Google Git
Sign in
aomedia / aom / refs/heads/experimental / . / av1 / encoder / partition_strategy.c
blob: a9fdb65f6fa382f66d15b0b9b6bd4e86b9eae5c1 [file] [log] [blame] [edit]
/*
* Copyright (c) 2019, 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 <float.h>
#include "config/aom_dsp_rtcd.h"
#include "aom_ports/system_state.h"
#include "av1/common/enums.h"
#include "av1/common/reconinter.h"
#if !CONFIG_REALTIME_ONLY
#include "av1/common/cnn.h"
#include "av1/encoder/partition_model_weights.h"
#include "av1/encoder/partition_cnn_weights.h"
#endif
#if CONFIG_EXT_RECUR_PARTITIONS
#include "av1/common/idct.h"
#include "av1/encoder/hybrid_fwd_txfm.h"
#endif // CONFIG_EXT_RECUR_PARTITIONS
#include "av1/encoder/encoder.h"
#include "av1/encoder/partition_search_utils.h"
#include "av1/encoder/partition_strategy.h"
#include "av1/encoder/rdopt.h"
#if !CONFIG_REALTIME_ONLY
static void simple_motion_search_prune_part_features(
AV1_COMP *const cpi, MACROBLOCK *x, SIMPLE_MOTION_DATA_TREE *sms_tree,
int mi_row, int mi_col, BLOCK_SIZE bsize, float *features,
int features_to_get);
#endif
static INLINE int convert_bsize_to_idx(BLOCK_SIZE bsize) {
switch (bsize) {
case BLOCK_128X128: return 0;
case BLOCK_64X64: return 1;
case BLOCK_32X32: return 2;
case BLOCK_16X16: return 3;
case BLOCK_8X8: return 4;
default: assert(0 && "Invalid bsize"); return -1;
}
}
#if !CONFIG_REALTIME_ONLY
// TODO(chiyotsai@google.com): This is very much a work in progress. We still
// need to the following:
// -- add support for hdres
// -- add support for pruning rectangular partitions
// -- use reconstructed pixels instead of source pixels for padding
// -- use chroma pixels in addition to luma pixels
void av1_intra_mode_cnn_partition(const AV1_COMMON *const cm, MACROBLOCK *x,
int bsize, int quad_tree_idx,
int *partition_none_allowed,
int *partition_horz_allowed,
int *partition_vert_allowed,
int *do_rectangular_split,
int *do_square_split) {
assert(cm->seq_params.sb_size >= BLOCK_64X64 &&
"Invalid sb_size for intra_cnn!");
const int bsize_idx = convert_bsize_to_idx(bsize);
if (bsize == BLOCK_128X128) {
return;
}
// Precompute the CNN part and cache the result in MACROBLOCK
if (bsize == BLOCK_64X64 && !x->cnn_output_valid) {
aom_clear_system_state();
const CNN_CONFIG *cnn_config = &av1_intra_mode_cnn_partition_cnn_config;
// Prepare the output
const CNN_THREAD_DATA thread_data = { .num_workers = 1, .workers = NULL };
const int num_outputs = 4;
const int output_dims[4] = { 1, 2, 4, 8 };
const int out_chs[4] = { CNN_BRANCH_0_OUT_CH, CNN_BRANCH_1_OUT_CH,
CNN_BRANCH_2_OUT_CH, CNN_BRANCH_3_OUT_CH };
float *output_buffer[CNN_TOT_OUT_CH];
float **cur_output_buf = output_buffer;
float *curr_buf_ptr = x->cnn_buffer;
for (int output_idx = 0; output_idx < num_outputs; output_idx++) {
const int num_chs = out_chs[output_idx];
const int ch_size = output_dims[output_idx] * output_dims[output_idx];
for (int ch = 0; ch < num_chs; ch++) {
cur_output_buf[ch] = curr_buf_ptr;
curr_buf_ptr += ch_size;
}
cur_output_buf += num_chs;
}
CNN_MULTI_OUT output = {
.num_outputs = 4,
.output_channels = out_chs,
.output_strides = output_dims,
.output_buffer = output_buffer,
};
// Prepare the input
const MACROBLOCKD *xd = &x->e_mbd;
const int bit_depth = xd->bd;
const int dc_q = av1_dc_quant_QTX(x->qindex, 0,
#if CONFIG_EXTQUANT
cm->seq_params.base_y_dc_delta_q,
#endif // CONFIG_EXTQUANT
bit_depth) >>
(bit_depth - 8);
x->log_q = logf(1.0f + (float)((int64_t)dc_q * (int64_t)dc_q) /
(256 << (2 * QUANT_TABLE_BITS)));
x->log_q = (x->log_q - av1_intra_mode_cnn_partition_mean[0]) /
av1_intra_mode_cnn_partition_std[0];
const int width = 65, height = 65,
stride = x->plane[AOM_PLANE_Y].src.stride;
if (xd->cur_buf->flags & YV12_FLAG_HIGHBITDEPTH) {
uint16_t *image[1] = {
CONVERT_TO_SHORTPTR(x->plane[AOM_PLANE_Y].src.buf) - stride - 1
};
av1_cnn_predict_img_multi_out_highbd(image, width, height, stride,
cnn_config, &thread_data, bit_depth,
&output);
} else {
uint8_t *image[1] = { x->plane[AOM_PLANE_Y].src.buf - stride - 1 };
av1_cnn_predict_img_multi_out(image, width, height, stride, cnn_config,
&thread_data, &output);
}
x->cnn_output_valid = 1;
}
if (!x->cnn_output_valid) {
return;
}
const NN_CONFIG *dnn_configs[5] = {
NULL,
&av1_intra_mode_cnn_partition_branch_0_dnn_config,
&av1_intra_mode_cnn_partition_branch_1_dnn_config,
&av1_intra_mode_cnn_partition_branch_2_dnn_config,
&av1_intra_mode_cnn_partition_branch_3_dnn_config,
};
const NN_CONFIG *dnn_config = dnn_configs[bsize_idx];
aom_clear_system_state();
float dnn_features[100];
float logits[4] = { 0.0f };
const float *branch_0 = x->cnn_buffer;
const float *branch_1 = branch_0 + CNN_BRANCH_0_OUT_SIZE;
const float *branch_2 = branch_1 + CNN_BRANCH_1_OUT_SIZE;
const float *branch_3 = branch_2 + CNN_BRANCH_2_OUT_SIZE;
if (bsize == BLOCK_64X64) {
int f_idx = 0;
for (int ch_idx = 0; ch_idx < CNN_BRANCH_0_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_0[ch_idx];
}
const int spa_stride = 2 * 2;
for (int lin_idx = 0; lin_idx < spa_stride; lin_idx++) {
for (int ch_idx = 0; ch_idx < CNN_BRANCH_1_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_1[lin_idx + ch_idx * spa_stride];
}
}
dnn_features[f_idx++] = x->log_q;
} else if (bsize == BLOCK_32X32) {
int f_idx = 0;
for (int idx = 0; idx < CNN_BRANCH_0_OUT_CH; idx++) {
dnn_features[f_idx++] = branch_0[idx];
}
const int curr_lin_idx = quad_to_linear_1[quad_tree_idx - 1];
const int spa_stride = 2 * 2;
for (int ch_idx = 0; ch_idx < CNN_BRANCH_1_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_1[curr_lin_idx + ch_idx * spa_stride];
}
dnn_features[f_idx++] = x->log_q;
} else if (bsize == BLOCK_16X16) {
int f_idx = 0;
const int prev_quad_idx = (quad_tree_idx - 1) / 4;
const int prev_lin_idx = quad_to_linear_1[prev_quad_idx - 1];
const int prev_spa_stride = 2 * 2;
for (int ch_idx = 0; ch_idx < CNN_BRANCH_1_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_1[prev_lin_idx + ch_idx * prev_spa_stride];
}
const int curr_lin_idx = quad_to_linear_2[quad_tree_idx - 5];
const int spa_stride = 4 * 4;
for (int ch_idx = 0; ch_idx < CNN_BRANCH_2_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_2[curr_lin_idx + ch_idx * spa_stride];
}
dnn_features[f_idx++] = x->log_q;
} else if (bsize == BLOCK_8X8) {
int f_idx = 0;
const int prev_quad_idx = (quad_tree_idx - 1) / 4;
const int prev_lin_idx = quad_to_linear_2[prev_quad_idx - 5];
const int prev_spa_stride = 4 * 4;
for (int ch_idx = 0; ch_idx < CNN_BRANCH_2_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_2[prev_lin_idx + ch_idx * prev_spa_stride];
}
const int curr_lin_idx = quad_to_linear_3[quad_tree_idx - 21];
const int spa_stride = 8 * 8;
for (int ch_idx = 0; ch_idx < CNN_BRANCH_3_OUT_CH; ch_idx++) {
dnn_features[f_idx++] = branch_3[curr_lin_idx + ch_idx * spa_stride];
}
dnn_features[f_idx++] = x->log_q;
} else {
assert(0 && "Invalid bsize in intra_cnn partition");
}
// Make decision
av1_nn_predict(dnn_features, dnn_config, 1, logits);
aom_clear_system_state();
const int is_720p_or_larger = AOMMIN(cm->width, cm->height) >= 720;
const int is_480p_or_larger = AOMMIN(cm->width, cm->height) >= 480;
