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/*
* 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 "av1/encoder/encodeframe_utils.h"
#include "config/aom_dsp_rtcd.h"
#include "av1/common/enums.h"
#include "av1/common/reconinter.h"
#if !CONFIG_REALTIME_ONLY
#include "av1/encoder/cnn.h"
#include "av1/encoder/partition_model_weights.h"
#include "av1/encoder/partition_cnn_weights.h"
#endif
#include "av1/encoder/encoder.h"
#include "av1/encoder/motion_search_facade.h"
#include "av1/encoder/partition_strategy.h"
#include "av1/encoder/partition_search.h"
#include "av1/encoder/rdopt.h"
#if !CONFIG_REALTIME_ONLY
static AOM_INLINE 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);
static bool ext_ml_model_decision_before_none(
AV1_COMP *cpi, const float features_from_motion[FEATURE_SIZE_SMS_SPLIT],
int *partition_none_allowed, int *partition_horz_allowed,
int *partition_vert_allowed, int *do_rectangular_split,
int *do_square_split);
static bool ext_ml_model_decision_before_none_part2(
AV1_COMP *cpi,
const float features_from_motion[FEATURE_SIZE_SMS_PRUNE_PART],
int *prune_horz, int *prune_vert);
static bool ext_ml_model_decision_after_none(
ExtPartController *const ext_part_controller, const int is_intra_frame,
const float *const features_after_none, int *do_square_split,
int *do_rectangular_split);
static bool ext_ml_model_decision_after_none_part2(
AV1_COMP *const cpi, const float *const features_terminate,
int *terminate_partition_search);
static bool ext_ml_model_decision_after_split(
AV1_COMP *const cpi, const float *const features_terminate,
int *terminate_partition_search);
static bool ext_ml_model_decision_after_split_part2(
ExtPartController *const ext_part_controller, const int is_intra_frame,
const float *const features_prune, int *prune_rect_part_horz,
int *prune_rect_part_vert);
static bool ext_ml_model_decision_after_rect(
ExtPartController *const ext_part_controller, const int is_intra_frame,
const float *const features_after_rect, int *horza_partition_allowed,
int *horzb_partition_allowed, int *verta_partition_allowed,
int *vertb_partition_allowed);
static bool ext_ml_model_decision_after_part_ab(
AV1_COMP *const cpi, MACROBLOCK *const x, BLOCK_SIZE bsize, int part_ctx,
int64_t best_rd, int64_t rect_part_rd[NUM_RECT_PARTS][SUB_PARTITIONS_RECT],
int64_t split_rd[SUB_PARTITIONS_SPLIT], int *const partition_horz4_allowed,
int *const partition_vert4_allowed, unsigned int pb_source_variance,
int mi_row, int mi_col);
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;
}
}
static char *get_feature_file_name(int id) {
static char *feature_file_names[] = {
"feature_before_partition_none",
"feature_before_partition_none_prune_rect",
"feature_after_partition_none_prune",
"feature_after_partition_none_terminate",
"feature_after_partition_split_terminate",
"feature_after_partition_split_prune_rect",
"feature_after_partition_rect",
"feature_after_partition_ab",
};
return feature_file_names[id];
}
static void write_features_to_file(const char *const path,
const bool is_test_mode,
const float *features,
const int feature_size, const int id,
const int bsize, const int mi_row,
const int mi_col) {
if (!WRITE_FEATURE_TO_FILE && !is_test_mode) return;
char filename[256];
snprintf(filename, sizeof(filename), "%s/%s", path,
get_feature_file_name(id));
FILE *pfile = fopen(filename, "a");
if (!is_test_mode) {
fprintf(pfile, "%d,%d,%d,%d,%d\n", id, bsize, mi_row, mi_col, feature_size);
}
for (int i = 0; i < feature_size; ++i) {
fprintf(pfile, "%.6f", features[i]);
if (i < feature_size - 1) fprintf(pfile, ",");
}
fprintf(pfile, "\n");
fclose(pfile);
}
// 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 quad_tree_idx,
int intra_cnn_based_part_prune_level,
PartitionSearchState *part_state) {
assert(cm->seq_params->sb_size >= BLOCK_64X64 &&
"Invalid sb_size for intra_cnn!");
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const BLOCK_SIZE bsize = blk_params->bsize;
const int bsize_idx = convert_bsize_to_idx(bsize);
if (bsize == BLOCK_128X128) {
return;
}
PartitionSearchInfo *part_info = &x->part_search_info;
// Precompute the CNN part and cache the result in MACROBLOCK
if (bsize == BLOCK_64X64 && !part_info->cnn_output_valid) {
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 = part_info->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, bit_depth) >> (bit_depth - 8);
part_info->log_q = logf(1.0f + (float)(dc_q * dc_q) / 256.0f);
part_info->log_q =
(part_info->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);
}
part_info->cnn_output_valid = 1;
}
if (!part_info->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];
float dnn_features[100];
float logits[4] = { 0.0f };
const float *branch_0 = part_info->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++] = part_info->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++] = part_info->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++] = part_info->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++] = part_info->log_q;
} else {
assert(0 && "Invalid bsize in intra_cnn partition");
}
// Make decision
av1_nn_predict(dnn_features, dnn_config, 1, logits);
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) {
// As screen contents tend to choose larger partitions, do not prune
// PARTITION_NONE when intra_cnn_based_part_prune_level=1.
if (intra_cnn_based_part_prune_level != 1) {
part_state->partition_none_allowed = 0;
}
part_state->do_square_split = 1;
av1_disable_rect_partitions(part_state);
}
if (logits[0] < no_split_thresh) {
av1_disable_square_split_partition(part_state);
}
}
void av1_simple_motion_search_based_split(AV1_COMP *const cpi, MACROBLOCK *x,
SIMPLE_MOTION_DATA_TREE *sms_tree,
PartitionSearchState *part_state) {
const AV1_COMMON *const cm = &cpi->common;
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const int mi_row = blk_params->mi_row, mi_col = blk_params->mi_col;
const BLOCK_SIZE bsize = blk_params->bsize;
const int bsize_idx = convert_bsize_to_idx(bsize);
const int is_720p_or_larger = AOMMIN(cm->width, cm->height) >= 720;
const int is_480p_or_larger = AOMMIN(cm->width, cm->height) >= 480;
// res_idx is 0 for res < 480p, 1 for 480p, 2 for 720p+
const int res_idx = is_480p_or_larger + is_720p_or_larger;
assert(bsize_idx >= 0 && bsize_idx <= 4 &&
"Invalid bsize in simple_motion_search_based_split");
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];
const int agg = cpi->sf.part_sf.simple_motion_search_prune_agg;
if (agg < 0) {
return;
}
const float split_only_thresh =
av1_simple_motion_search_split_thresh[agg][res_idx][bsize_idx];
const float no_split_thresh =
av1_simple_motion_search_no_split_thresh[agg][res_idx][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);
// Write features to file
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features,
FEATURE_SIZE_SMS_SPLIT, 0, bsize, mi_row, mi_col);
// Note: it is intended to not normalize the features here, to keep it
// consistent for all features collected and passed to the external model.
