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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 "test/av1_convolve_2d_test_util.h"
#include "av1/common/convolve.h"
#include "av1/common/common_data.h"
using std::tr1::tuple;
using std::tr1::make_tuple;
namespace libaom_test {
namespace AV1Convolve2D {
#if CONFIG_JNT_COMP
::testing::internal::ParamGenerator<Convolve2DParam> BuildParams(
convolve_2d_func filter, convolve_2d_func filter2) {
const Convolve2DParam params[] = {
make_tuple(4, 4, filter, filter2), make_tuple(8, 8, filter, filter2),
make_tuple(64, 64, filter, filter2), make_tuple(4, 16, filter, filter2),
make_tuple(32, 8, filter, filter2),
};
return ::testing::ValuesIn(params);
}
#else
::testing::internal::ParamGenerator<Convolve2DParam> BuildParams(
convolve_2d_func filter) {
const Convolve2DParam params[] = {
make_tuple(4, 4, filter), make_tuple(8, 8, filter),
make_tuple(64, 64, filter), make_tuple(4, 16, filter),
make_tuple(32, 8, filter),
};
return ::testing::ValuesIn(params);
}
#endif // CONFIG_JNT_COMP
AV1Convolve2DTest::~AV1Convolve2DTest() {}
void AV1Convolve2DTest::SetUp() { rnd_.Reset(ACMRandom::DeterministicSeed()); }
void AV1Convolve2DTest::TearDown() { libaom_test::ClearSystemState(); }
void AV1Convolve2DTest::RunCheckOutput(convolve_2d_func test_impl) {
const int w = 128, h = 128;
const int out_w = GET_PARAM(0), out_h = GET_PARAM(1);
int i, j, k;
uint8_t *input = new uint8_t[h * w];
int output_n = out_h * MAX_SB_SIZE;
CONV_BUF_TYPE *output = new CONV_BUF_TYPE[output_n];
CONV_BUF_TYPE *output2 = new CONV_BUF_TYPE[output_n];
for (i = 0; i < h; ++i)
for (j = 0; j < w; ++j) input[i * w + j] = rnd_.Rand8();
int hfilter, vfilter, subx, suby;
for (hfilter = EIGHTTAP_REGULAR; hfilter < INTERP_FILTERS_ALL; ++hfilter) {
for (vfilter = EIGHTTAP_REGULAR; vfilter < INTERP_FILTERS_ALL; ++vfilter) {
InterpFilterParams filter_params_x =
av1_get_interp_filter_params((InterpFilter)hfilter);
InterpFilterParams filter_params_y =
av1_get_interp_filter_params((InterpFilter)vfilter);
const int do_average = rnd_.Rand8() & 1;
ConvolveParams conv_params1 =
get_conv_params_no_round(0, do_average, 0, output, MAX_SB_SIZE);
ConvolveParams conv_params2 =
get_conv_params_no_round(0, do_average, 0, output2, MAX_SB_SIZE);
for (subx = 0; subx < 16; ++subx)
for (suby = 0; suby < 16; ++suby) {
// av1_convolve_2d is designed for accumulate two predicted blocks for
// compound mode, so we set num_iter to two here.
// A larger number may introduce overflow
const int num_iters = 2;
memset(output, 0, output_n * sizeof(*output));
memset(output2, 0, output_n * sizeof(*output2));
for (i = 0; i < num_iters; ++i) {
// Choose random locations within the source block
int offset_r = 3 + rnd_.PseudoUniform(h - out_h - 7);
int offset_c = 3 + rnd_.PseudoUniform(w - out_w - 7);
av1_convolve_2d_c(input + offset_r * w + offset_c, w, output,
MAX_SB_SIZE, out_w, out_h, &filter_params_x,
&filter_params_y, subx, suby, &conv_params1);
test_impl(input + offset_r * w + offset_c, w, output2, MAX_SB_SIZE,
out_w, out_h, &filter_params_x, &filter_params_y, subx,
suby, &conv_params2);
for (j = 0; j < out_h; ++j)
for (k = 0; k < out_w; ++k) {