float split_only_thresh = 100.0f, no_split_thresh = -100.0f;
if (is_720p_or_larger) {
split_only_thresh =
av1_intra_mode_cnn_partition_split_thresh_hdres[bsize_idx];
no_split_thresh =
av1_intra_mode_cnn_partition_no_split_thresh_hdres[bsize_idx];
} else if (is_480p_or_larger) {
split_only_thresh =
av1_intra_mode_cnn_partition_split_thresh_midres[bsize_idx];
no_split_thresh =
av1_intra_mode_cnn_partition_no_split_thresh_midres[bsize_idx];
} else {
split_only_thresh =
av1_intra_mode_cnn_partition_split_thresh_lowres[bsize_idx];
no_split_thresh =
av1_intra_mode_cnn_partition_no_split_thresh_lowres[bsize_idx];
}
if (logits[0] > split_only_thresh) {
*partition_none_allowed = 0;
*partition_horz_allowed = 0;
*partition_vert_allowed = 0;
*do_rectangular_split = 0;
}
if (logits[0] < no_split_thresh) {
*do_square_split = 0;
}
}
void av1_simple_motion_search_based_split(
AV1_COMP *const cpi, MACROBLOCK *x, SIMPLE_MOTION_DATA_TREE *sms_tree,
int mi_row, int mi_col, BLOCK_SIZE bsize, int *partition_none_allowed,
int *partition_horz_allowed, int *partition_vert_allowed,
int *do_rectangular_split, int *do_square_split) {
aom_clear_system_state();
const AV1_COMMON *const cm = &cpi->common;
const int is_720p_or_larger = AOMMIN(cm->width, cm->height) >= 720;
const int is_480p_or_larger = AOMMIN(cm->width, cm->height) >= 480;
const int bsize_idx = convert_bsize_to_idx(bsize);
assert(bsize_idx >= 0 && bsize_idx <= 4 &&
"Invalid bsize in simple_motion_search_based_split");
float split_only_thresh = 100.0f, no_split_thresh = -100.0f;
const float *ml_mean = av1_simple_motion_search_split_mean[bsize_idx];
const float *ml_std = av1_simple_motion_search_split_std[bsize_idx];
const NN_CONFIG *nn_config =
av1_simple_motion_search_split_nn_config[bsize_idx];
if (is_720p_or_larger) {
split_only_thresh = av1_simple_motion_search_split_hdres_thresh[bsize_idx];
no_split_thresh = av1_simple_motion_search_split_hdres_no_thresh[bsize_idx];
} else if (is_480p_or_larger) {
split_only_thresh = av1_simple_motion_search_split_midres_thresh[bsize_idx];
no_split_thresh =
av1_simple_motion_search_split_midres_no_thresh[bsize_idx];
} else {
split_only_thresh = av1_simple_motion_search_split_lowres_thresh[bsize_idx];
no_split_thresh =
av1_simple_motion_search_split_lowres_no_thresh[bsize_idx];
}
float features[FEATURE_SIZE_SMS_SPLIT] = { 0.0f };
simple_motion_search_prune_part_features(cpi, x, sms_tree, mi_row, mi_col,
bsize, features,
FEATURE_SMS_SPLIT_MODEL_FLAG);
for (int idx = 0; idx < FEATURE_SIZE_SMS_SPLIT; idx++) {
features[idx] = (features[idx] - ml_mean[idx]) / ml_std[idx];
}
float score = 0.0f;
av1_nn_predict(features, nn_config, 1, &score);
aom_clear_system_state();
if (score > split_only_thresh) {
*partition_none_allowed = 0;
*partition_horz_allowed = 0;
*partition_vert_allowed = 0;
*do_rectangular_split = 0;
}
if (cpi->sf.simple_motion_search_split >= 2 && score < no_split_thresh) {
*do_square_split = 0;
}
}
// Given a list of ref frames in refs, performs simple_motion_search on each of
// the refs and returns the ref with the smallest sse. Returns -1 if none of the
// ref in the list is available. Also stores the best sse and var in best_sse,
// best_var, respectively. If save_mv is 0, don't update mv_ref_fulls in
// sms_tree. If save_mv is 1, update mv_ref_fulls under sms_tree and the
// subtrees.
static int simple_motion_search_get_best_ref(
AV1_COMP *const cpi, MACROBLOCK *x, SIMPLE_MOTION_DATA_TREE *sms_tree,
int mi_row, int mi_col, BLOCK_SIZE bsize, const int *const refs,
int num_refs, int use_subpixel, int save_mv, unsigned int *best_sse,
unsigned int *best_var) {
const AV1_COMMON *const cm = &cpi->common;
int best_ref = -1;
if (mi_col >= cm->mi_cols || mi_row >= cm->mi_rows) {
// If the whole block is outside of the image, set the var and sse to 0.
*best_var = 0;
*best_sse = 0;
return best_ref;
}
// Otherwise do loop through the reference frames and find the one with the
// minimum SSE
const MACROBLOCKD *xd = &x->e_mbd;
const MV *mv_ref_fulls = sms_tree->mv_ref_fulls;
const int num_planes = 1;
*best_sse = INT_MAX;
for (int ref_idx = 0; ref_idx < num_refs; ref_idx++) {
const int ref = refs[ref_idx];
if (cpi->ref_frame_flags & av1_ref_frame_flag_list[ref]) {
unsigned int curr_sse = 0, curr_var = 0;
av1_simple_motion_search(cpi, x, mi_row, mi_col, bsize, ref,
mv_ref_fulls[ref], num_planes, use_subpixel);
curr_var = cpi->fn_ptr[bsize].vf(
x->plane[0].src.buf, x->plane[0].src.stride, xd->plane[0].dst.buf,
xd->plane[0].dst.stride, &curr_sse);
if (curr_sse < *best_sse) {
*best_sse = curr_sse;
*best_var = curr_var;
best_ref = ref;
}
if (save_mv) {
const int new_mv_row = x->best_mv.as_mv.row / 8;
const int new_mv_col = x->best_mv.as_mv.col / 8;
sms_tree->mv_ref_fulls[ref].row = new_mv_row;
sms_tree->mv_ref_fulls[ref].col = new_mv_col;
if (bsize >= BLOCK_8X8) {
for (int r_idx = 0; r_idx < 4; r_idx++) {
// Propagate the new motion vectors to a lower level
SIMPLE_MOTION_DATA_TREE *sub_tree = sms_tree->split[r_idx];
sub_tree->mv_ref_fulls[ref].row = new_mv_row;
sub_tree->mv_ref_fulls[ref].col = new_mv_col;
}
}
}
}
}
return best_ref;
}
// Collects features using simple_motion_search and store them in features. The
// features are also cached in SIMPLE_MOTION_DATA_TREE. By default, the features
// collected are the sse and var from the subblocks flagged by features_to_get.
// Furthermore, if features is not NULL, then 7 more features are appended to
// the end of features:
// - log(1.0 + dc_q ** 2)
// - whether an above macroblock exists
// - width of above macroblock
// - height of above macroblock
// - whether a left marcoblock exists
// - width of left macroblock
// - height of left macroblock
static void simple_motion_search_prune_part_features(
AV1_COMP *const cpi, MACROBLOCK *x, SIMPLE_MOTION_DATA_TREE *sms_tree,
int mi_row, int mi_col, BLOCK_SIZE bsize, float *features,
int features_to_get) {
const int w_mi = mi_size_wide[bsize];
const int h_mi = mi_size_high[bsize];
assert(mi_size_wide[bsize] == mi_size_high[bsize]);
assert(cpi->ref_frame_flags & av1_ref_frame_flag_list[LAST_FRAME] ||
cpi->ref_frame_flags & av1_ref_frame_flag_list[ALTREF_FRAME]);
// Setting up motion search
const int ref_list[] = { cpi->rc.is_src_frame_alt_ref ? ALTREF_FRAME
: LAST_FRAME };
const int num_refs = 1;
const int use_subpixel = 1;
// Doing whole block first to update the mv
if (!sms_tree->sms_none_valid && features_to_get & FEATURE_SMS_NONE_FLAG) {
simple_motion_search_get_best_ref(cpi, x, sms_tree, mi_row, mi_col, bsize,
ref_list, num_refs, use_subpixel, 1,
&sms_tree->sms_none_feat[0],
&sms_tree->sms_none_feat[1]);
sms_tree->sms_none_valid = 1;
}
// Split subblocks
if (features_to_get & FEATURE_SMS_SPLIT_FLAG) {
const BLOCK_SIZE subsize = get_partition_subsize(bsize, PARTITION_SPLIT);
for (int r_idx = 0; r_idx < 4; r_idx++) {
const int sub_mi_col = mi_col + (r_idx & 1) * w_mi / 2;
const int sub_mi_row = mi_row + (r_idx >> 1) * h_mi / 2;
SIMPLE_MOTION_DATA_TREE *sub_tree = sms_tree->split[r_idx];
if (!sub_tree->sms_none_valid) {
simple_motion_search_get_best_ref(
cpi, x, sub_tree, sub_mi_row, sub_mi_col, subsize, ref_list,