if (ext_ml_model_decision_before_none(
cpi, features, &part_state->partition_none_allowed,
&part_state->partition_rect_allowed[HORZ],
&part_state->partition_rect_allowed[VERT],
&part_state->do_rectangular_split, &part_state->do_square_split)) {
return;
}
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);
if (score > split_only_thresh) {
av1_set_square_split_only(part_state);
}
if (cpi->sf.part_sf.simple_motion_search_split >= 2 &&
score < no_split_thresh) {
av1_disable_square_split_partition(part_state);
}
// If the score is very low, prune rectangular split since it is unlikely to
// occur.
if (cpi->sf.part_sf.simple_motion_search_rect_split) {
const float scale = res_idx >= 2 ? 3.0f : 2.0f;
const float rect_split_thresh =
scale * av1_simple_motion_search_no_split_thresh
[cpi->sf.part_sf.simple_motion_search_rect_split][res_idx]
[bsize_idx];
if (score < rect_split_thresh) {
part_state->do_rectangular_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_params.mi_cols || mi_row >= cm->mi_params.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 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]) {
const FULLPEL_MV *start_mvs = sms_tree->start_mvs;
unsigned int curr_sse = 0, curr_var = 0;
int_mv best_mv =
av1_simple_motion_search(cpi, x, mi_row, mi_col, bsize, ref,
start_mvs[ref], num_planes, use_subpixel);
curr_var = cpi->ppi->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) {
sms_tree->start_mvs[ref].row = best_mv.as_mv.row / 8;
sms_tree->start_mvs[ref].col = best_mv.as_mv.col / 8;
if (bsize >= BLOCK_8X8) {
for (int r_idx = 0; r_idx < SUB_PARTITIONS_SPLIT; r_idx++) {
// Propagate the new motion vectors to a lower level
SIMPLE_MOTION_DATA_TREE *sub_tree = sms_tree->split[r_idx];
sub_tree->start_mvs[ref] = sms_tree->start_mvs[ref];
}
}
}
}
}
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 AOM_INLINE 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(bsize >= BLOCK_8X8);
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 < SUB_PARTITIONS_SPLIT; 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 < SUB_PARTITIONS_RECT; 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 < SUB_PARTITIONS_RECT; 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;
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 < SUB_PARTITIONS_SPLIT; 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]);
}
}
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, xd->bd) >> (xd->bd - 8);
features[f_idx++] = logf(1.0f + (float)(dc_q * dc_q) / 256.0f);
// 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->bsize : bsize;
const BLOCK_SIZE left_bsize = has_left ? xd->left_mbmi->bsize : 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_rect(AV1_COMP *const cpi, MACROBLOCK *x,
SIMPLE_MOTION_DATA_TREE *sms_tree,
PartitionSearchState *part_state) {
const AV1_COMMON *const cm = &cpi->common;
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const int mi_row = blk_params->mi_row, mi_col = blk_params->mi_col;
const BLOCK_SIZE bsize = blk_params->bsize;
const int bsize_idx = convert_bsize_to_idx(bsize);
const int is_720p_or_larger = AOMMIN(cm->width, cm->height) >= 720;
const int is_480p_or_larger = AOMMIN(cm->width, cm->height) >= 480;
// res_idx is 0 for lowres, 1 for 48p, 2 for 720p+
const int res_idx = is_480p_or_larger + is_720p_or_larger;
// Get model parameters
const NN_CONFIG *nn_config =
av1_simple_motion_search_prune_rect_nn_config[bsize_idx];
const float *ml_mean = av1_simple_motion_search_prune_rect_mean[bsize_idx],
*ml_std = av1_simple_motion_search_prune_rect_std[bsize_idx];
const int agg = cpi->sf.part_sf.simple_motion_search_prune_agg;
if (agg < 0) {
return;
}
const float prune_thresh =
av1_simple_motion_search_prune_rect_thresh[agg][res_idx][bsize_idx];
// If there is no valid threshold, return immediately.
if (!nn_config || prune_thresh == 0.0f) {
return;
}
// Get features
float features[FEATURE_SIZE_SMS_PRUNE_PART] = { 0.0f };
simple_motion_search_prune_part_features(cpi, x, sms_tree, mi_row, mi_col,
bsize, features,
FEATURE_SMS_PRUNE_PART_FLAG);
// Note: it is intended to not normalize the features here, to keep it
// consistent for all features collected and passed to the external model.
if (cpi->sf.part_sf.simple_motion_search_prune_rect &&
!frame_is_intra_only(cm) &&
(part_state->partition_rect_allowed[HORZ] ||
part_state->partition_rect_allowed[VERT]) &&
bsize >= BLOCK_8X8 && !av1_superres_scaled(cm)) {
// Write features to file
write_features_to_file(
cpi->oxcf.partition_info_path, cpi->ext_part_controller.test_mode,
features, FEATURE_SIZE_SMS_PRUNE_PART, 1, bsize, mi_row, mi_col);
if (ext_ml_model_decision_before_none_part2(
cpi, features, &part_state->prune_rect_part[HORZ],
&part_state->prune_rect_part[VERT])) {
return;
}
}
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);
av1_nn_softmax(scores, probs, num_classes);
// Determine if we should prune rectangular partitions.
if (probs[PARTITION_HORZ] <= prune_thresh) {
part_state->prune_rect_part[HORZ] = 1;
}
if (probs[PARTITION_VERT] <= prune_thresh) {
part_state->prune_rect_part[VERT] = 1;
}
}
// 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,
const RD_STATS *none_rdc, PartitionSearchState *part_state) {
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const int mi_row = blk_params->mi_row, mi_col = blk_params->mi_col;
const BLOCK_SIZE bsize = blk_params->bsize;
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");
}
// Write features to file
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features,
FEATURE_SIZE_SMS_TERM_NONE, 3, bsize, mi_row, mi_col);
if (ext_ml_model_decision_after_none_part2(
cpi, features, &part_state->terminate_partition_search)) {
return;
}
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) {
part_state->terminate_partition_search = 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;
// Currently this only allows 128X128 SB size. May extend it to 64X64 SB size.
assert(sb_size == BLOCK_128X128);
int f_idx = 0;
const int dc_q = av1_dc_quant_QTX(x->qindex, 0, xd->bd) >> (xd->bd - 8);
const float log_q_sq = logf(1.0f + (float)(dc_q * dc_q) / 256.0f);
// 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 FULLPEL_MV start_mv = kZeroFullMv;
int_mv best_mv = av1_simple_motion_sse_var(
cpi, x, this_mi_row, this_mi_col, mb_size, start_mv, 0, &sse, &var);
const float mv_row = (float)(best_mv.as_mv.row / 8);
const float mv_col = (float)(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;
}
const int blks = mb_rows * mb_cols;
const float avg_mv_row = sum_mv_row / (float)blks;
const float var_mv_row =
sum_mv_row_sq / (float)blks - avg_mv_row * avg_mv_row;
const float avg_mv_col = sum_mv_col / (float)blks;
const float var_mv_col =
sum_mv_col_sq / (float)blks - avg_mv_col * avg_mv_col;
const float avg_log_sse = sum_log_sse / (float)blks;
const float var_log_sse =
sum_log_sse_sq / (float)blks - 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);
}
// Convert result index to block size.
