mirror of
https://github.com/ceres-solver/ceres-solver.git
synced 2026-08-29 08:34:37 +08:00
Sameer Agarwal
5a30cae583
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
199 lines
7.5 KiB
C++
199 lines
7.5 KiB
C++
// Ceres Solver - A fast non-linear least squares minimizer
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// Copyright 2023 Google Inc. All rights reserved.
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// http://ceres-solver.org/
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//
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// Redistribution and use in source and binary forms, with or without
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// modification, are permitted provided that the following conditions are met:
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//
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// * Redistributions of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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// * Redistributions in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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// * Neither the name of Google Inc. nor the names of its contributors may be
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// used to endorse or promote products derived from this software without
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// specific prior written permission.
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//
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// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
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// ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE
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// LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
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// CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
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// SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
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// INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
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// CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
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// ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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// POSSIBILITY OF SUCH DAMAGE.
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//
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// Author: joydeepb@cs.utexas.edu (Joydeep Biswas)
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#include "ceres/cuda_kernels_vector_ops.h"
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#include <math.h>
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#include <limits>
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#include <string>
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#include <vector>
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#include "ceres/context_impl.h"
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#include "ceres/cuda_buffer.h"
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#include "ceres/internal/config.h"
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#include "ceres/internal/eigen.h"
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#include "glog/logging.h"
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#include "gtest/gtest.h"
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namespace ceres {
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namespace internal {
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#ifndef CERES_NO_CUDA
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TEST(CudaFP64ToFP32, SimpleConversions) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<double> fp64_cpu = {1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0};
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CudaBuffer<double> fp64_gpu(&context);
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fp64_gpu.CopyFromCpuVector(fp64_cpu);
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CudaBuffer<float> fp32_gpu(&context);
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fp32_gpu.Reserve(fp64_cpu.size());
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CudaFP64ToFP32(fp64_gpu.data(),
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fp32_gpu.data(),
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fp64_cpu.size(),
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context.DefaultStream());
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std::vector<float> fp32_cpu(fp64_cpu.size());
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fp32_gpu.CopyToCpu(fp32_cpu.data(), fp32_cpu.size());
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for (int i = 0; i < fp32_cpu.size(); ++i) {
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EXPECT_EQ(fp32_cpu[i], static_cast<float>(fp64_cpu[i]));
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}
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}
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TEST(CudaFP64ToFP32, NumericallyExtremeValues) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<double> fp64_cpu = {
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DBL_MIN, 10.0 * DBL_MIN, DBL_MAX, 0.1 * DBL_MAX};
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// First just make sure that the compiler has represented these values
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// accurately as fp64.
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EXPECT_GT(fp64_cpu[0], 0.0);
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EXPECT_GT(fp64_cpu[1], 0.0);
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EXPECT_TRUE(std::isfinite(fp64_cpu[2]));
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EXPECT_TRUE(std::isfinite(fp64_cpu[3]));
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CudaBuffer<double> fp64_gpu(&context);
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fp64_gpu.CopyFromCpuVector(fp64_cpu);
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CudaBuffer<float> fp32_gpu(&context);
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fp32_gpu.Reserve(fp64_cpu.size());
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CudaFP64ToFP32(fp64_gpu.data(),
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fp32_gpu.data(),
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fp64_cpu.size(),
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context.DefaultStream());
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std::vector<float> fp32_cpu(fp64_cpu.size());
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fp32_gpu.CopyToCpu(fp32_cpu.data(), fp32_cpu.size());
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EXPECT_EQ(fp32_cpu[0], 0.0f);
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EXPECT_EQ(fp32_cpu[1], 0.0f);
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EXPECT_EQ(fp32_cpu[2], std::numeric_limits<float>::infinity());
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EXPECT_EQ(fp32_cpu[3], std::numeric_limits<float>::infinity());
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}