num_refs, use_subpixel, 1, &sub_tree->sms_none_feat[0],
&sub_tree->sms_none_feat[1]);
sub_tree->sms_none_valid = 1;
}
}
}
// Rectangular subblocks
if (!sms_tree->sms_rect_valid && features_to_get & FEATURE_SMS_RECT_FLAG) {
// Horz subblock
BLOCK_SIZE subsize = get_partition_subsize(bsize, PARTITION_HORZ);
for (int r_idx = 0; r_idx < 2; r_idx++) {
const int sub_mi_col = mi_col + 0;
const int sub_mi_row = mi_row + r_idx * h_mi / 2;
simple_motion_search_get_best_ref(
cpi, x, sms_tree, sub_mi_row, sub_mi_col, subsize, ref_list, num_refs,
use_subpixel, 0, &sms_tree->sms_rect_feat[2 * r_idx],
&sms_tree->sms_rect_feat[2 * r_idx + 1]);
}
// Vert subblock
subsize = get_partition_subsize(bsize, PARTITION_VERT);
for (int r_idx = 0; r_idx < 2; r_idx++) {
const int sub_mi_col = mi_col + r_idx * w_mi / 2;
const int sub_mi_row = mi_row + 0;
simple_motion_search_get_best_ref(
cpi, x, sms_tree, sub_mi_row, sub_mi_col, subsize, ref_list, num_refs,
use_subpixel, 0, &sms_tree->sms_rect_feat[4 + 2 * r_idx],
&sms_tree->sms_rect_feat[4 + 2 * r_idx + 1]);
}
sms_tree->sms_rect_valid = 1;
}
if (!features) return;
aom_clear_system_state();
int f_idx = 0;
if (features_to_get & FEATURE_SMS_NONE_FLAG) {
for (int sub_idx = 0; sub_idx < 2; sub_idx++) {
features[f_idx++] = logf(1.0f + sms_tree->sms_none_feat[sub_idx]);
}
}
if (features_to_get & FEATURE_SMS_SPLIT_FLAG) {
for (int sub_idx = 0; sub_idx < 4; sub_idx++) {
SIMPLE_MOTION_DATA_TREE *sub_tree = sms_tree->split[sub_idx];
features[f_idx++] = logf(1.0f + sub_tree->sms_none_feat[0]);
features[f_idx++] = logf(1.0f + sub_tree->sms_none_feat[1]);
}
}
if (features_to_get & FEATURE_SMS_RECT_FLAG) {
for (int sub_idx = 0; sub_idx < 8; sub_idx++) {
features[f_idx++] = logf(1.0f + sms_tree->sms_rect_feat[sub_idx]);
}
}
aom_clear_system_state();
const MACROBLOCKD *xd = &x->e_mbd;
set_offsets_for_motion_search(cpi, x, mi_row, mi_col, bsize);
// Q_INDEX
const int dc_q = av1_dc_quant_QTX(x->qindex, 0,
#if CONFIG_EXTQUANT
cpi->common.seq_params.base_y_dc_delta_q,
#endif // CONFIG_EXTQUANT
xd->bd) >>
(xd->bd - 8);
features[f_idx++] = logf(1.0f + (float)((int64_t)dc_q * (int64_t)dc_q) /
(256 << (2 * QUANT_TABLE_BITS)));
// Neighbor stuff
const int has_above = !!xd->above_mbmi;
const int has_left = !!xd->left_mbmi;
const BLOCK_SIZE above_bsize = has_above ? xd->above_mbmi->sb_type : bsize;
const BLOCK_SIZE left_bsize = has_left ? xd->left_mbmi->sb_type : bsize;
features[f_idx++] = (float)has_above;
features[f_idx++] = (float)mi_size_wide_log2[above_bsize];
features[f_idx++] = (float)mi_size_high_log2[above_bsize];
features[f_idx++] = (float)has_left;
features[f_idx++] = (float)mi_size_wide_log2[left_bsize];
features[f_idx++] = (float)mi_size_high_log2[left_bsize];
}
void av1_simple_motion_search_prune_part(
AV1_COMP *const cpi, MACROBLOCK *x, SIMPLE_MOTION_DATA_TREE *sms_tree,
int mi_row, int mi_col, BLOCK_SIZE bsize, int *partition_none_allowed,
int *partition_horz_allowed, int *partition_vert_allowed,
int *do_square_split, int *do_rectangular_split, int *prune_horz,
int *prune_vert) {
const AV1_COMMON *const cm = &cpi->common;
// Get model parameters
const NN_CONFIG *nn_config = NULL;
const float *prune_thresh = NULL, *only_thresh = NULL;
const float *ml_mean = NULL, *ml_std = NULL;
float features[FEATURE_SIZE_SMS_PRUNE_PART] = { 0.0f };
if (bsize == BLOCK_128X128) {
nn_config = &av1_simple_motion_search_prune_part_nn_config_128;
ml_mean = av1_simple_motion_search_prune_part_mean_128;
ml_std = av1_simple_motion_search_prune_part_std_128;
prune_thresh = av1_simple_motion_search_prune_part_prune_thresh_128;
only_thresh = av1_simple_motion_search_prune_part_only_thresh_128;
} else if (bsize == BLOCK_64X64) {
nn_config = &av1_simple_motion_search_prune_part_nn_config_64;
ml_mean = av1_simple_motion_search_prune_part_mean_64;
ml_std = av1_simple_motion_search_prune_part_std_64;
prune_thresh = av1_simple_motion_search_prune_part_prune_thresh_64;
only_thresh = av1_simple_motion_search_prune_part_only_thresh_64;
} else if (bsize == BLOCK_32X32) {
nn_config = &av1_simple_motion_search_prune_part_nn_config_32;
ml_mean = av1_simple_motion_search_prune_part_mean_32;
ml_std = av1_simple_motion_search_prune_part_std_32;
prune_thresh = av1_simple_motion_search_prune_part_prune_thresh_32;
only_thresh = av1_simple_motion_search_prune_part_only_thresh_32;
} else if (bsize == BLOCK_16X16) {
nn_config = &av1_simple_motion_search_prune_part_nn_config_16;
ml_mean = av1_simple_motion_search_prune_part_mean_16;
ml_std = av1_simple_motion_search_prune_part_std_16;
prune_thresh = av1_simple_motion_search_prune_part_prune_thresh_16;
only_thresh = av1_simple_motion_search_prune_part_only_thresh_16;
} else if (bsize == BLOCK_8X8) {
nn_config = &av1_simple_motion_search_prune_part_nn_config_8;
ml_mean = av1_simple_motion_search_prune_part_mean_8;
ml_std = av1_simple_motion_search_prune_part_std_8;
prune_thresh = av1_simple_motion_search_prune_part_prune_thresh_8;
only_thresh = av1_simple_motion_search_prune_part_only_thresh_8;
} else {
assert(0 && "Unexpected block size in simple_motion_prune_part");
}
// If there is no valid threshold, return immediately.
if (!nn_config || (prune_thresh[PARTITION_HORZ] == 0.0f &&
prune_thresh[PARTITION_VERT] == 0.0f)) {
return;
}
if (bsize < BLOCK_8X8) {
return;
}
// Get features
simple_motion_search_prune_part_features(cpi, x, sms_tree, mi_row, mi_col,
bsize, features,
FEATURE_SMS_PRUNE_PART_FLAG);
for (int f_idx = 0; f_idx < FEATURE_SIZE_SMS_PRUNE_PART; f_idx++) {
features[f_idx] = (features[f_idx] - ml_mean[f_idx]) / ml_std[f_idx];
}
// Get probabilities
float scores[EXT_PARTITION_TYPES] = { 0.0f },
probs[EXT_PARTITION_TYPES] = { 0.0f };
const int num_classes = (bsize == BLOCK_128X128 || bsize == BLOCK_8X8)
? PARTITION_TYPES
: EXT_PARTITION_TYPES;
av1_nn_predict(features, nn_config, 1, scores);
aom_clear_system_state();
av1_nn_softmax(scores, probs, num_classes);
// Determine if we should prune rectangular partitions.
if (cpi->sf.simple_motion_search_prune_rect && !frame_is_intra_only(cm) &&
(*partition_horz_allowed || *partition_vert_allowed) &&
bsize >= BLOCK_8X8 && !av1_superres_scaled(cm)) {
*prune_horz = probs[PARTITION_HORZ] <= prune_thresh[PARTITION_HORZ];
*prune_vert = probs[PARTITION_VERT] <= prune_thresh[PARTITION_VERT];
}
// Silence compiler warnings
(void)only_thresh;
(void)partition_none_allowed;
(void)do_square_split;
(void)do_rectangular_split;
}
// Early terminates PARTITION_NONE using simple_motion_search features and the
// rate, distortion, and rdcost of PARTITION_NONE. This is only called when:
// - The frame is a show frame
// - The frame is not intra only
// - The current bsize is > BLOCK_8X8
// - blk_row + blk_height/2 < total_rows and blk_col + blk_width/2 < total_cols
void av1_simple_motion_search_early_term_none(
AV1_COMP *const cpi, MACROBLOCK *x, SIMPLE_MOTION_DATA_TREE *sms_tree,
int mi_row, int mi_col, BLOCK_SIZE bsize, const RD_STATS *none_rdc,
int *early_terminate) {
// TODO(chiyotsai@google.com): There are other features we can extract from
// PARTITION_NONE. Play with this later.