// result idx block size
// 0 BLOCK_16X16
// 1 BLOCK_32X32
// 2 BLOCK_64X64
// 3 BLOCK_128X128
static BLOCK_SIZE get_block_size(int idx) {
return (BLOCK_SIZE)((idx + 2) * 3);
}
BLOCK_SIZE av1_predict_max_partition(const AV1_COMP *const cpi,
const MACROBLOCK *const x,
const float *features) {
float scores[MAX_NUM_CLASSES_MAX_MIN_PART_PRED] = { 0.0f };
const NN_CONFIG *nn_config = &av1_max_part_pred_nn_config;
assert(cpi->sf.part_sf.auto_max_partition_based_on_simple_motion !=
NOT_IN_USE);
av1_nn_predict(features, nn_config, 1, scores);
int result = MAX_NUM_CLASSES_MAX_MIN_PART_PRED - 1;
if (cpi->sf.part_sf.auto_max_partition_based_on_simple_motion ==
DIRECT_PRED) {
result = 0;
float max_score = scores[0];
for (int i = 1; i < MAX_NUM_CLASSES_MAX_MIN_PART_PRED; ++i) {
if (scores[i] > max_score) {
max_score = scores[i];
result = i;
}
}
return get_block_size(result);
}
float probs[MAX_NUM_CLASSES_MAX_MIN_PART_PRED] = { 0.0f };
av1_nn_softmax(scores, probs, MAX_NUM_CLASSES_MAX_MIN_PART_PRED);
if (cpi->sf.part_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.part_sf.auto_max_partition_based_on_simple_motion ==
ADAPT_PRED) {
const BLOCK_SIZE sb_size = cpi->common.seq_params->sb_size;
const 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 get_block_size(result);
}
// Get the minimum partition block width and height(in log scale) under a
// SIMPLE_MOTION_DATA_TREE.
static AOM_INLINE 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 < SUB_PARTITIONS_SPLIT; ++i) {
get_min_bsize(sms_tree->split[i], min_bw, min_bh);
}
} else {
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;
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,
int64_t best_rd, int64_t part_none_rd,
int64_t part_split_rd,
int64_t *split_block_rd,
PartitionSearchState *part_state) {
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const int mi_row = blk_params->mi_row, mi_col = blk_params->mi_col;
const BLOCK_SIZE bsize = blk_params->bsize;
if (best_rd <= 0 || best_rd == INT64_MAX ||
part_state->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.part_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, xd->bd) >> (xd->bd - 8);
const int bs = block_size_wide[bsize];
int f_idx = 0;
float features[FEATURES] = { 0.0f };
features[f_idx++] = logf(1.0f + (float)dc_q / 4.0f);
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 < SUB_PARTITIONS_SPLIT; ++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);
// Write features to file
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features, FEATURES,
4, bsize, mi_row, mi_col);
if (ext_ml_model_decision_after_split(
cpi, features, &part_state->terminate_partition_search)) {
return;
}
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) {
part_state->terminate_partition_search = 1;
}
}
#undef FEATURES
void av1_ml_prune_rect_partition(AV1_COMP *const cpi, const MACROBLOCK *const x,
int64_t best_rd, int64_t none_rd,
const int64_t *split_rd,
PartitionSearchState *part_state) {
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const int mi_row = blk_params->mi_row, mi_col = blk_params->mi_col;
const BLOCK_SIZE bsize = blk_params->bsize;
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;
// 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 < SUB_PARTITIONS_SPLIT; 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[SUB_PARTITIONS_SPLIT];
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 < SUB_PARTITIONS_SPLIT; ++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 < SUB_PARTITIONS_SPLIT; i++)
features[5 + i] = (float)split_variance[i] / (float)whole_block_variance;
// Write features to file
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features,
/*feature_size=*/9, 5, bsize, mi_row, mi_col);
if (ext_ml_model_decision_after_split_part2(
&cpi->ext_part_controller, frame_is_intra_only(&cpi->common),
features, &part_state->prune_rect_part[HORZ],
&part_state->prune_rect_part[VERT])) {
return;
}
// 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);
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) part_state->prune_rect_part[HORZ] = 1;
if (probs[2] <= cur_thresh) part_state->prune_rect_part[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(AV1_COMP *const cpi, int part_ctx, int var_ctx,
int64_t best_rd,
PartitionSearchState *part_state,
int *ab_partitions_allowed) {
const PartitionBlkParams blk_params = part_state->part_blk_params;
const int mi_row = blk_params.mi_row;
const int mi_col = blk_params.mi_col;
const int bsize = blk_params.bsize;
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;
// 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 < SUB_PARTITIONS_RECT; ++i) {
const int64_t *horz_rd = part_state->rect_part_rd[HORZ];
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 < SUB_PARTITIONS_RECT; ++i) {
const int64_t *vert_rd = part_state->rect_part_rd[VERT];
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 < SUB_PARTITIONS_SPLIT; ++i) {
const int64_t *split_rd = part_state->split_rd;
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);
// Write features to file
if (!frame_is_intra_only(&cpi->common)) {
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features,
/*feature_size=*/10, 6, bsize, mi_row, mi_col);
}
if (ext_ml_model_decision_after_rect(
&cpi->ext_part_controller, frame_is_intra_only(&cpi->common),
features, &ab_partitions_allowed[HORZ_A],
&ab_partitions_allowed[HORZ_B], &ab_partitions_allowed[VERT_A],
&ab_partitions_allowed[VERT_B])) {
return;
}
// Calculate scores using the NN model.
