test/corner_match_test.cc - avm - Git at Google


            
            /*

            
             * 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 "third_party/googletest/src/googletest/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
	/*
	* 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 "third_party/googletest/src/googletest/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