int idx = j * MAX_SB_SIZE + k;
ASSERT_EQ(output[idx], output2[idx])
<< "Pixel mismatch at index " << idx << " = (" << j << ", "
<< k << "), sub pixel offset = (" << suby << ", " << subx
<< ")";
}
}
}
}
}
delete[] input;
delete[] output;
delete[] output2;
}
#if CONFIG_JNT_COMP
void AV1Convolve2DTest::RunCheckOutput2(convolve_2d_func test_impl) {
const int w = 128, h = 128;
const int out_w = GET_PARAM(0), out_h = GET_PARAM(1);
int i, j, k, l, m;
uint8_t *input = new uint8_t[h * w];
int output_n = out_h * MAX_SB_SIZE;
CONV_BUF_TYPE *output = new CONV_BUF_TYPE[output_n];
CONV_BUF_TYPE *output2 = new CONV_BUF_TYPE[output_n];
for (i = 0; i < h; ++i)
for (j = 0; j < w; ++j) input[i * w + j] = rnd_.Rand8();
int hfilter, vfilter, subx, suby;
for (hfilter = EIGHTTAP_REGULAR; hfilter < INTERP_FILTERS_ALL; ++hfilter) {
for (vfilter = EIGHTTAP_REGULAR; vfilter < INTERP_FILTERS_ALL; ++vfilter) {
InterpFilterParams filter_params_x =
av1_get_interp_filter_params((InterpFilter)hfilter);
InterpFilterParams filter_params_y =
av1_get_interp_filter_params((InterpFilter)vfilter);
const int do_average = rnd_.Rand8() & 1;
ConvolveParams conv_params1 =
get_conv_params_no_round(0, do_average, 0, output, MAX_SB_SIZE);
ConvolveParams conv_params2 =
get_conv_params_no_round(0, do_average, 0, output2, MAX_SB_SIZE);
// Test special case where fwd and bck offsets are -1
conv_params1.fwd_offset = -1;
conv_params1.bck_offset = -1;
conv_params2.fwd_offset = -1;
conv_params2.bck_offset = -1;
for (subx = 0; subx < 16; ++subx)
for (suby = 0; suby < 16; ++suby) {
// av1_convolve_2d is designed for accumulate two predicted blocks
// for compound mode, so we set num_iter to two here.
// A larger number may introduce overflow
const int num_iters = 2;
memset(output, 0, output_n * sizeof(*output));
memset(output2, 0, output_n * sizeof(*output2));
for (i = 0; i < num_iters; ++i) {
// Choose random locations within the source block
int offset_r = 3 + rnd_.PseudoUniform(h - out_h - 7);
int offset_c = 3 + rnd_.PseudoUniform(w - out_w - 7);
av1_jnt_convolve_2d_c(input + offset_r * w + offset_c, w, output,
MAX_SB_SIZE, out_w, out_h, &filter_params_x,
&filter_params_y, subx, suby, &conv_params1);
test_impl(input + offset_r * w + offset_c, w, output2, MAX_SB_SIZE,
out_w, out_h, &filter_params_x, &filter_params_y, subx,
suby, &conv_params2);
for (j = 0; j < out_h; ++j)
for (k = 0; k < out_w; ++k) {
int idx = j * MAX_SB_SIZE + k;
ASSERT_EQ(output[idx], output2[idx])
<< "Mismatch at unit tests for av1_jnt_convolve_2d\n"
<< "Pixel mismatch at index " << idx << " = (" << j << ", "
<< k << "), sub pixel offset = (" << suby << ", " << subx
<< ")";
}
}
}
// Test different combination of fwd and bck offset weights
for (l = 0; l < 2; ++l) {
for (m = 0; m < 4; ++m) {
conv_params1.fwd_offset = quant_dist_lookup_table[l][m][0];
conv_params1.bck_offset = quant_dist_lookup_table[l][m][1];
conv_params2.fwd_offset = quant_dist_lookup_table[l][m][0];
conv_params2.bck_offset = quant_dist_lookup_table[l][m][1];
for (subx = 0; subx < 16; ++subx)
for (suby = 0; suby < 16; ++suby) {
// av1_convolve_2d is designed for accumulate two predicted blocks
// for compound mode, so we set num_iter to two here.