float score[16] = { 0.0f };
av1_nn_predict(features, nn_config, 1, score);
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;
}
av1_zero_array(ab_partitions_allowed, NUM_AB_PARTS);
for (int i = 0; i < 16; ++i) {
if (int_score[i] >= thresh) {
if ((i >> 0) & 1) ab_partitions_allowed[HORZ_A] = 1;
if ((i >> 1) & 1) ab_partitions_allowed[HORZ_B] = 1;
if ((i >> 2) & 1) ab_partitions_allowed[VERT_A] = 1;
if ((i >> 3) & 1) ab_partitions_allowed[VERT_B] = 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(AV1_COMP *const cpi, MACROBLOCK *const x,
int part_ctx, int64_t best_rd,
PartitionSearchState *part_state,
int *part4_allowed,
unsigned int pb_source_variance) {
const PartitionBlkParams blk_params = part_state->part_blk_params;
const int mi_row = blk_params.mi_row;
const int mi_col = blk_params.mi_col;
const int bsize = blk_params.bsize;
int64_t(*rect_part_rd)[SUB_PARTITIONS_RECT] = part_state->rect_part_rd;
int64_t *split_rd = part_state->split_rd;
if (ext_ml_model_decision_after_part_ab(
cpi, x, bsize, part_ctx, best_rd, rect_part_rd, split_rd,
&part4_allowed[HORZ4], &part4_allowed[VERT4], pb_source_variance,
mi_row, mi_col))
return;
if (best_rd >= 1000000000) return;
int64_t *horz_rd = rect_part_rd[HORZ4];
int64_t *vert_rd = rect_part_rd[VERT4];
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;
// 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 < SUB_PARTITIONS_RECT; ++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 < SUB_PARTITIONS_RECT; ++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 < SUB_PARTITIONS_SPLIT; ++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[SUB_PARTITIONS_PART4] = { 0 };
unsigned int vert_4_source_var[SUB_PARTITIONS_PART4] = { 0 };
{
BLOCK_SIZE horz_4_bs = get_partition_subsize(bsize, PARTITION_HORZ_4);
BLOCK_SIZE vert_4_bs = get_partition_subsize(bsize, PARTITION_VERT_4);
av1_setup_src_planes(x, cpi->source, mi_row, mi_col,
av1_num_planes(&cpi->common), bsize);
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 < SUB_PARTITIONS_PART4; ++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 < SUB_PARTITIONS_PART4; ++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 < SUB_PARTITIONS_PART4; ++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);
// Write features to file
if (!frame_is_intra_only(&cpi->common)) {
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features,
FEATURES, 7, bsize, mi_row, mi_col);
}
// Calculate scores using the NN model.
float score[LABELS] = { 0.0f };
av1_nn_predict(features, nn_config, 1, score);
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;
}
av1_zero_array(part4_allowed, NUM_PART4_TYPES);
for (int i = 0; i < LABELS; ++i) {
if (int_score[i] >= thresh) {
if ((i >> 0) & 1) part4_allowed[HORZ4] = 1;
if ((i >> 1) & 1) part4_allowed[VERT4] = 1;
}
}
}
#undef FEATURES
#undef LABELS
#define FEATURES 4
void av1_ml_predict_breakout(AV1_COMP *const cpi, const MACROBLOCK *const x,
const RD_STATS *const rd_stats,
unsigned int pb_source_variance, int bit_depth,
PartitionSearchState *part_state) {
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const int mi_row = blk_params->mi_row, mi_col = blk_params->mi_col;
const BLOCK_SIZE bsize = blk_params->bsize;
const NN_CONFIG *nn_config = NULL;
int thresh = 0;
switch (bsize) {
case BLOCK_8X8:
nn_config = &av1_partition_breakout_nnconfig_8;
thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[0];
break;
case BLOCK_16X16:
nn_config = &av1_partition_breakout_nnconfig_16;
thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[1];
break;
case BLOCK_32X32:
nn_config = &av1_partition_breakout_nnconfig_32;
thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[2];
break;
case BLOCK_64X64:
nn_config = &av1_partition_breakout_nnconfig_64;
thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[3];
break;
case BLOCK_128X128:
nn_config = &av1_partition_breakout_nnconfig_128;
thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[4];
break;
default: assert(0 && "Unexpected bsize.");
}
if (!nn_config || thresh < 0) return;
const float ml_predict_breakout_thresh_scale[3] = { 1.15f, 1.05f, 1.0f };
thresh = (int)((float)thresh *
ml_predict_breakout_thresh_scale
[cpi->sf.part_sf.ml_predict_breakout_level - 1]);
// Generate feature values.
float features[FEATURES];
int feature_index = 0;
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] >> (bit_depth - 8);
features[feature_index++] = (float)(dc_q * dc_q) / 256.0f;
assert(feature_index == FEATURES);
// Write features to file
write_features_to_file(cpi->oxcf.partition_info_path,
cpi->ext_part_controller.test_mode, features, FEATURES,
2, bsize, mi_row, mi_col);
if (ext_ml_model_decision_after_none(&cpi->ext_part_controller,
frame_is_intra_only(&cpi->common),
features, &part_state->do_square_split,
&part_state->do_rectangular_split)) {
return;
}
// Calculate score using the NN model.
float score = 0.0f;
av1_nn_predict(features, nn_config, 1, &score);
// Make decision.
if ((int)(score * 100) >= thresh) {
part_state->do_square_split = 0;
part_state->do_rectangular_split = 0;
}
}
#undef FEATURES
void av1_prune_partitions_before_search(AV1_COMP *const cpi,
MACROBLOCK *const x,
SIMPLE_MOTION_DATA_TREE *const sms_tree,
PartitionSearchState *part_state) {
const AV1_COMMON *const cm = &cpi->common;
const CommonModeInfoParams *const mi_params = &cm->mi_params;
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const BLOCK_SIZE bsize = blk_params->bsize;
// Prune rectangular, AB and 4-way partition based on q index and block size
if (cpi->sf.part_sf.prune_rectangular_split_based_on_qidx) {
// Enumeration difference between two square partitions
const int sqr_bsize_step = BLOCK_32X32 - BLOCK_16X16;
int max_bsize =
BLOCK_32X32 - (x->qindex * 3 / QINDEX_RANGE) * sqr_bsize_step;
max_bsize = AOMMAX(max_bsize, BLOCK_4X4);
const BLOCK_SIZE max_prune_bsize =
(BLOCK_SIZE)AOMMIN(max_bsize, BLOCK_32X32);
// Prune partition
// qidx 0 to 85: prune bsize below BLOCK_32X32
// qidx 86 to 170: prune bsize below BLOCK_16X16
// qidx 171 to 255: prune bsize below BLOCK_8X8
if (bsize < max_prune_bsize) {
av1_disable_rect_partitions(part_state);
}
}
if (cpi->sf.part_sf.prune_sub_8x8_partition_level && (bsize == BLOCK_8X8)) {
const MACROBLOCKD *const xd = &x->e_mbd;
int prune_sub_8x8 = 1;
if (cpi->sf.part_sf.prune_sub_8x8_partition_level == 1) {
int num_neighbors_lt_8x8 = 0;
if (xd->left_available)
num_neighbors_lt_8x8 += (xd->left_mbmi->bsize <= BLOCK_8X8);
if (xd->up_available)
num_neighbors_lt_8x8 += (xd->above_mbmi->bsize <= BLOCK_8X8);
// Evaluate only if both left and above blocks are of size <= BLOCK_8X8.