float features[FEATURE_SIZE_SMS_TERM_NONE] = { 0.0f };
simple_motion_search_prune_part_features(cpi, x, sms_tree, mi_row, mi_col,
bsize, features,
FEATURE_SMS_PRUNE_PART_FLAG);
int f_idx = FEATURE_SIZE_SMS_PRUNE_PART;
features[f_idx++] = logf(1.0f + (float)none_rdc->rate);
features[f_idx++] = logf(1.0f + (float)none_rdc->dist);
features[f_idx++] = logf(1.0f + (float)none_rdc->rdcost);
assert(f_idx == FEATURE_SIZE_SMS_TERM_NONE);
const float *ml_mean = NULL;
const float *ml_std = NULL;
const float *ml_model = NULL;
if (bsize == BLOCK_128X128) {
ml_mean = av1_simple_motion_search_term_none_mean_128;
ml_std = av1_simple_motion_search_term_none_std_128;
ml_model = av1_simple_motion_search_term_none_model_128;
} else if (bsize == BLOCK_64X64) {
ml_mean = av1_simple_motion_search_term_none_mean_64;
ml_std = av1_simple_motion_search_term_none_std_64;
ml_model = av1_simple_motion_search_term_none_model_64;
} else if (bsize == BLOCK_32X32) {
ml_mean = av1_simple_motion_search_term_none_mean_32;
ml_std = av1_simple_motion_search_term_none_std_32;
ml_model = av1_simple_motion_search_term_none_model_32;
} else if (bsize == BLOCK_16X16) {
ml_mean = av1_simple_motion_search_term_none_mean_16;
ml_std = av1_simple_motion_search_term_none_std_16;
ml_model = av1_simple_motion_search_term_none_model_16;
} else {
assert(0 && "Unexpected block size in simple_motion_term_none");
}
if (ml_model) {
float score = 0.0f;
for (f_idx = 0; f_idx < FEATURE_SIZE_SMS_TERM_NONE; f_idx++) {
score +=
ml_model[f_idx] * (features[f_idx] - ml_mean[f_idx]) / ml_std[f_idx];
}
score += ml_model[FEATURE_SIZE_SMS_TERM_NONE];
if (score >= 0.0f) {
*early_terminate = 1;
}
}
}
void av1_get_max_min_partition_features(AV1_COMP *const cpi, MACROBLOCK *x,
int mi_row, int mi_col,
float *features) {
AV1_COMMON *const cm = &cpi->common;
MACROBLOCKD *xd = &x->e_mbd;
const BLOCK_SIZE sb_size = cm->seq_params.sb_size;
assert(sb_size == BLOCK_128X128);
int f_idx = 0;
const int dc_q = av1_dc_quant_QTX(x->qindex, 0,
#if CONFIG_EXTQUANT
cm->seq_params.base_y_dc_delta_q,
#endif // CONFIG_EXTQUANT
xd->bd) >>
(xd->bd - 8);
aom_clear_system_state();
const float log_q_sq = logf(1.0f + (float)((int64_t)dc_q * (int64_t)dc_q) /
(256 << (2 * QUANT_TABLE_BITS)));
// Perform full-pixel single motion search in Y plane of 16x16 mbs in the sb
float sum_mv_row_sq = 0;
float sum_mv_row = 0;
float min_abs_mv_row = FLT_MAX;
float max_abs_mv_row = 0;
float sum_mv_col_sq = 0;
float sum_mv_col = 0;
float min_abs_mv_col = FLT_MAX;
float max_abs_mv_col = 0;
float sum_log_sse_sq = 0;
float sum_log_sse = 0;
float min_log_sse = FLT_MAX;
float max_log_sse = 0;
const BLOCK_SIZE mb_size = BLOCK_16X16;
const int mb_rows = block_size_high[sb_size] / block_size_high[mb_size];
const int mb_cols = block_size_wide[sb_size] / block_size_wide[mb_size];
const int mb_in_mi_size_high_log2 = mi_size_high_log2[mb_size];
const int mb_in_mi_size_wide_log2 = mi_size_wide_log2[mb_size];
for (int mb_row = 0; mb_row < mb_rows; mb_row++)
for (int mb_col = 0; mb_col < mb_cols; mb_col++) {
const int this_mi_row = mi_row + (mb_row << mb_in_mi_size_high_log2);
const int this_mi_col = mi_col + (mb_col << mb_in_mi_size_wide_log2);
unsigned int sse = 0;
unsigned int var = 0;
const MV ref_mv_full = { .row = 0, .col = 0 };
av1_simple_motion_sse_var(cpi, x, this_mi_row, this_mi_col, mb_size,
ref_mv_full, 0, &sse, &var);
aom_clear_system_state();
const float mv_row = (float)(x->best_mv.as_mv.row / 8);
const float mv_col = (float)(x->best_mv.as_mv.col / 8);
const float log_sse = logf(1.0f + (float)sse);
const float abs_mv_row = fabsf(mv_row);
const float abs_mv_col = fabsf(mv_col);
sum_mv_row_sq += mv_row * mv_row;
sum_mv_row += mv_row;
sum_mv_col_sq += mv_col * mv_col;
sum_mv_col += mv_col;
if (abs_mv_row < min_abs_mv_row) min_abs_mv_row = abs_mv_row;
if (abs_mv_row > max_abs_mv_row) max_abs_mv_row = abs_mv_row;
if (abs_mv_col < min_abs_mv_col) min_abs_mv_col = abs_mv_col;
if (abs_mv_col > max_abs_mv_col) max_abs_mv_col = abs_mv_col;
sum_log_sse_sq += log_sse * log_sse;
sum_log_sse += log_sse;
if (log_sse < min_log_sse) min_log_sse = log_sse;
if (log_sse > max_log_sse) max_log_sse = log_sse;
}
aom_clear_system_state();
const float avg_mv_row = sum_mv_row / 64.0f;
const float var_mv_row = sum_mv_row_sq / 64.0f - avg_mv_row * avg_mv_row;
const float avg_mv_col = sum_mv_col / 64.0f;
const float var_mv_col = sum_mv_col_sq / 64.0f - avg_mv_col * avg_mv_col;
const float avg_log_sse = sum_log_sse / 64.0f;
const float var_log_sse = sum_log_sse_sq / 64.0f - avg_log_sse * avg_log_sse;
features[f_idx++] = avg_log_sse;
features[f_idx++] = avg_mv_col;
features[f_idx++] = avg_mv_row;
features[f_idx++] = log_q_sq;
features[f_idx++] = max_abs_mv_col;
features[f_idx++] = max_abs_mv_row;
features[f_idx++] = max_log_sse;
features[f_idx++] = min_abs_mv_col;
features[f_idx++] = min_abs_mv_row;
features[f_idx++] = min_log_sse;
features[f_idx++] = var_log_sse;
features[f_idx++] = var_mv_col;
features[f_idx++] = var_mv_row;
assert(f_idx == FEATURE_SIZE_MAX_MIN_PART_PRED);
}
BLOCK_SIZE av1_predict_max_partition(AV1_COMP *const cpi, MACROBLOCK *const x,
const float *features) {
float scores[MAX_NUM_CLASSES_MAX_MIN_PART_PRED] = { 0.0f },
probs[MAX_NUM_CLASSES_MAX_MIN_PART_PRED] = { 0.0f };
const NN_CONFIG *nn_config = &av1_max_part_pred_nn_config;
assert(cpi->sf.auto_max_partition_based_on_simple_motion != NOT_IN_USE);
aom_clear_system_state();
av1_nn_predict(features, nn_config, 1, scores);
av1_nn_softmax(scores, probs, MAX_NUM_CLASSES_MAX_MIN_PART_PRED);
int result = MAX_NUM_CLASSES_MAX_MIN_PART_PRED - 1;
if (cpi->sf.auto_max_partition_based_on_simple_motion == DIRECT_PRED) {
result = 0;
float max_prob = probs[0];
for (int i = 1; i < MAX_NUM_CLASSES_MAX_MIN_PART_PRED; ++i) {
if (probs[i] > max_prob) {
max_prob = probs[i];
result = i;
}
}
} else if (cpi->sf.auto_max_partition_based_on_simple_motion ==
RELAXED_PRED) {
for (result = MAX_NUM_CLASSES_MAX_MIN_PART_PRED - 1; result >= 0;
--result) {
if (result < MAX_NUM_CLASSES_MAX_MIN_PART_PRED - 1) {
probs[result] += probs[result + 1];
}
if (probs[result] > 0.2) break;
}
} else if (cpi->sf.auto_max_partition_based_on_simple_motion == ADAPT_PRED) {
const BLOCK_SIZE sb_size = cpi->common.seq_params.sb_size;
MACROBLOCKD *const xd = &x->e_mbd;
// TODO(debargha): x->source_variance is unavailable at this point,
// so compute. The redundant recomputation later can be removed.
const unsigned int source_variance =
is_cur_buf_hbd(xd)
? av1_high_get_sby_perpixel_variance(cpi, &x->plane[0].src, sb_size,
xd->bd)
: av1_get_sby_perpixel_variance(cpi, &x->plane[0].src, sb_size);
if (source_variance > 16) {
const double thresh = source_variance < 128 ? 0.05 : 0.1;
for (result = MAX_NUM_CLASSES_MAX_MIN_PART_PRED - 1; result >= 0;
--result) {
if (result < MAX_NUM_CLASSES_MAX_MIN_PART_PRED - 1) {
probs[result] += probs[result + 1];
}
if (probs[result] > thresh) break;
}
}
}
return (BLOCK_SIZE)((result + 2) * 3);
}
// Get the minimum partition block width and height(in log scale) under a
// SIMPLE_MOTION_DATA_TREE.
static void get_min_bsize(const SIMPLE_MOTION_DATA_TREE *sms_tree, int *min_bw,
int *min_bh) {
if (!sms_tree) return;
const BLOCK_SIZE bsize = sms_tree->block_size;
if (bsize == BLOCK_4X4) {
*min_bw = 0;
*min_bh = 0;
return;
}
PARTITION_TYPE part_type = sms_tree->partitioning;
if (part_type == PARTITION_INVALID) return;
if (part_type == PARTITION_SPLIT) {
for (int i = 0; i < 4; ++i) {
get_min_bsize(sms_tree->split[i], min_bw, min_bh);
}
} else {
#if !CONFIG_EXT_RECUR_PARTITIONS
if (part_type == PARTITION_HORZ_A || part_type == PARTITION_HORZ_B ||
part_type == PARTITION_VERT_A || part_type == PARTITION_VERT_B)
part_type = PARTITION_SPLIT;
#endif // !CONFIG_EXT_RECUR_PARTITIONS
const BLOCK_SIZE subsize = get_partition_subsize(bsize, part_type);
if (subsize != BLOCK_INVALID) {
*min_bw = AOMMIN(*min_bw, mi_size_wide_log2[subsize]);
*min_bh = AOMMIN(*min_bh, mi_size_high_log2[subsize]);
}
}
}
static INLINE void add_rd_feature(int64_t rd, int64_t best_rd, float *features,
int *feature_idx) {
const int rd_valid = rd > 0 && rd < INT64_MAX;
const float rd_ratio = rd_valid ? (float)rd / best_rd : 1.0f;
features[(*feature_idx)++] = (float)rd_valid;
features[(*feature_idx)++] = rd_ratio;
}
#define FEATURES 31
void av1_ml_early_term_after_split(AV1_COMP *const cpi, MACROBLOCK *const x,
SIMPLE_MOTION_DATA_TREE *const sms_tree,
BLOCK_SIZE bsize, int64_t best_rd,
int64_t part_none_rd, int64_t part_split_rd,
int64_t *split_block_rd, int mi_row,
int mi_col,
int *const terminate_partition_search) {
if (best_rd <= 0 || best_rd == INT64_MAX || *terminate_partition_search)
return;
const AV1_COMMON *const cm = &cpi->common;
const int is_480p_or_larger = AOMMIN(cm->width, cm->height) >= 480;
const NN_CONFIG *nn_config = NULL;
float thresh = -1e6;
switch (bsize) {
case BLOCK_128X128: break;
case BLOCK_64X64:
nn_config = &av1_early_term_after_split_nnconfig_64;
thresh = is_480p_or_larger ? -2.0f : -1.2f;
break;
case BLOCK_32X32:
nn_config = &av1_early_term_after_split_nnconfig_32;
thresh = is_480p_or_larger ? -2.6f : -2.3f;
break;
case BLOCK_16X16:
nn_config = &av1_early_term_after_split_nnconfig_16;
thresh = is_480p_or_larger ? -2.0f : -2.4f;
break;
case BLOCK_8X8:
nn_config = &av1_early_term_after_split_nnconfig_8;
thresh = is_480p_or_larger ? -1.0f : -1.4f;
break;
case BLOCK_4X4: break;
default:
assert(0 && "Invalid block size in av1_ml_early_term_after_split().");
break;
}
if (!nn_config) return;
// Use more conservative threshold for level 1.