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TEST(CudaFP32ToFP64, SimpleConversions) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<float> fp32_cpu = {1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0};
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CudaBuffer<float> fp32_gpu(&context);
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fp32_gpu.CopyFromCpuVector(fp32_cpu);
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CudaBuffer<double> fp64_gpu(&context);
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fp64_gpu.Reserve(fp32_cpu.size());
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CudaFP32ToFP64(fp32_gpu.data(),
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fp64_gpu.data(),
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fp32_cpu.size(),
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context.DefaultStream());
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std::vector<double> fp64_cpu(fp32_cpu.size());
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fp64_gpu.CopyToCpu(fp64_cpu.data(), fp64_cpu.size());
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for (int i = 0; i < fp64_cpu.size(); ++i) {
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EXPECT_EQ(fp64_cpu[i], static_cast<double>(fp32_cpu[i]));
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}
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}
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TEST(CudaSetZeroFP32, NonZeroInput) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<float> fp32_cpu = {1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0};
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CudaBuffer<float> fp32_gpu(&context);
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fp32_gpu.CopyFromCpuVector(fp32_cpu);
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CudaSetZeroFP32(fp32_gpu.data(), fp32_cpu.size(), context.DefaultStream());
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std::vector<float> fp32_cpu_zero(fp32_cpu.size());
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fp32_gpu.CopyToCpu(fp32_cpu_zero.data(), fp32_cpu_zero.size());
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for (int i = 0; i < fp32_cpu_zero.size(); ++i) {
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EXPECT_EQ(fp32_cpu_zero[i], 0.0f);
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}
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}
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TEST(CudaSetZeroFP64, NonZeroInput) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<double> fp64_cpu = {1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0};
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CudaBuffer<double> fp64_gpu(&context);
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fp64_gpu.CopyFromCpuVector(fp64_cpu);
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CudaSetZeroFP64(fp64_gpu.data(), fp64_cpu.size(), context.DefaultStream());
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std::vector<double> fp64_cpu_zero(fp64_cpu.size());
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fp64_gpu.CopyToCpu(fp64_cpu_zero.data(), fp64_cpu_zero.size());
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for (int i = 0; i < fp64_cpu_zero.size(); ++i) {
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EXPECT_EQ(fp64_cpu_zero[i], 0.0);
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}
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}
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TEST(CudaDsxpy, DoubleValues) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<float> fp32_cpu_a = {1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0};
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std::vector<double> fp64_cpu_b = {
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1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0};
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CudaBuffer<float> fp32_gpu_a(&context);
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fp32_gpu_a.CopyFromCpuVector(fp32_cpu_a);
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CudaBuffer<double> fp64_gpu_b(&context);
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fp64_gpu_b.CopyFromCpuVector(fp64_cpu_b);
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CudaDsxpy(fp64_gpu_b.data(),
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fp32_gpu_a.data(),
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fp32_gpu_a.size(),
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context.DefaultStream());
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fp64_gpu_b.CopyToCpu(fp64_cpu_b.data(), fp64_cpu_b.size());
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for (int i = 0; i < fp64_cpu_b.size(); ++i) {
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EXPECT_DOUBLE_EQ(fp64_cpu_b[i], 2.0 * fp32_cpu_a[i]);
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}
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}
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TEST(CudaDtDxpy, ComputeFourItems) {
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ContextImpl context;
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std::string cuda_error;
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EXPECT_TRUE(context.InitCuda(&cuda_error)) << cuda_error;
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std::vector<double> x_cpu = {1, 2, 3, 4};
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std::vector<double> y_cpu = {4, 3, 2, 1};
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std::vector<double> d_cpu = {10, 20, 30, 40};
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CudaBuffer<double> x_gpu(&context);
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x_gpu.CopyFromCpuVector(x_cpu);
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CudaBuffer<double> y_gpu(&context);
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y_gpu.CopyFromCpuVector(y_cpu);
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CudaBuffer<double> d_gpu(&context);
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d_gpu.CopyFromCpuVector(d_cpu);
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CudaDtDxpy(y_gpu.data(),
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d_gpu.data(),
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x_gpu.data(),
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y_gpu.size(),
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context.DefaultStream());
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y_gpu.CopyToCpu(y_cpu.data(), y_cpu.size());
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EXPECT_DOUBLE_EQ(y_cpu[0], 4.0 + 10.0 * 10.0 * 1.0);
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EXPECT_DOUBLE_EQ(y_cpu[1], 3.0 + 20.0 * 20.0 * 2.0);
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EXPECT_DOUBLE_EQ(y_cpu[2], 2.0 + 30.0 * 30.0 * 3.0);
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EXPECT_DOUBLE_EQ(y_cpu[3], 1.0 + 40.0 * 40.0 * 4.0);
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}
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#endif // CERES_NO_CUDA
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} // namespace internal
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} // namespace ceres
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