if (num_neighbors_lt_8x8 == 2) {
prune_sub_8x8 = 0;
}
}
if (prune_sub_8x8) {
av1_disable_all_splits(part_state);
}
}
// A CNN-based speed feature pruning out either split or all non-split
// partition in INTRA frame coding.
const int try_intra_cnn_based_part_prune =
frame_is_intra_only(cm) &&
cpi->sf.part_sf.intra_cnn_based_part_prune_level &&
cm->seq_params->sb_size >= BLOCK_64X64 && bsize <= BLOCK_64X64 &&
blk_params->bsize_at_least_8x8 &&
av1_is_whole_blk_in_frame(blk_params, mi_params);
if (try_intra_cnn_based_part_prune) {
av1_intra_mode_cnn_partition(
&cpi->common, x, x->part_search_info.quad_tree_idx,
cpi->sf.part_sf.intra_cnn_based_part_prune_level, part_state);
}
// Use simple motion search to prune out split or non-split partitions. This
// must be done prior to PARTITION_SPLIT to propagate the initial mvs to a
// smaller blocksize.
const int try_split_only =
cpi->sf.part_sf.simple_motion_search_split &&
part_state->do_square_split && blk_params->bsize_at_least_8x8 &&
av1_is_whole_blk_in_frame(blk_params, mi_params) &&
!frame_is_intra_only(cm) && !av1_superres_scaled(cm);
if (try_split_only) {
av1_simple_motion_search_based_split(cpi, x, sms_tree, part_state);
}
// Use simple motion search to prune out rectangular partition in some
// direction. The results are stored in prune_horz and prune_vert in order to
// bypass future related pruning checks if a pruning decision has been made.
// We want to search at least one partition mode, so don't prune if NONE and
// SPLIT are disabled.
const int non_rect_part_allowed =
part_state->do_square_split || part_state->partition_none_allowed;
// Only run the model if the partitions are not already pruned.
const int rect_part_allowed = part_state->do_rectangular_split &&
((part_state->partition_rect_allowed[HORZ] &&
!part_state->prune_rect_part[HORZ]) ||
(part_state->partition_rect_allowed[VERT] &&
!part_state->prune_rect_part[VERT]));
const int try_prune_rect = cpi->sf.part_sf.simple_motion_search_prune_rect &&
!frame_is_intra_only(cm) &&
non_rect_part_allowed && rect_part_allowed &&
!av1_superres_scaled(cm);
if (try_prune_rect) {
av1_simple_motion_search_prune_rect(cpi, x, sms_tree, part_state);
}
}
#ifndef NDEBUG
static AOM_INLINE int is_bsize_square(BLOCK_SIZE bsize) {
return block_size_wide[bsize] == block_size_high[bsize];
}
#endif // NDEBUG
void av1_prune_partitions_by_max_min_bsize(SuperBlockEnc *sb_enc,
PartitionSearchState *part_state) {
assert(is_bsize_square(sb_enc->max_partition_size));
assert(is_bsize_square(sb_enc->min_partition_size));
assert(sb_enc->min_partition_size <= sb_enc->max_partition_size);
const PartitionBlkParams *blk_params = &part_state->part_blk_params;
const BLOCK_SIZE bsize = blk_params->bsize;
assert(is_bsize_square(bsize));
const int max_partition_size_1d = block_size_wide[sb_enc->max_partition_size];
const int min_partition_size_1d = block_size_wide[sb_enc->min_partition_size];
const int bsize_1d = block_size_wide[bsize];
assert(min_partition_size_1d <= max_partition_size_1d);
const int is_le_min_sq_part = bsize_1d <= min_partition_size_1d;
const int is_gt_max_sq_part = bsize_1d > max_partition_size_1d;
if (is_gt_max_sq_part) {
// If current block size is larger than max, only allow split.
av1_set_square_split_only(part_state);
} else if (is_le_min_sq_part) {
// If current block size is less or equal to min, only allow none if valid
// block large enough; only allow split otherwise.
av1_disable_rect_partitions(part_state);
// only disable square split when current block is not at the picture
// boundary. otherwise, inherit the square split flag from previous logic
if (av1_blk_has_rows_and_cols(blk_params)) {
part_state->do_square_split = 0;
}
part_state->partition_none_allowed = !(part_state->do_square_split);
}
}
// Decide whether to evaluate the AB partition specified by part_type based on
// split and HORZ/VERT info
int evaluate_ab_partition_based_on_split(
const PC_TREE *pc_tree, PARTITION_TYPE rect_part,
const RD_RECT_PART_WIN_INFO *rect_part_win_info, int qindex, int split_idx1,
int split_idx2) {
int num_win = 0;
// Threshold for number of winners
// Conservative pruning for high quantizers
const int num_win_thresh = AOMMIN(3 * (2 * (MAXQ - qindex) / MAXQ), 3);
int sub_part_win = (rect_part_win_info == NULL)
? (pc_tree->partitioning == rect_part)
: (rect_part == PARTITION_HORZ)
? rect_part_win_info->rect_part_win[HORZ]
: rect_part_win_info->rect_part_win[VERT];
num_win += (sub_part_win) ? 1 : 0;
if (pc_tree->split[split_idx1]) {
num_win +=
(pc_tree->split[split_idx1]->partitioning == PARTITION_NONE) ? 1 : 0;
} else {
num_win += 1;
}
if (pc_tree->split[split_idx2]) {
num_win +=
(pc_tree->split[split_idx2]->partitioning == PARTITION_NONE) ? 1 : 0;
} else {
num_win += 1;
}
if (num_win < num_win_thresh) {
return 0;
}
return 1;
}
void av1_prune_ab_partitions(AV1_COMP *cpi, const MACROBLOCK *x,
const PC_TREE *pc_tree, int pb_source_variance,
int64_t best_rdcost,
const RD_RECT_PART_WIN_INFO *rect_part_win_info,
bool ext_partition_allowed,
PartitionSearchState *part_state,
int *ab_partitions_allowed) {
int64_t *horz_rd = part_state->rect_part_rd[HORZ];
int64_t *vert_rd = part_state->rect_part_rd[VERT];
int64_t *split_rd = part_state->split_rd;
const PartitionCfg *const part_cfg = &cpi->oxcf.part_cfg;
// The standard AB partitions are allowed initially if ext-partition-types are
// allowed.
int horzab_partition_allowed = ext_partition_allowed &&
part_cfg->enable_ab_partitions &&
part_state->partition_rect_allowed[HORZ];
int vertab_partition_allowed = ext_partition_allowed &&
part_cfg->enable_ab_partitions &&
part_state->partition_rect_allowed[VERT];
// Pruning: pruning out AB partitions on one main direction based on the
// current best partition and source variance.
if (cpi->sf.part_sf.prune_ext_partition_types_search_level) {
if (cpi->sf.part_sf.prune_ext_partition_types_search_level == 1) {
// TODO(debargha,huisu@google.com): may need to tune the threshold for
// pb_source_variance.