if (cpi->sf.ml_early_term_after_part_split_level < 2) thresh -= 0.3f;
const MACROBLOCKD *const xd = &x->e_mbd;
const int dc_q = av1_dc_quant_QTX(x->qindex, 0,
#if CONFIG_EXTQUANT
cm->seq_params.base_y_dc_delta_q,
#endif // CONFIG_EXTQUANT
xd->bd) >>
(xd->bd - 8);
const int bs = block_size_wide[bsize];
int f_idx = 0;
float features[FEATURES] = { 0.0f };
aom_clear_system_state();
features[f_idx++] = logf(1.0f + (float)dc_q / (4 << QUANT_TABLE_BITS));
features[f_idx++] = logf(1.0f + (float)best_rd / bs / bs / 1024.0f);
add_rd_feature(part_none_rd, best_rd, features, &f_idx);
add_rd_feature(part_split_rd, best_rd, features, &f_idx);
for (int i = 0; i < 4; ++i) {
add_rd_feature(split_block_rd[i], best_rd, features, &f_idx);
int min_bw = MAX_SB_SIZE_LOG2;
int min_bh = MAX_SB_SIZE_LOG2;
get_min_bsize(sms_tree->split[i], &min_bw, &min_bh);
features[f_idx++] = (float)min_bw;
features[f_idx++] = (float)min_bh;
}
simple_motion_search_prune_part_features(cpi, x, sms_tree, mi_row, mi_col,
bsize, NULL,
FEATURE_SMS_PRUNE_PART_FLAG);
features[f_idx++] = logf(1.0f + (float)sms_tree->sms_none_feat[1]);
features[f_idx++] = logf(1.0f + (float)sms_tree->split[0]->sms_none_feat[1]);
features[f_idx++] = logf(1.0f + (float)sms_tree->split[1]->sms_none_feat[1]);
features[f_idx++] = logf(1.0f + (float)sms_tree->split[2]->sms_none_feat[1]);
features[f_idx++] = logf(1.0f + (float)sms_tree->split[3]->sms_none_feat[1]);
features[f_idx++] = logf(1.0f + (float)sms_tree->sms_rect_feat[1]);
features[f_idx++] = logf(1.0f + (float)sms_tree->sms_rect_feat[3]);
features[f_idx++] = logf(1.0f + (float)sms_tree->sms_rect_feat[5]);
features[f_idx++] = logf(1.0f + (float)sms_tree->sms_rect_feat[7]);
assert(f_idx == FEATURES);
float score = 0.0f;
av1_nn_predict(features, nn_config, 1, &score);
// Score is indicator of confidence that we should NOT terminate.
if (score < thresh) *terminate_partition_search = 1;
}
#undef FEATURES
void av1_ml_prune_rect_partition(const AV1_COMP *const cpi,
const MACROBLOCK *const x, BLOCK_SIZE bsize,
int64_t best_rd, int64_t none_rd,
int64_t *split_rd, int *const dst_prune_horz,
int *const dst_prune_vert) {
if (bsize < BLOCK_8X8 || best_rd >= 1000000000) return;
best_rd = AOMMAX(best_rd, 1);
const NN_CONFIG *nn_config = NULL;
const float prob_thresholds[5] = { 0.01f, 0.01f, 0.004f, 0.002f, 0.002f };
float cur_thresh = 0.0f;
switch (bsize) {
case BLOCK_8X8:
nn_config = &av1_rect_partition_nnconfig_8;
cur_thresh = prob_thresholds[0];
break;
case BLOCK_16X16:
nn_config = &av1_rect_partition_nnconfig_16;
cur_thresh = prob_thresholds[1];
break;
case BLOCK_32X32:
nn_config = &av1_rect_partition_nnconfig_32;
cur_thresh = prob_thresholds[2];
break;
case BLOCK_64X64:
nn_config = &av1_rect_partition_nnconfig_64;
cur_thresh = prob_thresholds[3];
break;
case BLOCK_128X128:
nn_config = &av1_rect_partition_nnconfig_128;
cur_thresh = prob_thresholds[4];
break;
default: assert(0 && "Unexpected bsize.");
}
if (!nn_config) return;
aom_clear_system_state();
// 1. Compute input features
float features[9];
// RD cost ratios
for (int i = 0; i < 5; i++) features[i] = 1.0f;
if (none_rd > 0 && none_rd < 1000000000)
features[0] = (float)none_rd / (float)best_rd;
for (int i = 0; i < 4; i++) {
if (split_rd[i] > 0 && split_rd[i] < 1000000000)
features[1 + i] = (float)split_rd[i] / (float)best_rd;
}
// Variance ratios
const MACROBLOCKD *const xd = &x->e_mbd;
int whole_block_variance;
if (is_cur_buf_hbd(xd)) {
whole_block_variance = av1_high_get_sby_perpixel_variance(
cpi, &x->plane[0].src, bsize, xd->bd);
} else {
whole_block_variance =
av1_get_sby_perpixel_variance(cpi, &x->plane[0].src, bsize);
}
whole_block_variance = AOMMAX(whole_block_variance, 1);
int split_variance[4];
const BLOCK_SIZE subsize = get_partition_subsize(bsize, PARTITION_SPLIT);
struct buf_2d buf;
buf.stride = x->plane[0].src.stride;
const int bw = block_size_wide[bsize];
for (int i = 0; i < 4; ++i) {
const int x_idx = (i & 1) * bw / 2;
const int y_idx = (i >> 1) * bw / 2;
buf.buf = x->plane[0].src.buf + x_idx + y_idx * buf.stride;
if (is_cur_buf_hbd(xd)) {
split_variance[i] =
av1_high_get_sby_perpixel_variance(cpi, &buf, subsize, xd->bd);
} else {
split_variance[i] = av1_get_sby_perpixel_variance(cpi, &buf, subsize);
}
}
for (int i = 0; i < 4; i++)
features[5 + i] = (float)split_variance[i] / (float)whole_block_variance;
// 2. Do the prediction and prune 0-2 partitions based on their probabilities
float raw_scores[3] = { 0.0f };
av1_nn_predict(features, nn_config, 1, raw_scores);
aom_clear_system_state();
float probs[3] = { 0.0f };
av1_nn_softmax(raw_scores, probs, 3);
// probs[0] is the probability of the fact that both rectangular partitions
// are worse than current best_rd
if (probs[1] <= cur_thresh) (*dst_prune_horz) = 1;
if (probs[2] <= cur_thresh) (*dst_prune_vert) = 1;
}
// Use a ML model to predict if horz_a, horz_b, vert_a, and vert_b should be
// considered.
void av1_ml_prune_ab_partition(BLOCK_SIZE bsize, int part_ctx, int var_ctx,
int64_t best_rd, int64_t horz_rd[2],
int64_t vert_rd[2], int64_t split_rd[4],
int *const horza_partition_allowed,
int *const horzb_partition_allowed,
int *const verta_partition_allowed,
int *const vertb_partition_allowed) {
if (bsize < BLOCK_8X8 || best_rd >= 1000000000) return;
const NN_CONFIG *nn_config = NULL;
switch (bsize) {
case BLOCK_8X8: nn_config = NULL; break;
case BLOCK_16X16: nn_config = &av1_ab_partition_nnconfig_16; break;
case BLOCK_32X32: nn_config = &av1_ab_partition_nnconfig_32; break;
case BLOCK_64X64: nn_config = &av1_ab_partition_nnconfig_64; break;
case BLOCK_128X128: nn_config = &av1_ab_partition_nnconfig_128; break;
default: assert(0 && "Unexpected bsize.");
}
if (!nn_config) return;
aom_clear_system_state();
// Generate features.
float features[10];
int feature_index = 0;
features[feature_index++] = (float)part_ctx;
features[feature_index++] = (float)var_ctx;
const int rdcost = (int)AOMMIN(INT_MAX, best_rd);
int sub_block_rdcost[8] = { 0 };
int rd_index = 0;
for (int i = 0; i < 2; ++i) {
if (horz_rd[i] > 0 && horz_rd[i] < 1000000000)
sub_block_rdcost[rd_index] = (int)horz_rd[i];
++rd_index;
}
for (int i = 0; i < 2; ++i) {
if (vert_rd[i] > 0 && vert_rd[i] < 1000000000)
sub_block_rdcost[rd_index] = (int)vert_rd[i];
++rd_index;
}
for (int i = 0; i < 4; ++i) {
if (split_rd[i] > 0 && split_rd[i] < 1000000000)
sub_block_rdcost[rd_index] = (int)split_rd[i];
++rd_index;
}
for (int i = 0; i < 8; ++i) {
// Ratio between the sub-block RD and the whole-block RD.
float rd_ratio = 1.0f;
if (sub_block_rdcost[i] > 0 && sub_block_rdcost[i] < rdcost)
rd_ratio = (float)sub_block_rdcost[i] / (float)rdcost;
features[feature_index++] = rd_ratio;
}
assert(feature_index == 10);
// Calculate scores using the NN model.
float score[16] = { 0.0f };
av1_nn_predict(features, nn_config, 1, score);
aom_clear_system_state();
int int_score[16];
int max_score = -1000;
for (int i = 0; i < 16; ++i) {
int_score[i] = (int)(100 * score[i]);
max_score = AOMMAX(int_score[i], max_score);
}
// Make decisions based on the model scores.
int thresh = max_score;
switch (bsize) {
case BLOCK_16X16: thresh -= 150; break;
case BLOCK_32X32: thresh -= 100; break;
default: break;
}
*horza_partition_allowed = 0;
*horzb_partition_allowed = 0;
*verta_partition_allowed = 0;
*vertb_partition_allowed = 0;
for (int i = 0; i < 16; ++i) {
if (int_score[i] >= thresh) {
if ((i >> 0) & 1) *horza_partition_allowed = 1;
if ((i >> 1) & 1) *horzb_partition_allowed = 1;
if ((i >> 2) & 1) *verta_partition_allowed = 1;
if ((i >> 3) & 1) *vertb_partition_allowed = 1;
}
}
}
#define FEATURES 18
#define LABELS 4
// Use a ML model to predict if horz4 and vert4 should be considered.
