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/*
* Copyright (c) 2016, Alliance for Open Media. All rights reserved.
*
* This source code is subject to the terms of the BSD 2 Clause License and
* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
* was not distributed with this source code in the LICENSE file, you can
* obtain it at www.aomedia.org/license/software. If the Alliance for Open
* Media Patent License 1.0 was not distributed with this source code in the
* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
*/
#include <memory>
#include <new>
#include <tuple>
#include "config/aom_dsp_rtcd.h"
#include "gtest/gtest.h"
#include "test/acm_random.h"
#include "test/util.h"
#include "test/register_state_check.h"
#include "aom_dsp/flow_estimation/corner_match.h"
namespace test_libaom {
namespace AV1CornerMatch {
using libaom_test::ACMRandom;
typedef bool (*ComputeMeanStddevFunc)(const unsigned char *frame, int stride,
int x, int y, double *mean,
double *one_over_stddev);
typedef double (*ComputeCorrFunc)(const unsigned char *frame1, int stride1,
int x1, int y1, double mean1,
double one_over_stddev1,
const unsigned char *frame2, int stride2,
int x2, int y2, double mean2,
double one_over_stddev2);
using std::make_tuple;
using std::tuple;
typedef tuple<int, ComputeMeanStddevFunc, ComputeCorrFunc> CornerMatchParam;
class AV1CornerMatchTest : public ::testing::TestWithParam<CornerMatchParam> {
public:
~AV1CornerMatchTest() override;
void SetUp() override;
protected:
void GenerateInput(uint8_t *input1, uint8_t *input2, int w, int h, int mode);
void RunCheckOutput();
void RunSpeedTest();
ComputeMeanStddevFunc target_compute_mean_stddev_func;
ComputeCorrFunc target_compute_corr_func;
libaom_test::ACMRandom rnd_;
};
GTEST_ALLOW_UNINSTANTIATED_PARAMETERIZED_TEST(AV1CornerMatchTest);
AV1CornerMatchTest::~AV1CornerMatchTest() = default;
void AV1CornerMatchTest::SetUp() {
rnd_.Reset(ACMRandom::DeterministicSeed());
target_compute_mean_stddev_func = GET_PARAM(1);
target_compute_corr_func = GET_PARAM(2);
}
void AV1CornerMatchTest::GenerateInput(uint8_t *input1, uint8_t *input2, int w,
int h, int mode) {
if (mode == 0) {
for (int i = 0; i < h; ++i)
for (int j = 0; j < w; ++j) {
input1[i * w + j] = rnd_.Rand8();
input2[i * w + j] = rnd_.Rand8();
}
} else if (mode == 1) {
for (int i = 0; i < h; ++i)
for (int j = 0; j < w; ++j) {
int v = rnd_.Rand8();
input1[i * w + j] = v;
input2[i * w + j] = (v / 2) + (rnd_.Rand8() & 15);
}
}
}
void AV1CornerMatchTest::RunCheckOutput() {
const int w = 128, h = 128;
const int num_iters = 1000;
std::unique_ptr<uint8_t[]> input1(new (std::nothrow) uint8_t[w * h]);
std::unique_ptr<uint8_t[]> input2(new (std::nothrow) uint8_t[w * h]);
ASSERT_NE(input1, nullptr);
ASSERT_NE(input2, nullptr);
// Test the two extreme cases:
// i) Random data, should have correlation close to 0
// ii) Linearly related data + noise, should have correlation close to 1
int mode = GET_PARAM(0);
GenerateInput(&input1[0], &input2[0], w, h, mode);
for (int i = 0; i < num_iters; ++i) {
int x1 = MATCH_SZ_BY2 + rnd_.PseudoUniform(w + 1 - MATCH_SZ);
int y1 = MATCH_SZ_BY2 + rnd_.PseudoUniform(h + 1 - MATCH_SZ);
int x2 = MATCH_SZ_BY2 + rnd_.PseudoUniform(w + 1 - MATCH_SZ);
int y2 = MATCH_SZ_BY2 + rnd_.PseudoUniform(h + 1 - MATCH_SZ);
double c_mean1, c_one_over_stddev1, c_mean2, c_one_over_stddev2;
bool c_valid1 = aom_compute_mean_stddev_c(input1.get(), w, x1, y1, &c_mean1,
&c_one_over_stddev1);
bool c_valid2 = aom_compute_mean_stddev_c(input2.get(), w, x2, y2, &c_mean2,
&c_one_over_stddev2);
double simd_mean1, simd_one_over_stddev1, simd_mean2, simd_one_over_stddev2;
bool simd_valid1 = target_compute_mean_stddev_func(
input1.get(), w, x1, y1, &simd_mean1, &simd_one_over_stddev1);
bool simd_valid2 = target_compute_mean_stddev_func(
input2.get(), w, x2, y2, &simd_mean2, &simd_one_over_stddev2);
// Run the correlation calculation even if one of the "valid" flags is
// false, i.e. if one of the patches doesn't have enough variance. This is
// safe because any potential division by 0 is caught in
// aom_compute_mean_stddev(), and one_over_stddev is set to 0 instead.