horzab_partition_allowed &= (pc_tree->partitioning == PARTITION_HORZ ||
(pc_tree->partitioning == PARTITION_NONE &&
pb_source_variance < 32) ||
pc_tree->partitioning == PARTITION_SPLIT);
vertab_partition_allowed &= (pc_tree->partitioning == PARTITION_VERT ||
(pc_tree->partitioning == PARTITION_NONE &&
pb_source_variance < 32) ||
pc_tree->partitioning == PARTITION_SPLIT);
} else {
horzab_partition_allowed &= (pc_tree->partitioning == PARTITION_HORZ ||
pc_tree->partitioning == PARTITION_SPLIT);
vertab_partition_allowed &= (pc_tree->partitioning == PARTITION_VERT ||
pc_tree->partitioning == PARTITION_SPLIT);
}
horz_rd[0] = (horz_rd[0] < INT64_MAX ? horz_rd[0] : 0);
horz_rd[1] = (horz_rd[1] < INT64_MAX ? horz_rd[1] : 0);
vert_rd[0] = (vert_rd[0] < INT64_MAX ? vert_rd[0] : 0);
vert_rd[1] = (vert_rd[1] < INT64_MAX ? vert_rd[1] : 0);
split_rd[0] = (split_rd[0] < INT64_MAX ? split_rd[0] : 0);
split_rd[1] = (split_rd[1] < INT64_MAX ? split_rd[1] : 0);
split_rd[2] = (split_rd[2] < INT64_MAX ? split_rd[2] : 0);
split_rd[3] = (split_rd[3] < INT64_MAX ? split_rd[3] : 0);
}
// Pruning: pruning out horz_a or horz_b if the combined rdcost of its
// subblocks estimated from previous partitions is much higher than the best
// rd so far.
ab_partitions_allowed[HORZ_A] = horzab_partition_allowed;
ab_partitions_allowed[HORZ_B] = horzab_partition_allowed;
if (cpi->sf.part_sf.prune_ext_partition_types_search_level) {
const int64_t horz_a_rd = horz_rd[1] + split_rd[0] + split_rd[1];
const int64_t horz_b_rd = horz_rd[0] + split_rd[2] + split_rd[3];
switch (cpi->sf.part_sf.prune_ext_partition_types_search_level) {
case 1:
ab_partitions_allowed[HORZ_A] &= (horz_a_rd / 16 * 14 < best_rdcost);
ab_partitions_allowed[HORZ_B] &= (horz_b_rd / 16 * 14 < best_rdcost);
break;
case 2:
default:
ab_partitions_allowed[HORZ_A] &= (horz_a_rd / 16 * 15 < best_rdcost);
ab_partitions_allowed[HORZ_B] &= (horz_b_rd / 16 * 15 < best_rdcost);
break;
}
}
// Pruning: pruning out vert_a or vert_b if the combined rdcost of its
// subblocks estimated from previous partitions is much higher than the best
// rd so far.
ab_partitions_allowed[VERT_A] = vertab_partition_allowed;
ab_partitions_allowed[VERT_B] = vertab_partition_allowed;
if (cpi->sf.part_sf.prune_ext_partition_types_search_level) {
const int64_t vert_a_rd = vert_rd[1] + split_rd[0] + split_rd[2];
const int64_t vert_b_rd = vert_rd[0] + split_rd[1] + split_rd[3];
switch (cpi->sf.part_sf.prune_ext_partition_types_search_level) {
case 1:
ab_partitions_allowed[VERT_A] &= (vert_a_rd / 16 * 14 < best_rdcost);
ab_partitions_allowed[VERT_B] &= (vert_b_rd / 16 * 14 < best_rdcost);
break;
case 2:
default:
ab_partitions_allowed[VERT_A] &= (vert_a_rd / 16 * 15 < best_rdcost);
ab_partitions_allowed[VERT_B] &= (vert_b_rd / 16 * 15 < best_rdcost);
break;
}
}
// Pruning: pruning out some ab partitions using a DNN taking rd costs of
// sub-blocks from previous basic partition types.
if (cpi->sf.part_sf.ml_prune_partition && ext_partition_allowed &&
part_state->partition_rect_allowed[HORZ] &&
part_state->partition_rect_allowed[VERT]) {
// TODO(huisu@google.com): x->source_variance may not be the current
// block's variance. The correct one to use is pb_source_variance. Need to
// re-train the model to fix it.
av1_ml_prune_ab_partition(cpi, pc_tree->partitioning,
get_unsigned_bits(x->source_variance),
best_rdcost, part_state, ab_partitions_allowed);
}
// Pruning: pruning AB partitions based on the number of horz/vert wins
// in the current block and sub-blocks in PARTITION_SPLIT.
if (cpi->sf.part_sf.prune_ext_part_using_split_info >= 2 &&
ab_partitions_allowed[HORZ_A]) {
ab_partitions_allowed[HORZ_A] &= evaluate_ab_partition_based_on_split(
pc_tree, PARTITION_HORZ, rect_part_win_info, x->qindex, 0, 1);
}
if (cpi->sf.part_sf.prune_ext_part_using_split_info >= 2 &&
ab_partitions_allowed[HORZ_B]) {
ab_partitions_allowed[HORZ_B] &= evaluate_ab_partition_based_on_split(
pc_tree, PARTITION_HORZ, rect_part_win_info, x->qindex, 2, 3);
}
if (cpi->sf.part_sf.prune_ext_part_using_split_info >= 2 &&
ab_partitions_allowed[VERT_A]) {
ab_partitions_allowed[VERT_A] &= evaluate_ab_partition_based_on_split(
pc_tree, PARTITION_VERT, rect_part_win_info, x->qindex, 0, 2);
}
if (cpi->sf.part_sf.prune_ext_part_using_split_info >= 2 &&
ab_partitions_allowed[VERT_B]) {
ab_partitions_allowed[VERT_B] &= evaluate_ab_partition_based_on_split(
pc_tree, PARTITION_VERT, rect_part_win_info, x->qindex, 1, 3);
}
}
// Prepare features for the external model. Specifically, features after
// ab partition is searched.
static void prepare_features_after_part_ab(
const AV1_COMP *const cpi, MACROBLOCK *const x, BLOCK_SIZE bsize,
int part_ctx, int64_t best_rd,
int64_t rect_part_rd[NUM_RECT_PARTS][SUB_PARTITIONS_RECT],
int64_t split_rd[SUB_PARTITIONS_SPLIT], unsigned int pb_source_variance,
int mi_row, int mi_col, aom_partition_features_t *const features) {
int64_t *horz_rd = rect_part_rd[HORZ];
int64_t *vert_rd = rect_part_rd[VERT];
// Generate features.