void av1_ml_prune_4_partition(const AV1_COMP *const cpi, MACROBLOCK *const x,
BLOCK_SIZE bsize, int part_ctx, int64_t best_rd,
int64_t horz_rd[2], int64_t vert_rd[2],
int64_t split_rd[4],
int *const partition_horz4_allowed,
int *const partition_vert4_allowed,
unsigned int pb_source_variance, int mi_row,
int mi_col) {
if (best_rd >= 1000000000) return;
const NN_CONFIG *nn_config = NULL;
switch (bsize) {
case BLOCK_16X16: nn_config = &av1_4_partition_nnconfig_16; break;
case BLOCK_32X32: nn_config = &av1_4_partition_nnconfig_32; break;
case BLOCK_64X64: nn_config = &av1_4_partition_nnconfig_64; break;
default: assert(0 && "Unexpected bsize.");
}
if (!nn_config) return;
aom_clear_system_state();
// Generate features.
float features[FEATURES];
int feature_index = 0;
features[feature_index++] = (float)part_ctx;
features[feature_index++] = (float)get_unsigned_bits(pb_source_variance);
const int rdcost = (int)AOMMIN(INT_MAX, best_rd);
int sub_block_rdcost[8] = { 0 };
int rd_index = 0;
for (int i = 0; i < 2; ++i) {
if (horz_rd[i] > 0 && horz_rd[i] < 1000000000)
sub_block_rdcost[rd_index] = (int)horz_rd[i];
++rd_index;
}
for (int i = 0; i < 2; ++i) {
if (vert_rd[i] > 0 && vert_rd[i] < 1000000000)
sub_block_rdcost[rd_index] = (int)vert_rd[i];
++rd_index;
}
for (int i = 0; i < 4; ++i) {
if (split_rd[i] > 0 && split_rd[i] < 1000000000)
sub_block_rdcost[rd_index] = (int)split_rd[i];
++rd_index;
}
for (int i = 0; i < 8; ++i) {
// Ratio between the sub-block RD and the whole-block RD.
float rd_ratio = 1.0f;
if (sub_block_rdcost[i] > 0 && sub_block_rdcost[i] < rdcost)
rd_ratio = (float)sub_block_rdcost[i] / (float)rdcost;
features[feature_index++] = rd_ratio;
}
// Get variance of the 1:4 and 4:1 sub-blocks.
unsigned int horz_4_source_var[4] = { 0 };
unsigned int vert_4_source_var[4] = { 0 };
{
#if CONFIG_EXT_RECUR_PARTITIONS
BLOCK_SIZE horz_4_bs = get_partition_subsize(bsize, PARTITION_HORZ_3);
BLOCK_SIZE vert_4_bs = get_partition_subsize(bsize, PARTITION_VERT_3);
#else
BLOCK_SIZE horz_4_bs = get_partition_subsize(bsize, PARTITION_HORZ_4);
BLOCK_SIZE vert_4_bs = get_partition_subsize(bsize, PARTITION_VERT_4);
#endif // CONFIG_EXT_RECUR_PARTITIONS
CHROMA_REF_INFO chr_ref_info = { 1, 0, mi_row, mi_col, bsize, bsize };
av1_setup_src_planes(x, cpi->source, mi_row, mi_col,
av1_num_planes(&cpi->common), &chr_ref_info);
const int src_stride = x->plane[0].src.stride;
uint8_t *src = x->plane[0].src.buf;
const MACROBLOCKD *const xd = &x->e_mbd;
struct buf_2d horz_4_src, vert_4_src;
horz_4_src.stride = src_stride;
vert_4_src.stride = src_stride;
for (int i = 0; i < 4; ++i) {
horz_4_src.buf = src + i * block_size_high[horz_4_bs] * src_stride;
vert_4_src.buf = src + i * block_size_wide[vert_4_bs];
if (is_cur_buf_hbd(xd)) {
horz_4_source_var[i] = av1_high_get_sby_perpixel_variance(
cpi, &horz_4_src, horz_4_bs, xd->bd);
vert_4_source_var[i] = av1_high_get_sby_perpixel_variance(
cpi, &vert_4_src, vert_4_bs, xd->bd);
} else {
horz_4_source_var[i] =
av1_get_sby_perpixel_variance(cpi, &horz_4_src, horz_4_bs);
vert_4_source_var[i] =
av1_get_sby_perpixel_variance(cpi, &vert_4_src, vert_4_bs);
}
}
}
const float denom = (float)(pb_source_variance + 1);
const float low_b = 0.1f;
const float high_b = 10.0f;
for (int i = 0; i < 4; ++i) {
// Ratio between the 4:1 sub-block variance and the whole-block variance.
float var_ratio = (float)(horz_4_source_var[i] + 1) / denom;
if (var_ratio < low_b) var_ratio = low_b;
if (var_ratio > high_b) var_ratio = high_b;
features[feature_index++] = var_ratio;
}
for (int i = 0; i < 4; ++i) {
// Ratio between the 1:4 sub-block RD and the whole-block RD.
float var_ratio = (float)(vert_4_source_var[i] + 1) / denom;
if (var_ratio < low_b) var_ratio = low_b;
if (var_ratio > high_b) var_ratio = high_b;
features[feature_index++] = var_ratio;
}
assert(feature_index == FEATURES);
// Calculate scores using the NN model.
float score[LABELS] = { 0.0f };
av1_nn_predict(features, nn_config, 1, score);
aom_clear_system_state();
int int_score[LABELS];
int max_score = -1000;
for (int i = 0; i < LABELS; ++i) {
int_score[i] = (int)(100 * score[i]);
max_score = AOMMAX(int_score[i], max_score);
}
// Make decisions based on the model scores.
int thresh = max_score;
switch (bsize) {
case BLOCK_16X16: thresh -= 500; break;
case BLOCK_32X32: thresh -= 500; break;
case BLOCK_64X64: thresh -= 200; break;
default: break;
}
*partition_horz4_allowed = 0;
*partition_vert4_allowed = 0;
for (int i = 0; i < LABELS; ++i) {
if (int_score[i] >= thresh) {
if ((i >> 0) & 1) *partition_horz4_allowed = 1;
if ((i >> 1) & 1) *partition_vert4_allowed = 1;
}
}
}
#undef FEATURES
#undef LABELS
#define FEATURES 4
int av1_ml_predict_breakout(const AV1_COMP *const cpi, BLOCK_SIZE bsize,
const MACROBLOCK *const x,
const RD_STATS *const rd_stats,
unsigned int pb_source_variance) {
const NN_CONFIG *nn_config = NULL;
int thresh = 0;
switch (bsize) {
case BLOCK_8X8:
nn_config = &av1_partition_breakout_nnconfig_8;
thresh = cpi->sf.ml_partition_search_breakout_thresh[0];
break;
case BLOCK_16X16:
nn_config = &av1_partition_breakout_nnconfig_16;
thresh = cpi->sf.ml_partition_search_breakout_thresh[1];
break;
case BLOCK_32X32:
nn_config = &av1_partition_breakout_nnconfig_32;
thresh = cpi->sf.ml_partition_search_breakout_thresh[2];
break;
case BLOCK_64X64:
nn_config = &av1_partition_breakout_nnconfig_64;
thresh = cpi->sf.ml_partition_search_breakout_thresh[3];
break;
case BLOCK_128X128:
nn_config = &av1_partition_breakout_nnconfig_128;
thresh = cpi->sf.ml_partition_search_breakout_thresh[4];
break;
default: assert(0 && "Unexpected bsize.");
}
if (!nn_config || thresh < 0) return 0;
// Generate feature values.
float features[FEATURES];
int feature_index = 0;
aom_clear_system_state();
const int num_pels_log2 = num_pels_log2_lookup[bsize];
float rate_f = (float)AOMMIN(rd_stats->rate, INT_MAX);
rate_f = ((float)x->rdmult / 128.0f / 512.0f / (float)(1 << num_pels_log2)) *
rate_f;
features[feature_index++] = rate_f;
const float dist_f =
(float)(AOMMIN(rd_stats->dist, INT_MAX) >> num_pels_log2);
features[feature_index++] = dist_f;
features[feature_index++] = (float)pb_source_variance;
const int dc_q = (int)x->plane[0].dequant_QTX[0];
features[feature_index++] =
(float)(dc_q * dc_q) / (256 << (2 * QUANT_TABLE_BITS));
assert(feature_index == FEATURES);
// Calculate score using the NN model.
float score = 0.0f;
av1_nn_predict(features, nn_config, 1, &score);
aom_clear_system_state();
// Make decision.
return (int)(score * 100) >= thresh;
}
#undef FEATURES
#if CONFIG_EXT_RECUR_PARTITIONS
// Gets the number of sms data in a single dimension
static INLINE int get_sms_count_from_length(int mi_length) {
switch (mi_length) {
case 32: return BLOCK_128_COUNT;
case 16: return BLOCK_64_COUNT;
case 8: return BLOCK_32_COUNT;
case 4: return BLOCK_16_COUNT;
case 2: return BLOCK_8_COUNT;
case 1: return BLOCK_4_COUNT;
default: assert(0 && "Invalid mi_width"); return -1;
}
}
// Gets the linear index corresponds to the current block.
static INLINE int get_sms_arr_1d_idx(int mi_bsize, int mi_in_sb) {
int idx = -1;
if (mi_bsize == 1) {
idx = mi_in_sb;
} else {
assert(mi_in_sb % (mi_bsize / 2) == 0);
idx = mi_in_sb / (mi_bsize / 2);
}
assert(idx >= 0 && idx < get_sms_count_from_length(mi_bsize));
return idx;
}
#define MAKE_SMS_ARR_SWITCH_CASE(width, height) \
case BLOCK_##width##X##height: { \
return sms_bufs->b_##width##x##height; \
}
// Returns the buffer in SimpleMotionDataBufs that correspond to bsize.
