start_col_c : " << start_col_c << "\n"
|
|
<< "Cref : \n" << C_plus_ref << "\n"
|
|
<< "C: \n" << C_plus;
|
|
|
|
|
|
C_minus_ref.block(start_row_c, start_col_c, kRowA, kColB) -=
|
|
A * B;
|
|
|
|
MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(
|
|
A.data(), kRowA, kColA,
|
|
B.data(), kRowB, kColB,
|
|
C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
|
|
|
|
EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
|
|
<< "C -= A * B \n"
|
|
<< "row_stride_c : " << row_stride_c << "\n"
|
|
<< "col_stride_c : " << col_stride_c << "\n"
|
|
<< "start_row_c : " << start_row_c << "\n"
|
|
<< "start_col_c : " << start_col_c << "\n"
|
|
<< "Cref : \n" << C_minus_ref << "\n"
|
|
<< "C: \n" << C_minus;
|
|
|
|
C_assign_ref.block(start_row_c, start_col_c, kRowA, kColB) =
|
|
A * B;
|
|
|
|
MatrixMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(
|
|
A.data(), kRowA, kColA,
|
|
B.data(), kRowB, kColB,
|
|
C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
|
|
|
|
EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
|
|
<< "C = A * B \n"
|
|
<< "row_stride_c : " << row_stride_c << "\n"
|
|
<< "col_stride_c : " << col_stride_c << "\n"
|
|
<< "start_row_c : " << start_row_c << "\n"
|
|
<< "start_col_c : " << start_col_c << "\n"
|
|
<< "Cref : \n" << C_assign_ref << "\n"
|
|
<< "C: \n" << C_assign;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST(BLAS, MatrixTransposeMatrixMultiply) {
|
|
const int kRowA = 5;
|
|
const int kColA = 3;
|
|
Matrix A(kRowA, kColA);
|
|
A.setOnes();
|
|
|
|
const int kRowB = 5;
|
|
const int kColB = 7;
|
|
Matrix B(kRowB, kColB);
|
|
B.setOnes();
|
|
|
|
for (int row_stride_c = kColA; row_stride_c < 3 * kColA; ++row_stride_c) {
|
|
for (int col_stride_c = kColB; col_stride_c < 3 * kColB; ++col_stride_c) {
|
|
Matrix C(row_stride_c, col_stride_c);
|
|
C.setOnes();
|
|
|
|
Matrix C_plus = C;
|
|
Matrix C_minus = C;
|
|
Matrix C_assign = C;
|
|
|
|
Matrix C_plus_ref = C;
|
|
Matrix C_minus_ref = C;
|
|
Matrix C_assign_ref = C;
|
|
for (int start_row_c = 0; start_row_c + kColA < row_stride_c; ++start_row_c) {
|
|
for (int start_col_c = 0; start_col_c + kColB < col_stride_c; ++start_col_c) {
|
|
C_plus_ref.block(start_row_c, start_col_c, kColA, kColB) +=
|
|
A.transpose() * B;
|
|
|
|
MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 1>(
|
|
A.data(), kRowA, kColA,
|
|
B.data(), kRowB, kColB,
|
|
C_plus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
|
|
|
|
EXPECT_NEAR((C_plus_ref - C_plus).norm(), 0.0, kTolerance)
|
|
<< "C += A' * B \n"
|
|
<< "row_stride_c : " << row_stride_c << "\n"
|
|
<< "col_stride_c : " << col_stride_c << "\n"
|
|
<< "start_row_c : " << start_row_c << "\n"
|
|
<< "start_col_c : " << start_col_c << "\n"
|
|
<< "Cref : \n" << C_plus_ref << "\n"
|
|
<< "C: \n" << C_plus;
|
|
|
|
C_minus_ref.block(start_row_c, start_col_c, kColA, kColB) -=
|
|
A.transpose() * B;
|
|
|
|
MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, -1>(
|
|
A.data(), kRowA, kColA,
|
|
B.data(), kRowB, kColB,
|
|
C_minus.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
|
|
|
|
EXPECT_NEAR((C_minus_ref - C_minus).norm(), 0.0, kTolerance)
|
|
<< "C -= A' * B \n"
|
|
<< "row_stride_c : " << row_stride_c << "\n"
|
|
<< "col_stride_c : " << col_stride_c << "\n"
|
|
<< "start_row_c : " << start_row_c << "\n"
|
|
<< "start_col_c : " << start_col_c << "\n"
|
|