int feature_index = 0;
features->after_part_ab.f[feature_index++] = (float)part_ctx;
features->after_part_ab.f[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 < SUB_PARTITIONS_RECT; ++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 < SUB_PARTITIONS_RECT; ++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 < SUB_PARTITIONS_SPLIT; ++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->after_part_ab.f[feature_index++] = rd_ratio;
}
// Get variance of the 1:4 and 4:1 sub-blocks.
unsigned int horz_4_source_var[SUB_PARTITIONS_PART4] = { 0 };
unsigned int vert_4_source_var[SUB_PARTITIONS_PART4] = { 0 };
{
BLOCK_SIZE horz_4_bs = get_partition_subsize(bsize, PARTITION_HORZ_4);
BLOCK_SIZE vert_4_bs = get_partition_subsize(bsize, PARTITION_VERT_4);
av1_setup_src_planes(x, cpi->source, mi_row, mi_col,
av1_num_planes(&cpi->common), bsize);
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 < SUB_PARTITIONS_PART4; ++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 < SUB_PARTITIONS_PART4; ++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->after_part_ab.f[feature_index++] = var_ratio;
}
for (int i = 0; i < SUB_PARTITIONS_PART4; ++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->after_part_ab.f[feature_index++] = var_ratio;
}
assert(feature_index == 18);
}
// If the external partition model is used, we let it determine partition
// decisions before partition none. Specifically, these parameters:
// partition_none_allowed
// partition_horz_allowed
// partition_vert_allowed
// do_rectangular_split
// do_square_split
static bool ext_ml_model_decision_before_none(
AV1_COMP *cpi, const float features_from_motion[FEATURE_SIZE_SMS_SPLIT],
int *partition_none_allowed, int *partition_horz_allowed,
int *partition_vert_allowed, int *do_rectangular_split,
int *do_square_split) {
ExtPartController *const ext_part_controller = &cpi->ext_part_controller;
if (!ext_part_controller->ready) return false;
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_BEFORE_PART_NONE;
for (int i = 0; i < FEATURE_SIZE_SMS_SPLIT; ++i) {
features.before_part_none.f[i] = features_from_motion[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*partition_none_allowed = decision.partition_none_allowed;
*partition_horz_allowed = decision.partition_rect_allowed[HORZ];
*partition_vert_allowed = decision.partition_rect_allowed[VERT];
*do_rectangular_split = decision.do_rectangular_split;
*do_square_split = decision.do_square_split;
return true;
}
// If the external partition model is used, we let it determine partition
// decisions before partition none. Specifically, these parameters:
// prune_horz
// prune_vert
static bool ext_ml_model_decision_before_none_part2(
AV1_COMP *cpi,
const float features_from_motion[FEATURE_SIZE_SMS_PRUNE_PART],
int *prune_horz, int *prune_vert) {
ExtPartController *const ext_part_controller = &cpi->ext_part_controller;
if (!ext_part_controller->ready) return false;
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_BEFORE_PART_NONE_PART2;
for (int i = 0; i < FEATURE_SIZE_SMS_PRUNE_PART; ++i) {
features.before_part_none.f_part2[i] = features_from_motion[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*prune_horz = decision.prune_rect_part[HORZ];
*prune_vert = decision.prune_rect_part[VERT];
return true;
}
// If the external partition model is used, we let it determine partition
// decisions after none partition. Specifically, these parameters:
// do_square_split
// do_rectangular_split
bool ext_ml_model_decision_after_none(
ExtPartController *const ext_part_controller, const int is_intra_frame,
const float *const features_after_none, int *do_square_split,
int *do_rectangular_split) {
if (!ext_part_controller->ready || is_intra_frame) return false;
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_AFTER_PART_NONE;
for (int i = 0; i < 4; ++i) {
features.after_part_none.f[i] = features_after_none[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*do_square_split = decision.do_square_split;
*do_rectangular_split = decision.do_rectangular_split;
return true;
}
// If the external partition model is used, we let it determine partition
// decisions after none partition. Specifically, these parameters:
// terminate_partition_search
bool ext_ml_model_decision_after_none_part2(
AV1_COMP *const cpi, const float *const features_terminate,
int *terminate_partition_search) {
AV1_COMMON *const cm = &cpi->common;
ExtPartController *const ext_part_controller = &cpi->ext_part_controller;
if (!ext_part_controller->ready || frame_is_intra_only(cm)) return false;
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_AFTER_PART_NONE_PART2;
for (int i = 0; i < FEATURE_SIZE_SMS_TERM_NONE; ++i) {
features.after_part_none.f_terminate[i] = features_terminate[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*terminate_partition_search = decision.terminate_partition_search;
return true;
}
// If the external partition model is used, we let it determine partition
// decisions after none partition. Specifically, these parameters:
// terminate_partition_search
bool ext_ml_model_decision_after_split(AV1_COMP *const cpi,
const float *const features_terminate,
int *terminate_partition_search) {
const AV1_COMMON *const cm = &cpi->common;
ExtPartController *const ext_part_controller = &cpi->ext_part_controller;
if (frame_is_intra_only(cm) || !cpi->ext_part_controller.ready) {
return false;
}
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_AFTER_PART_SPLIT;
for (int i = 0; i < 31; ++i) {
features.after_part_split.f_terminate[i] = features_terminate[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*terminate_partition_search = decision.terminate_partition_search;
return true;
}
// If the external partition model is used, we let it determine partition
// decisions after none partition. Specifically, these parameters:
// prune_rect_part[HORZ]
// prune_rect_part[VERT]
bool ext_ml_model_decision_after_split_part2(
ExtPartController *const ext_part_controller, const int is_intra_frame,
const float *const features_prune, int *prune_rect_part_horz,
int *prune_rect_part_vert) {
if (is_intra_frame || !ext_part_controller->ready) {
return false;
}
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_AFTER_PART_SPLIT_PART2;
for (int i = 0; i < 9; ++i) {
features.after_part_split.f_prune_rect[i] = features_prune[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*prune_rect_part_horz = decision.prune_rect_part[0];
*prune_rect_part_vert = decision.prune_rect_part[1];
return true;
}
// If the external partition model is used, we let it determine partition
// decisions after rectangular partition. Specifically, these parameters:
// horza_partition_allowed
// horzb_partition_allowed
// verta_partition_allowed
// vertb_partition_allowed
static bool ext_ml_model_decision_after_rect(
ExtPartController *const ext_part_controller, const int is_intra_frame,
const float *const features_after_rect, int *horza_partition_allowed,
int *horzb_partition_allowed, int *verta_partition_allowed,
int *vertb_partition_allowed) {
if (is_intra_frame || !ext_part_controller->ready) return false;
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_AFTER_PART_RECT;
for (int i = 0; i < 10; ++i) {
features.after_part_rect.f[i] = features_after_rect[i];
}
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*horza_partition_allowed = decision.horza_partition_allowed;
*horzb_partition_allowed = decision.horzb_partition_allowed;
*verta_partition_allowed = decision.verta_partition_allowed;
*vertb_partition_allowed = decision.vertb_partition_allowed;
return true;
}
// If the external partition model is used, we let it determine partition
// decisions after AB partition. Specifically, these parameters:
// partition_vert4_allowed
// partition_horz4_allowed
static bool ext_ml_model_decision_after_part_ab(
AV1_COMP *const cpi, MACROBLOCK *const x, BLOCK_SIZE bsize, int part_ctx,
int64_t best_rd, int64_t rect_part_rd[NUM_RECT_PARTS][SUB_PARTITIONS_RECT],
int64_t split_rd[SUB_PARTITIONS_SPLIT], int *const partition_horz4_allowed,
int *const partition_vert4_allowed, unsigned int pb_source_variance,
int mi_row, int mi_col) {
const AV1_COMMON *const cm = &cpi->common;
ExtPartController *const ext_part_controller = &cpi->ext_part_controller;
if (!frame_is_intra_only(cm) && ext_part_controller->ready) {
// Setup features.