static INLINE SimpleMotionData *get_sms_arr(SimpleMotionDataBufs *sms_bufs,
BLOCK_SIZE bsize) {
switch (bsize) {
// Square blocks
MAKE_SMS_ARR_SWITCH_CASE(128, 128);
MAKE_SMS_ARR_SWITCH_CASE(64, 64);
MAKE_SMS_ARR_SWITCH_CASE(32, 32);
MAKE_SMS_ARR_SWITCH_CASE(16, 16);
MAKE_SMS_ARR_SWITCH_CASE(8, 8);
MAKE_SMS_ARR_SWITCH_CASE(4, 4);
// 1:2 blocks
MAKE_SMS_ARR_SWITCH_CASE(64, 128);
MAKE_SMS_ARR_SWITCH_CASE(32, 64);
MAKE_SMS_ARR_SWITCH_CASE(16, 32);
MAKE_SMS_ARR_SWITCH_CASE(8, 16);
MAKE_SMS_ARR_SWITCH_CASE(4, 8);
// 2:1 blocks
MAKE_SMS_ARR_SWITCH_CASE(128, 64);
MAKE_SMS_ARR_SWITCH_CASE(64, 32);
MAKE_SMS_ARR_SWITCH_CASE(32, 16);
MAKE_SMS_ARR_SWITCH_CASE(16, 8);
MAKE_SMS_ARR_SWITCH_CASE(8, 4);
// 1:4 blocks
MAKE_SMS_ARR_SWITCH_CASE(16, 64);
MAKE_SMS_ARR_SWITCH_CASE(8, 32);
MAKE_SMS_ARR_SWITCH_CASE(4, 16);
// 4:1 blocks
MAKE_SMS_ARR_SWITCH_CASE(64, 16);
MAKE_SMS_ARR_SWITCH_CASE(32, 8);
MAKE_SMS_ARR_SWITCH_CASE(16, 4);
#if CONFIG_FLEX_PARTITION
// 1:8 blocks
MAKE_SMS_ARR_SWITCH_CASE(8, 64);
MAKE_SMS_ARR_SWITCH_CASE(4, 32);
// 8:1 blocks
MAKE_SMS_ARR_SWITCH_CASE(64, 8);
MAKE_SMS_ARR_SWITCH_CASE(32, 4);
// 16:1 blocks
MAKE_SMS_ARR_SWITCH_CASE(64, 4);
// 1:16 blocks
MAKE_SMS_ARR_SWITCH_CASE(4, 64);
#endif // CONFIG_FLEX_PARTITION
default: assert(0 && "Invalid bsize"); return NULL;
}
}
#undef MAKE_SMS_ARR_SWITCH_CASE
// Retrieves the SimpleMotionData from SimpleMotionDataBufs
SimpleMotionData *av1_get_sms_data_entry(SimpleMotionDataBufs *sms_bufs,
int mi_row, int mi_col,
BLOCK_SIZE bsize, BLOCK_SIZE sb_size) {
assert(mi_size_high[sb_size] == mi_size_wide[sb_size]);
const int mi_in_sb = mi_size_high[sb_size];
const int mi_row_in_sb = mi_row % mi_in_sb;
const int mi_col_in_sb = mi_col % mi_in_sb;
const int mi_high = mi_size_high[bsize];
const int mi_wide = mi_size_wide[bsize];
const int idx_row_in_sb = get_sms_arr_1d_idx(mi_high, mi_row_in_sb);
const int idx_col_in_sb = get_sms_arr_1d_idx(mi_wide, mi_col_in_sb);
const int arr_stride = get_sms_count_from_length(mi_wide);
SimpleMotionData *sms_arr = get_sms_arr(sms_bufs, bsize);
return &sms_arr[idx_row_in_sb * arr_stride + idx_col_in_sb];
}
void av1_cache_best_partition(SimpleMotionDataBufs *sms_bufs, int mi_row,
int mi_col, BLOCK_SIZE bsize, BLOCK_SIZE sb_size,
PARTITION_TYPE partition) {
SimpleMotionData *cur_block =
av1_get_sms_data_entry(sms_bufs, mi_row, mi_col, bsize, sb_size);
cur_block->has_prev_partition = 1;
cur_block->prev_partition = partition;
}
static AOM_INLINE void compute_sms_txfm_data(
const MACROBLOCK *x, BLOCK_SIZE bsize, const aom_variance_fn_ptr_t *fn_ptr,
int *est_rate, int64_t *dist) {
const MACROBLOCKD *xd = &x->e_mbd;
const uint8_t *src_buf = x->plane[0].src.buf;
const uint8_t *dst_buf = xd->plane[0].dst.buf;
const int src_stride = x->plane[0].src.stride;
const int dst_stride = xd->plane[0].dst.stride;
const int bw = block_size_wide[bsize];
const int bh = block_size_high[bsize];
const int is_hbd = is_cur_buf_hbd(xd);
const int bd = xd->bd;
assert(!is_hbd &&
"high bitdepth sms txfm pipeline has not been implemented yet");
DECLARE_ALIGNED(32, int16_t, src_diff[MAX_SB_SQUARE]);
DECLARE_ALIGNED(32, tran_low_t, coeff[MAX_SB_SQUARE]);
DECLARE_ALIGNED(32, tran_low_t, qcoeff[MAX_SB_SQUARE]);
DECLARE_ALIGNED(32, tran_low_t, dqcoeff[MAX_SB_SQUARE]);
DECLARE_ALIGNED(16, uint8_t, recon[MAX_TX_SQUARE]);
const int diff_stride = bw;
const int recon_stride = MAX_TX_SIZE;
aom_subtract_block(bh, bw, src_diff, diff_stride, src_buf, src_stride,
dst_buf, dst_stride);
const TX_SIZE tx_size = max_txsize_lookup[bsize];
const BLOCK_SIZE tx_bsize = txsize_to_bsize[tx_size];
const int tx_wide = tx_size_wide[tx_size];
const int tx_high = tx_size_high[tx_size];
const int pix_num = av1_get_max_eob(tx_size);
const TX_TYPE tx_type = DCT_DCT;
TxfmParam txfm_param;
txfm_param.tx_type = tx_type;
txfm_param.tx_size = tx_size;
txfm_param.lossless = 0;
txfm_param.bd = bd;
txfm_param.is_hbd = is_hbd;
txfm_param.tx_set_type = EXT_TX_SET_ALL16;
*est_rate = *dist = 0;
for (int row = 0; row < bh; row += tx_high) {
for (int col = 0; col < bw; col += tx_wide) {
const int16_t *cur_diff = src_diff + row * diff_stride + col;
const uint8_t *cur_src = src_buf + row * src_stride + col;
const uint8_t *cur_dst = dst_buf + row * dst_stride + col;
// Txfm
av1_fwd_txfm(cur_diff, coeff, diff_stride, &txfm_param);
// Quantize
const struct macroblock_plane *const p = &x->plane[AOM_PLANE_Y];
const SCAN_ORDER *const scan_order = &av1_default_scan_orders[tx_size];
uint16_t eob = -1;
int cur_est_rate = 0;
av1_quantize_fp(coeff, pix_num, p->zbin_QTX, p->round_fp_QTX,
p->quant_fp_QTX, p->quant_shift_QTX, qcoeff, dqcoeff,
p->dequant_QTX, &eob, scan_order->scan,
scan_order->iscan);
for (int idx = 0; idx < eob; ++idx) {
const int abs_level = abs(qcoeff[scan_order->scan[idx]]);
cur_est_rate += (int)(log(abs_level + 1.0) / log(2.0)) + 1;
}
cur_est_rate <<= AV1_PROB_COST_SHIFT;
*est_rate += cur_est_rate;
// Inverse transform
aom_convolve_copy(cur_dst, dst_stride, recon, recon_stride, tx_wide,
tx_high);
av1_inverse_transform_block(xd, dqcoeff, AOM_PLANE_Y, tx_type, tx_size,
recon, recon_stride, eob, 0);
uint32_t cur_dist = 0;
fn_ptr[tx_bsize].vf(cur_src, src_stride, recon, recon_stride, &cur_dist);
*dist += cur_dist;
}
}
*dist *= 16;
}
// Performs a simple motion search and store the result in sms_data.
static void compute_sms_data(AV1_COMP *const cpi, const TileInfo *const tile,
MACROBLOCK *x, SimpleMotionData *sms_data,
int mi_row, int mi_col, BLOCK_SIZE bsize) {
const AV1_COMMON *const cm = &cpi->common;
const int ref_frame =
cpi->rc.is_src_frame_alt_ref ? ALTREF_FRAME : LAST_FRAME;
if (mi_col >= cm->mi_cols || mi_row >= cm->mi_rows) {
// If the whole block is outside of the image, set the var and sse to 0.
sms_data->sse = 0;
sms_data->var = 0;
sms_data->dist = 0;
sms_data->rate = 0;
sms_data->rdcost = 0;
sms_data->valid = 1;
return;
}
av1_enc_set_offsets(cpi, tile, x, mi_row, mi_col, bsize, NULL);
// We need to update the rd-mult here to in case we are doing simple motion
// search on a subblock of the current coding block.
