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Diffstat (limited to 'internal/ceres/blas_test.cc')
| -rw-r--r-- | internal/ceres/blas_test.cc | 303 |
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diff --git a/internal/ceres/blas_test.cc b/internal/ceres/blas_test.cc deleted file mode 100644 index efa7e7b..0000000 --- a/internal/ceres/blas_test.cc +++ /dev/null @@ -1,303 +0,0 @@ -// Ceres Solver - A fast non-linear least squares minimizer -// Copyright 2013 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/blas.h" - -#include "gtest/gtest.h" -#include "ceres/internal/eigen.h" - -namespace ceres { -namespace internal { - -TEST(BLAS, MatrixMatrixMultiply) { - const double kTolerance = 1e-16; - 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 double kTolerance = 1e-16; - 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 double kTolerance = 1e-16; - 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 double kTolerance = 1e-16; - 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 |