aom_partition_features_t features;
features.id = FEATURE_AFTER_PART_AB;
prepare_features_after_part_ab(cpi, x, bsize, part_ctx, best_rd,
rect_part_rd, split_rd, pb_source_variance,
mi_row, mi_col, &features);
// Send necessary features to the external model.
av1_ext_part_send_features(ext_part_controller, &features);
// Get partition decisions from the external model.
aom_partition_decision_t decision;
const bool valid_decision =
av1_ext_part_get_partition_decision(ext_part_controller, &decision);
if (!valid_decision) return false;
// Populate decisions
*partition_horz4_allowed = decision.partition_horz4_allowed;
*partition_vert4_allowed = decision.partition_vert4_allowed;
return true;
}
return false;
}
// This function resembles "av1_setup_sms_tree()" in context_tree.c
// with function signature change.
SIMPLE_MOTION_DATA_TREE *setup_sms_tree(AV1_COMP *const cpi,
SIMPLE_MOTION_DATA_TREE *sms_tree) {
AV1_COMMON *const cm = &cpi->common;
const int stat_generation_stage = is_stat_generation_stage(cpi);
const int is_sb_size_128 = cm->seq_params->sb_size == BLOCK_128X128;
const int tree_nodes =
av1_get_pc_tree_nodes(is_sb_size_128, stat_generation_stage);
int sms_tree_index = 0;
SIMPLE_MOTION_DATA_TREE *this_sms;
int square_index = 1;
int nodes;
aom_free(sms_tree);
CHECK_MEM_ERROR(cm, sms_tree, aom_calloc(tree_nodes, sizeof(*sms_tree)));
this_sms = &sms_tree[0];
if (!stat_generation_stage) {
const int leaf_factor = is_sb_size_128 ? 4 : 1;
const int leaf_nodes = 256 * leaf_factor;
// Sets up all the leaf nodes in the tree.
for (sms_tree_index = 0; sms_tree_index < leaf_nodes; ++sms_tree_index) {
SIMPLE_MOTION_DATA_TREE *const tree = &sms_tree[sms_tree_index];
tree->block_size = square[0];
}
// Each node has 4 leaf nodes, fill each block_size level of the tree
// from leafs to the root.
for (nodes = leaf_nodes >> 2; nodes > 0; nodes >>= 2) {
for (int i = 0; i < nodes; ++i) {
SIMPLE_MOTION_DATA_TREE *const tree = &sms_tree[sms_tree_index];
tree->block_size = square[square_index];
for (int j = 0; j < 4; j++) tree->split[j] = this_sms++;
++sms_tree_index;
}
++square_index;
}
} else {
// Allocation for firstpass/LAP stage
// TODO(Mufaddal): refactor square_index to use a common block_size macro
// from firstpass.c
SIMPLE_MOTION_DATA_TREE *const tree = &sms_tree[sms_tree_index];
square_index = 2;
tree->block_size = square[square_index];
}
// Set up the root node for the largest superblock size
return &sms_tree[tree_nodes - 1];
}
static void write_motion_feature_to_file(
const char *const path, const int sb_counter, const unsigned int *block_sse,
const unsigned int *block_var, const int num_blocks, const BLOCK_SIZE bsize,
const BLOCK_SIZE fixed_block_size, const int mi_row, const int mi_col) {
char filename[256];
snprintf(filename, sizeof(filename), "%s/motion_search_feature_sb%d", path,
sb_counter);
FILE *pfile = fopen(filename, "w");
fprintf(pfile, "%d,%d,%d,%d,%d\n", mi_row, mi_col, bsize,
block_size_wide[fixed_block_size], num_blocks);
for (int i = 0; i < num_blocks; ++i) {
fprintf(pfile, "%d", block_sse[i]);
if (i < num_blocks - 1) fprintf(pfile, ",");
}
fprintf(pfile, "\n");
for (int i = 0; i < num_blocks; ++i) {
fprintf(pfile, "%d", block_var[i]);
if (i < num_blocks - 1) fprintf(pfile, ",");
}
fprintf(pfile, "\n");
fclose(pfile);
}
void av1_collect_motion_search_features_sb(AV1_COMP *const cpi, ThreadData *td,
const int mi_row, const int mi_col,
const BLOCK_SIZE bsize,
aom_partition_features_t *features) {
const AV1_COMMON *const cm = &cpi->common;
MACROBLOCK *const x = &td->mb;
const BLOCK_SIZE fixed_block_size = BLOCK_16X16;
const int col_step = mi_size_wide[bsize] / mi_size_wide[fixed_block_size];
const int row_step = mi_size_high[bsize] / mi_size_high[fixed_block_size];
SIMPLE_MOTION_DATA_TREE *sms_tree = NULL;
SIMPLE_MOTION_DATA_TREE *sms_root = setup_sms_tree(cpi, sms_tree);
av1_init_simple_motion_search_mvs_for_sb(cpi, NULL, x, sms_root, mi_row,
mi_col);
av1_reset_simple_motion_tree_partition(sms_root, bsize);
const int ref_list[] = { cpi->rc.is_src_frame_alt_ref ? ALTREF_FRAME
: LAST_FRAME };
const int num_blocks = col_step * row_step;
unsigned int *block_sse = aom_calloc(num_blocks, sizeof(*block_sse));
unsigned int *block_var = aom_calloc(num_blocks, sizeof(*block_var));
int idx = 0;
for (int row = mi_row;
row < AOMMIN(mi_row + mi_size_high[bsize], cm->mi_params.mi_rows);
row += row_step) {
for (int col = mi_col;
col < AOMMIN(mi_col + mi_size_wide[bsize], cm->mi_params.mi_cols);
col += col_step) {
simple_motion_search_get_best_ref(
cpi, x, sms_root, row, col, fixed_block_size, ref_list,