const int orig_rdmult = x->rdmult;
const AQ_MODE aq_mode = cpi->oxcf.aq_mode;
MB_MODE_INFO *mbmi = x->e_mbd.mi[0];
av1_setup_block_rdmult(cpi, x, mi_row, mi_col, bsize, aq_mode, mbmi);
// Set error per bit for current rdmult
set_error_per_bit(x, x->rdmult);
if (cpi->ref_frame_flags & av1_ref_frame_flag_list[ref_frame]) {
const MACROBLOCKD *xd = &x->e_mbd;
const uint8_t *src_buf = x->plane[0].src.buf;
const uint8_t *dst_buf = xd->plane[0].dst.buf;
const int src_stride = x->plane[0].src.stride;
const int dst_stride = xd->plane[0].dst.stride;
if (sms_data->num_start_mvs == 0) {
sms_data->start_mv_list[sms_data->num_start_mvs++] = kZeroMv;
}
sms_data->rdcost = INT64_MAX;
SimpleMotionData best_data = *sms_data;
for (int idx = 0; idx < sms_data->num_start_mvs; idx++) {
const MV start_mv = sms_data->start_mv_list[idx];
av1_simple_motion_search_ext(cpi, tile, x, mi_row, mi_col, bsize,
ref_frame, start_mv, sms_data);
sms_data->var = cpi->fn_ptr[bsize].vf(src_buf, src_stride, dst_buf,
dst_stride, &sms_data->sse);
sms_data->dist = 16 * sms_data->sse;
sms_data->rate = 0;
if (USE_EST_TXFM) {
compute_sms_txfm_data(x, bsize, cpi->fn_ptr, &sms_data->rate,
&sms_data->dist);
}
sms_data->rdcost = RDCOST(x->rdmult, sms_data->rate, sms_data->dist);
if (sms_data->rdcost <= best_data.rdcost) {
best_data = *sms_data;
}
}
*sms_data = best_data;
}
sms_data->valid = 1;
sms_data->bsize = bsize;
sms_data->mi_row = mi_row;
sms_data->mi_col = mi_col;
x->rdmult = orig_rdmult;
return;
}
#if CONFIG_DEBUG
static INLINE void print_sms(const SimpleMotionData *sms_data, char *prefix) {
BLOCK_SIZE bsize = sms_data->bsize;
MV fullmv = sms_data->fullmv;
MV submv = sms_data->submv;
printf("%s:: bsize: (%d, %d), mi_row: %d, mi_col: %d, rd: %ld\n", prefix,
block_size_wide[bsize], block_size_high[bsize], sms_data->mi_row,
sms_data->mi_col, sms_data->rdcost);
printf("%s:: fullmv: (%d, %d), submv: (%d, %d),\n", prefix, fullmv.row,
fullmv.col, submv.row, submv.col);
printf("%s:: mv_cost_type: %d, sadpb: %d, errpb: %d, mv_prec: %d\n", prefix,
sms_data->mv_cost_type, sms_data->sadpb, sms_data->errorperbit,
sms_data->mv_precision);
}
#endif
static INLINE void add_start_mv_to_block(SimpleMotionData *block, MV start_mv) {
if (block->num_start_mvs == kSMSMaxStartMVs) {
return;
}
for (int idx = 0; idx < block->num_start_mvs; idx++) {
const int_mv *cur_mv = (int_mv *)&block->start_mv_list[idx];
if (((int_mv *)&start_mv)->as_int == cur_mv->as_int) {
return;
}
}
block->start_mv_list[block->num_start_mvs++] = start_mv;
}
static INLINE void add_start_mv_to_partition(
SimpleMotionDataBufs *sms_bufs, int mi_row, int mi_col, BLOCK_SIZE bsize,
BLOCK_SIZE sb_size, PARTITION_TYPE partition, MV start_mv) {
const int quarter_step_h = block_size_high[bsize] / 4;
const int quarter_step_w = block_size_wide[bsize] / 4;
static const int subblock_count[EXT_PARTITION_TYPES] = {
1, // PARTITION_NONE
2, // PARTITION_HORZ
2, // PARTITION_VERT
3, // PARTITION_HORZ_3
3, // PARTITION_VERT_3
};
// PARTITION x NUM_SUBBLOCKS x (ROW and COL)
static const int step_multiplier[EXT_PARTITION_TYPES][3][2] = {
{ { 0, 0 }, { 0, 0 }, { 0, 0 } }, // PARTITION_NONE
{ { 0, 0 }, { 2, 0 }, { 0, 0 } }, // PARTITION_HORZ
{ { 0, 0 }, { 0, 2 }, { 0, 0 } }, // PARTITION_VERT
{ { 0, 0 }, { 1, 0 }, { 3, 0 } }, // PARTITION_HORZ_3
{ { 0, 0 }, { 0, 1 }, { 0, 3 } }, // PARTITION_VERT_3
};
for (int idx = 0; idx < subblock_count[partition]; idx++) {
BLOCK_SIZE subsize = get_partition_subsize(bsize, partition);
if (subsize == BLOCK_INVALID) {
return;
} else if (partition == PARTITION_HORZ_3 && idx == 1) {
subsize = get_partition_subsize(bsize, PARTITION_HORZ);
} else if (partition == PARTITION_VERT_3 && idx == 1) {
subsize = get_partition_subsize(bsize, PARTITION_VERT);
}
const int sub_row =
mi_row + step_multiplier[partition][idx][0] * quarter_step_h / 4;
const int sub_col =
mi_col + step_multiplier[partition][idx][1] * quarter_step_w / 4;
SimpleMotionData *subblock =
av1_get_sms_data_entry(sms_bufs, sub_row, sub_col, subsize, sb_size);
add_start_mv_to_block(subblock, start_mv);
}
}
// Computes and stores the simple motion search data for the block at mi_row,
// mi_col with block size bsize.
SimpleMotionData *av1_get_sms_data(AV1_COMP *const cpi,
const TileInfo *const tile, MACROBLOCK *x,
int mi_row, int mi_col, BLOCK_SIZE bsize) {
const AV1_COMMON *const cm = &cpi->common;
const BLOCK_SIZE sb_size = cm->seq_params.sb_size;
SimpleMotionDataBufs *sms_bufs = x->sms_bufs;
SimpleMotionData *cur_block =
av1_get_sms_data_entry(sms_bufs, mi_row, mi_col, bsize, sb_size);
const int valid = cur_block->valid;
if (!valid) {
compute_sms_data(cpi, tile, x, cur_block, mi_row, mi_col, bsize);
for (PARTITION_TYPE partition = PARTITION_NONE;
partition < EXT_PARTITION_TYPES; partition++) {
add_start_mv_to_partition(sms_bufs, mi_row, mi_col, bsize, sb_size,
partition, cur_block->fullmv);
}
}
return cur_block;
}
PARTITION_TYPE av1_get_prev_partition(AV1_COMP *const cpi, MACROBLOCK *x,
int mi_row, int mi_col,
BLOCK_SIZE bsize) {
const AV1_COMMON *const cm = &cpi->common;
const BLOCK_SIZE sb_size = cm->seq_params.sb_size;
SimpleMotionDataBufs *sms_bufs = x->sms_bufs;
const SimpleMotionData *cur_block =
av1_get_sms_data_entry(sms_bufs, mi_row, mi_col, bsize, sb_size);
if (cur_block->has_prev_partition) {
return cur_block->prev_partition;
} else {
return PARTITION_INVALID;
}
}
static INLINE void gather_part_rd_stats(RD_STATS *rd_stats,
const SMSPartitionStats *stat,
int rdmult) {
av1_init_rd_stats(rd_stats);
if (stat->part_rate < INT_MAX) {
// rd_stats->rate += part_rate;
} else {
rd_stats->rate = INT_MAX;
rd_stats->rdcost = INT64_MAX;
return;
}
for (int idx = 0; idx < stat->num_sub_parts; idx++) {
rd_stats->rate += stat->sms_data[idx]->rate;
rd_stats->dist += stat->sms_data[idx]->dist;
}
rd_stats->rdcost = RDCOST(rdmult, rd_stats->rate, rd_stats->dist);
}
/*! \brief Checks if the average linear dimension of bsize is greater than or
* equal to dim. */
static INLINE int is_avg_dim_greater_than(BLOCK_SIZE bsize, int dim) {
if (bsize == BLOCK_INVALID) {
return 0;
}
const int avg_dim = (block_size_wide[bsize] + block_size_high[bsize]) / 2;
return avg_dim > dim;
}
int av1_prune_new_part(const SMSPartitionStats *old_part,
const SMSPartitionStats *new_part, int rdmult,
BLOCK_SIZE bsize) {
RD_STATS old_rd_stat, new_rd_stat;
gather_part_rd_stats(&old_rd_stat, old_part, rdmult);
gather_part_rd_stats(&new_rd_stat, new_part, rdmult);
if (ENABLE_FAST_RECUR_PARTITION < 2 && is_avg_dim_greater_than(bsize, 32)) {
return old_rd_stat.rdcost < new_rd_stat.rdcost;
}
return old_rd_stat.rdcost < (int)(1.001 * new_rd_stat.rdcost);
}
#endif // CONFIG_EXT_RECUR_PARTITIONS
void av1_get_max_min_partition_size(AV1_COMP *cpi, ThreadData *td,
BLOCK_SIZE *max_sq_size,
BLOCK_SIZE *min_sq_size, int mi_row,
int mi_col) {
const AV1_COMMON *cm = &cpi->common;
MACROBLOCK *x = &td->mb;
const BLOCK_SIZE sb_size = cm->seq_params.sb_size;
switch (cpi->oxcf.max_partition_size) {
case 4: *max_sq_size = BLOCK_4X4; break;
case 8: *max_sq_size = BLOCK_8X8; break;
case 16: *max_sq_size = BLOCK_16X16; break;
case 32: *max_sq_size = BLOCK_32X32; break;
case 64: *max_sq_size = BLOCK_64X64; break;
case 128: *max_sq_size = BLOCK_128X128; break;
default: assert(0); break;
}
*max_sq_size = AOMMIN(*max_sq_size, sb_size);
switch (cpi->oxcf.min_partition_size) {
case 4: *min_sq_size = BLOCK_4X4; break;
case 8: *min_sq_size = BLOCK_8X8; break;
case 16: *min_sq_size = BLOCK_16X16; break;
case 32: *min_sq_size = BLOCK_32X32; break;
case 64: *min_sq_size = BLOCK_64X64; break;
case 128: *min_sq_size = BLOCK_128X128; break;
default: assert(0); break;
}
if (use_auto_max_partition(cpi, sb_size, mi_row, mi_col)) {
float features[FEATURE_SIZE_MAX_MIN_PART_PRED] = { 0.0f };
av1_get_max_min_partition_features(cpi, x, mi_row, mi_col, features);
*max_sq_size =
AOMMIN(av1_predict_max_partition(cpi, x, features), *max_sq_size);
}
*min_sq_size = AOMMIN(*min_sq_size, *max_sq_size);
}
#endif // !CONFIG_REALTIME_ONLY