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// Copyright (c) the JPEG XL Project Authors. All rights reserved.
//
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
#include <hwy/targets.h>
#include "benchmark/benchmark.h"
#include "lib/jxl/convolve.h"
#include "lib/jxl/gauss_blur.h"
#include "lib/jxl/image_ops.h"
namespace jxl {
namespace {
JXL_MAYBE_UNUSED ImageF Convolve(const ImageF& in,
const std::vector<float>& kernel) {
return ConvolveAndSample(in, kernel, 1);
}
void BM_GaussBlur1d(benchmark::State& state) {
// Uncomment to disable SIMD and force and scalar implementation
// hwy::DisableTargets(~HWY_SCALAR);
// Uncomment to run AVX2
// hwy::DisableTargets(HWY_AVX3);
const size_t length = state.range();
const double sigma = 7.0; // (from Butteraugli application)
ImageF in(length, 1);
const float expected = length;
FillImage(expected, &in);
ImageF temp(length, 1);
ImageF out(length, 1);
const auto rg = CreateRecursiveGaussian(sigma);
for (auto _ : state) {
FastGaussian1D(rg, in.Row(0), length, out.Row(0));
// Prevent optimizing out
JXL_ASSERT(std::abs(out.ConstRow(0)[length / 2] - expected) / expected <
9E-5);
}
state.SetItemsProcessed(length * state.iterations());
}
void BM_GaussBlur2d(benchmark::State& state) {
// See GaussBlur1d for SIMD changes.
const size_t xsize = state.range();
const size_t ysize = xsize;
const double sigma = 7.0; // (from Butteraugli application)
ImageF in(xsize, ysize);
const float expected = xsize + ysize;
FillImage(expected, &in);
ImageF temp(xsize, ysize);
ImageF out(xsize, ysize);
ThreadPool* null_pool = nullptr;
const auto rg = CreateRecursiveGaussian(sigma);
for (auto _ : state) {
FastGaussian(rg, in, null_pool, &temp, &out);
// Prevent optimizing out
JXL_ASSERT(std::abs(out.ConstRow(ysize / 2)[xsize / 2] - expected) /
expected <
9E-5);
}
state.SetItemsProcessed(xsize * ysize * state.iterations());
}
void BM_GaussBlurFir(benchmark::State& state) {
// See GaussBlur1d for SIMD changes.
const size_t xsize = state.range();
const size_t ysize = xsize;
const double sigma = 7.0; // (from Butteraugli application)
ImageF in(xsize, ysize);
const float expected = xsize + ysize;
FillImage(expected, &in);
ImageF temp(xsize, ysize);
ImageF out(xsize, ysize);
const std::vector<float> kernel =
GaussianKernel(static_cast<int>(4 * sigma), static_cast<float>(sigma));
for (auto _ : state) {
// Prevent optimizing out
JXL_ASSERT(std::abs(Convolve(in, kernel).ConstRow(ysize / 2)[xsize / 2] -
expected) /
expected <
9E-5);
}
state.SetItemsProcessed(xsize * ysize * state.iterations());
}
void BM_GaussBlurSep7(benchmark::State& state) {
// See GaussBlur1d for SIMD changes.
const size_t xsize = state.range();
const size_t ysize = xsize;
ImageF in(xsize, ysize);
const float expected = xsize + ysize;
FillImage(expected, &in);
ImageF temp(xsize, ysize);
ImageF out(xsize, ysize);
ThreadPool* null_pool = nullptr;
// Gaussian with sigma 1
const WeightsSeparable7 weights = {{HWY_REP4(0.383103f), HWY_REP4(0.241843f),
HWY_REP4(0.060626f), HWY_REP4(0.00598f)},
{HWY_REP4(0.383103f), HWY_REP4(0.241843f),
HWY_REP4(0.060626f), HWY_REP4(0.00598f)}};
for (auto _ : state) {
Separable7(in, Rect(in), weights, null_pool, &out);
// Prevent optimizing out
JXL_ASSERT(std::abs(out.ConstRow(ysize / 2)[xsize / 2] - expected) /
expected <
9E-5);
}
state.SetItemsProcessed(xsize * ysize * state.iterations());
}
BENCHMARK(BM_GaussBlur1d)->Range(1 << 8, 1 << 14);
BENCHMARK(BM_GaussBlur2d)->Range(1 << 7, 1 << 10);
BENCHMARK(BM_GaussBlurFir)->Range(1 << 7, 1 << 10);
BENCHMARK(BM_GaussBlurSep7)->Range(1 << 7, 1 << 10);
} // namespace
} // namespace jxl
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