// A larger number may introduce overflow
const int num_iters = 2;
memset(output, 0, output_n * sizeof(*output));
memset(output2, 0, output_n * sizeof(*output2));
for (i = 0; i < num_iters; ++i) {
// Choose random locations within the source block
int offset_r = 3 + rnd_.PseudoUniform(h - out_h - 7);
int offset_c = 3 + rnd_.PseudoUniform(w - out_w - 7);
av1_jnt_convolve_2d_c(input + offset_r * w + offset_c, w,
output, MAX_SB_SIZE, out_w, out_h,
&filter_params_x, &filter_params_y, subx,
suby, &conv_params1);
test_impl(input + offset_r * w + offset_c, w, output2,
MAX_SB_SIZE, out_w, out_h, &filter_params_x,
&filter_params_y, subx, suby, &conv_params2);
for (j = 0; j < out_h; ++j)
for (k = 0; k < out_w; ++k) {
int idx = j * MAX_SB_SIZE + k;
ASSERT_EQ(output[idx], output2[idx])
<< "Mismatch at unit tests for av1_jnt_convolve_2d\n"
<< "Pixel mismatch at index " << idx << " = (" << j
<< ", " << k << "), sub pixel offset = (" << suby
<< ", " << subx << ")";
}
}
}
}
}
}
}
delete[] input;
delete[] output;
delete[] output2;
}
#endif // CONFIG_JNT_COMP
} // namespace AV1Convolve2D
#if CONFIG_HIGHBITDEPTH
namespace AV1HighbdConvolve2D {
::testing::internal::ParamGenerator<HighbdConvolve2DParam> BuildParams(
highbd_convolve_2d_func filter) {
const HighbdConvolve2DParam params[] = {
make_tuple(4, 4, 8, filter), make_tuple(8, 8, 8, filter),
make_tuple(64, 64, 8, filter), make_tuple(4, 16, 8, filter),
make_tuple(32, 8, 8, filter), make_tuple(4, 4, 10, filter),
make_tuple(8, 8, 10, filter), make_tuple(64, 64, 10, filter),
make_tuple(4, 16, 10, filter), make_tuple(32, 8, 10, filter),
make_tuple(4, 4, 12, filter), make_tuple(8, 8, 12, filter),
make_tuple(64, 64, 12, filter), make_tuple(4, 16, 12, filter),
make_tuple(32, 8, 12, filter),
};
return ::testing::ValuesIn(params);
}
AV1HighbdConvolve2DTest::~AV1HighbdConvolve2DTest() {}
void AV1HighbdConvolve2DTest::SetUp() {
rnd_.Reset(ACMRandom::DeterministicSeed());
}
void AV1HighbdConvolve2DTest::TearDown() { libaom_test::ClearSystemState(); }
void AV1HighbdConvolve2DTest::RunCheckOutput(
highbd_convolve_2d_func test_impl) {
const int w = 128, h = 128;
const int out_w = GET_PARAM(0), out_h = GET_PARAM(1);
const int bd = GET_PARAM(2);
int i, j, k;
uint16_t *input = new uint16_t[h * w];
int output_n = out_h * MAX_SB_SIZE;
CONV_BUF_TYPE *output = new CONV_BUF_TYPE[output_n];
CONV_BUF_TYPE *output2 = new CONV_BUF_TYPE[output_n];
for (i = 0; i < h; ++i)
for (j = 0; j < w; ++j) input[i * w + j] = rnd_.Rand16() & ((1 << bd) - 1);
int hfilter, vfilter, subx, suby;
for (hfilter = EIGHTTAP_REGULAR; hfilter < INTERP_FILTERS_ALL; ++hfilter) {
for (vfilter = EIGHTTAP_REGULAR; vfilter < INTERP_FILTERS_ALL; ++vfilter) {
InterpFilterParams filter_params_x =
av1_get_interp_filter_params((InterpFilter)hfilter);
InterpFilterParams filter_params_y =
av1_get_interp_filter_params((InterpFilter)vfilter);
ConvolveParams conv_params1 =
get_conv_params_no_round(0, 0, 0, output, MAX_SB_SIZE);
ConvolveParams conv_params2 =
get_conv_params_no_round(0, 0, 0, output2, MAX_SB_SIZE);
for (subx = 0; subx < 16; ++subx)
for (suby = 0; suby < 16; ++suby) {
// av1_convolve_2d is designed for accumulate two predicted blocks for
// compound mode, so we set num_iter to two here.
// A larger number may introduce overflow
const int num_iters = 2;
memset(output, 0, output_n * sizeof(*output));
memset(output2, 0, output_n * sizeof(*output2));
for (i = 0; i < num_iters; ++i) {
// Choose random locations within the source block
int offset_r = 3 + rnd_.PseudoUniform(h - out_h - 7);
int offset_c = 3 + rnd_.PseudoUniform(w - out_w - 7);
av1_highbd_convolve_2d_c(input + offset_r * w + offset_c, w, output,
MAX_SB_SIZE, out_w, out_h,
&filter_params_x, &filter_params_y, subx,
suby, &conv_params1, bd);
test_impl(input + offset_r * w + offset_c, w, output2, MAX_SB_SIZE,
out_w, out_h, &filter_params_x, &filter_params_y, subx,
suby, &conv_params2, bd);
for (j = 0; j < out_h; ++j)
for (k = 0; k < out_w; ++k) {
int idx = j * MAX_SB_SIZE + k;
ASSERT_EQ(output[idx], output2[idx])
<< "Pixel mismatch at index " << idx << " = (" << j << ", "
<< k << "), sub pixel offset = (" << suby << ", " << subx
<< ")";
}
}
}
}
}
delete[] input;
delete[] output;
delete[] output2;
}
} // namespace AV1HighbdConvolve2D
#endif // CONFIG_HIGHBITDEPTH
} // namespace libaom_test