wuggy/Dactyloidae
wuggy/Dactyloidae
1
0
Fork 0
mirror of https://repo.dactyloidae.xyz/Dactyloidae/UXP.git synced 2026-09-19 14:57:32 +09:00
Code Issues Projects Releases Packages Wiki Activity

Update aom to commit id e87fb2378f01103d5d6e477a4ef6892dc714e614

Browse source
This commit is contained in:
trav90 2018-10-18 21:53:44 -05:00 • committed by Roy Tam
commit 992c6637e3
429 changed files with 76047 additions and 40937 deletions
Download patch file Download diff file Expand all files Collapse all files

49
third_party/aom/test/av1_convolve_2d_test_util.cc vendored
View file

@ -23,9 +23,9 @@ namespace AV1Convolve2D {
::testing::internal::ParamGenerator<Convolve2DParam> BuildParams(
convolve_2d_func filter) {
const Convolve2DParam params[] = {
make_tuple(4, 4, 20, filter), make_tuple(8, 8, 10, filter),
make_tuple(64, 64, 1, filter), make_tuple(4, 16, 10, filter),
make_tuple(32, 8, 5, filter),
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);
}
@ -38,7 +38,6 @@ 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);
const int num_iters = GET_PARAM(2);
int i, j, k;
uint8_t *input = new uint8_t[h * w];
@ -50,9 +49,6 @@ void AV1Convolve2DTest::RunCheckOutput(convolve_2d_func test_impl) {
for (i = 0; i < h; ++i)
for (j = 0; j < w; ++j) input[i * w + j] = rnd_.Rand8();
memset(output, 0, output_n * sizeof(CONV_BUF_TYPE));
memset(output2, 0, output_n * sizeof(CONV_BUF_TYPE));
int hfilter, vfilter, subx, suby;
for (hfilter = EIGHTTAP_REGULAR; hfilter < INTERP_FILTERS_ALL; ++hfilter) {
for (vfilter = EIGHTTAP_REGULAR; vfilter < INTERP_FILTERS_ALL; ++vfilter) {
@ -60,13 +56,20 @@ void AV1Convolve2DTest::RunCheckOutput(convolve_2d_func test_impl) {
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, 0, 0, output, MAX_SB_SIZE);
get_conv_params_no_round(0, do_average, 0, output, MAX_SB_SIZE);
ConvolveParams conv_params2 =
get_conv_params_no_round(0, 0, 0, output2, MAX_SB_SIZE);
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);
@ -102,14 +105,14 @@ namespace AV1HighbdConvolve2D {
::testing::internal::ParamGenerator<HighbdConvolve2DParam> BuildParams(
highbd_convolve_2d_func filter) {
const HighbdConvolve2DParam params[] = {
make_tuple(4, 4, 20, 8, filter), make_tuple(8, 8, 10, 8, filter),
make_tuple(64, 64, 1, 8, filter), make_tuple(4, 16, 10, 8, filter),
make_tuple(32, 8, 10, 8, filter), make_tuple(4, 4, 20, 10, filter),
make_tuple(8, 8, 10, 10, filter), make_tuple(64, 64, 1, 10, filter),
make_tuple(4, 16, 10, 10, filter), make_tuple(32, 8, 10, 10, filter),
make_tuple(4, 4, 20, 12, filter), make_tuple(8, 8, 10, 12, filter),
make_tuple(64, 64, 1, 12, filter), make_tuple(4, 16, 10, 12, filter),
make_tuple(32, 8, 10, 12, filter),
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);
}
@ -125,8 +128,7 @@ 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 num_iters = GET_PARAM(2);
const int bd = GET_PARAM(3);
const int bd = GET_PARAM(2);
int i, j, k;
uint16_t *input = new uint16_t[h * w];
@ -138,9 +140,6 @@ void AV1HighbdConvolve2DTest::RunCheckOutput(
for (i = 0; i < h; ++i)
for (j = 0; j < w; ++j) input[i * w + j] = rnd_.Rand16() & ((1 << bd) - 1);
memset(output, 0, output_n * sizeof(CONV_BUF_TYPE));
memset(output2, 0, output_n * sizeof(CONV_BUF_TYPE));
int hfilter, vfilter, subx, suby;
for (hfilter = EIGHTTAP_REGULAR; hfilter < INTERP_FILTERS_ALL; ++hfilter) {
for (vfilter = EIGHTTAP_REGULAR; vfilter < INTERP_FILTERS_ALL; ++vfilter) {
@ -155,6 +154,12 @@ void AV1HighbdConvolve2DTest::RunCheckOutput(
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);