<< "Cref : \n" << C_minus_ref << "\n"
|
|
<< "C: \n" << C_minus;
|
|
|
|
C_assign_ref.block(start_row_c, start_col_c, kColA, kColB) =
|
|
A.transpose() * B;
|
|
|
|
MatrixTransposeMatrixMultiply<kRowA, kColA, kRowB, kColB, 0>(
|
|
A.data(), kRowA, kColA,
|
|
B.data(), kRowB, kColB,
|
|
C_assign.data(), start_row_c, start_col_c, row_stride_c, col_stride_c);
|
|
|
|
EXPECT_NEAR((C_assign_ref - C_assign).norm(), 0.0, kTolerance)
|
|
<< "C = A' * B \n"
|
|
<< "row_stride_c : " << row_stride_c << "\n"
|
|
<< "col_stride_c : " << col_stride_c << "\n"
|
|
<< "start_row_c : " << start_row_c << "\n"
|
|
<< "start_col_c : " << start_col_c << "\n"
|
|
<< "Cref : \n" << C_assign_ref << "\n"
|
|
<< "C: \n" << C_assign;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
TEST(BLAS, MatrixVectorMultiply) {
|
|
const int kRowA = 5;
|
|
const int kColA = 3;
|
|
Matrix A(kRowA, kColA);
|
|
A.setOnes();
|
|
|
|
Vector b(kColA);
|
|
b.setOnes();
|
|
|
|
Vector c(kRowA);
|
|
c.setOnes();
|
|
|
|
Vector c_plus = c;
|
|
Vector c_minus = c;
|
|
Vector c_assign = c;
|
|
|
|
Vector c_plus_ref = c;
|
|
Vector c_minus_ref = c;
|
|
Vector c_assign_ref = c;
|
|
|
|
c_plus_ref += A * b;
|
|
MatrixVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
|
|
b.data(),
|
|
c_plus.data());
|
|
EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
|
|
<< "c += A * b \n"
|
|
<< "c_ref : \n" << c_plus_ref << "\n"
|
|
<< "c: \n" << c_plus;
|
|
|
|
c_minus_ref -= A * b;
|
|
MatrixVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
|
|
b.data(),
|
|
c_minus.data());
|
|
EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
|
|
<< "c += A * b \n"
|
|
<< "c_ref : \n" << c_minus_ref << "\n"
|
|
<< "c: \n" << c_minus;
|
|
|
|
c_assign_ref = A * b;
|
|
MatrixVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
|
|
b.data(),
|
|
c_assign.data());
|
|
EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
|
|
<< "c += A * b \n"
|
|
<< "c_ref : \n" << c_assign_ref << "\n"
|
|
<< "c: \n" << c_assign;
|
|
}
|
|
|
|
TEST(BLAS, MatrixTransposeVectorMultiply) {
|
|
const int kRowA = 5;
|
|
const int kColA = 3;
|
|
Matrix A(kRowA, kColA);
|
|
A.setRandom();
|
|
|
|
Vector b(kRowA);
|
|
b.setRandom();
|
|
|
|
Vector c(kColA);
|
|
c.setOnes();
|
|
|
|
Vector c_plus = c;
|
|
Vector c_minus = c;
|
|
Vector c_assign = c;
|
|
|
|
Vector c_plus_ref = c;
|
|
Vector c_minus_ref = c;
|
|
Vector c_assign_ref = c;
|
|
|
|
c_plus_ref += A.transpose() * b;
|
|
MatrixTransposeVectorMultiply<kRowA, kColA, 1>(A.data(), kRowA, kColA,
|
|
b.data(),
|
|
c_plus.data());
|
|
EXPECT_NEAR((c_plus_ref - c_plus).norm(), 0.0, kTolerance)
|
|
<< "c += A' * b \n"
|
|
<< "c_ref : \n" << c_plus_ref << "\n"
|
|
<< "c: \n" << c_plus;
|
|
|
|
c_minus_ref -= A.transpose() * b;
|
|
MatrixTransposeVectorMultiply<kRowA, kColA, -1>(A.data(), kRowA, kColA,
|
|
b.data(),
|
|
c_minus.data());
|
|
EXPECT_NEAR((c_minus_ref - c_minus).norm(), 0.0, kTolerance)
|
|
<< "c += A' * b \n"
|
|
<< "c_ref : \n" << c_minus_ref << "\n"
|
|
<< "c: \n" << c_minus;
|
|
|
|
c_assign_ref = A.transpose() * b;
|
|
MatrixTransposeVectorMultiply<kRowA, kColA, 0>(A.data(), kRowA, kColA,
|
|
b.data(),
|
|
c_assign.data());
|
|
EXPECT_NEAR((c_assign_ref - c_assign).norm(), 0.0, kTolerance)
|
|
<< "c += A' * b \n"
|
|
<< "c_ref : \n" << c_assign_ref << "\n"
|
|
<< "c: \n" << c_assign;
|
|
}
|
|
|
|
} // namespace internal
|
|
} // namespace ceres
|