// This causes aom_compute_correlation() to return 0, without causing a
// division by 0.
const double c_corr = aom_compute_correlation_c(
input1.get(), w, x1, y1, c_mean1, c_one_over_stddev1, input2.get(), w,
x2, y2, c_mean2, c_one_over_stddev2);
const double simd_corr = target_compute_corr_func(
input1.get(), w, x1, y1, c_mean1, c_one_over_stddev1, input2.get(), w,
x2, y2, c_mean2, c_one_over_stddev2);
ASSERT_EQ(simd_valid1, c_valid1);
ASSERT_EQ(simd_valid2, c_valid2);
ASSERT_EQ(simd_mean1, c_mean1);
ASSERT_EQ(simd_one_over_stddev1, c_one_over_stddev1);
ASSERT_EQ(simd_mean2, c_mean2);
ASSERT_EQ(simd_one_over_stddev2, c_one_over_stddev2);
ASSERT_EQ(simd_corr, c_corr);
}
}
void AV1CornerMatchTest::RunSpeedTest() {
const int w = 16, h = 16;
const int num_iters = 1000000;
aom_usec_timer ref_timer, test_timer;
std::unique_ptr<uint8_t[]> input1(new (std::nothrow) uint8_t[w * h]);
std::unique_ptr<uint8_t[]> input2(new (std::nothrow) uint8_t[w * h]);
ASSERT_NE(input1, nullptr);
ASSERT_NE(input2, nullptr);
// Test the two extreme cases:
// i) Random data, should have correlation close to 0
// ii) Linearly related data + noise, should have correlation close to 1
int mode = GET_PARAM(0);
GenerateInput(&input1[0], &input2[0], w, h, mode);
// Time aom_compute_mean_stddev()
double c_mean1, c_one_over_stddev1, c_mean2, c_one_over_stddev2;
aom_usec_timer_start(&ref_timer);
for (int i = 0; i < num_iters; i++) {
aom_compute_mean_stddev_c(input1.get(), w, 0, 0, &c_mean1,
&c_one_over_stddev1);
aom_compute_mean_stddev_c(input2.get(), w, 0, 0, &c_mean2,
&c_one_over_stddev2);
}
aom_usec_timer_mark(&ref_timer);
int elapsed_time_c = static_cast<int>(aom_usec_timer_elapsed(&ref_timer));
double simd_mean1, simd_one_over_stddev1, simd_mean2, simd_one_over_stddev2;
aom_usec_timer_start(&test_timer);
for (int i = 0; i < num_iters; i++) {
target_compute_mean_stddev_func(input1.get(), w, 0, 0, &simd_mean1,
&simd_one_over_stddev1);
target_compute_mean_stddev_func(input2.get(), w, 0, 0, &simd_mean2,
&simd_one_over_stddev2);
}
aom_usec_timer_mark(&test_timer);
int elapsed_time_simd = static_cast<int>(aom_usec_timer_elapsed(&test_timer));
printf(
"aom_compute_mean_stddev(): c_time=%6d simd_time=%6d "
"gain=%.3f\n",
elapsed_time_c, elapsed_time_simd,
(elapsed_time_c / (double)elapsed_time_simd));
// Time aom_compute_correlation
aom_usec_timer_start(&ref_timer);
for (int i = 0; i < num_iters; i++) {
aom_compute_correlation_c(input1.get(), w, 0, 0, c_mean1,
c_one_over_stddev1, input2.get(), w, 0, 0,
c_mean2, c_one_over_stddev2);
}
aom_usec_timer_mark(&ref_timer);
elapsed_time_c = static_cast<int>(aom_usec_timer_elapsed(&ref_timer));
aom_usec_timer_start(&test_timer);
for (int i = 0; i < num_iters; i++) {
target_compute_corr_func(input1.get(), w, 0, 0, c_mean1, c_one_over_stddev1,
input2.get(), w, 0, 0, c_mean2,
c_one_over_stddev2);
}
aom_usec_timer_mark(&test_timer);
elapsed_time_simd = static_cast<int>(aom_usec_timer_elapsed(&test_timer));
printf(
"aom_compute_correlation(): c_time=%6d simd_time=%6d "
"gain=%.3f\n",
elapsed_time_c, elapsed_time_simd,
(elapsed_time_c / (double)elapsed_time_simd));
}
TEST_P(AV1CornerMatchTest, CheckOutput) { RunCheckOutput(); }
TEST_P(AV1CornerMatchTest, DISABLED_Speed) { RunSpeedTest(); }
#if HAVE_SSE4_1
INSTANTIATE_TEST_SUITE_P(
SSE4_1, AV1CornerMatchTest,
::testing::Values(make_tuple(0, &aom_compute_mean_stddev_sse4_1,
&aom_compute_correlation_sse4_1),
make_tuple(1, &aom_compute_mean_stddev_sse4_1,
&aom_compute_correlation_sse4_1)));
#endif
#if HAVE_AVX2
INSTANTIATE_TEST_SUITE_P(
AVX2, AV1CornerMatchTest,
::testing::Values(make_tuple(0, &aom_compute_mean_stddev_avx2,
&aom_compute_correlation_avx2),
make_tuple(1, &aom_compute_mean_stddev_avx2,
&aom_compute_correlation_avx2)));
#endif
} // namespace AV1CornerMatch
} // namespace test_libaom