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Diffstat (limited to 'libs/libaom/src/av1/encoder/partition_strategy.c')
| -rw-r--r-- | libs/libaom/src/av1/encoder/partition_strategy.c | 1288 |
1 files changed, 1288 insertions, 0 deletions
diff --git a/libs/libaom/src/av1/encoder/partition_strategy.c b/libs/libaom/src/av1/encoder/partition_strategy.c new file mode 100644 index 000000000..cc820ba24 --- /dev/null +++ b/libs/libaom/src/av1/encoder/partition_strategy.c @@ -0,0 +1,1288 @@ +/* + * 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/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/rdopt.h" + +#if !CONFIG_REALTIME_ONLY +static AOM_INLINE void simple_motion_search_prune_part_features( + AV1_COMP *const cpi, MACROBLOCK *x, PC_TREE *pc_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, bit_depth) >> (bit_depth - 8); + x->log_q = logf(1.0f + (float)(dc_q * dc_q) / 256.0f); + 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, PC_TREE *pc_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 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; + + 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, pc_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.part_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 +// pc_tree. If save_mv is 1, update mv_ref_fulls under pc_tree and the +// subtrees. +static int simple_motion_search_get_best_ref( + AV1_COMP *const cpi, MACROBLOCK *x, PC_TREE *pc_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 = pc_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->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) { + pc_tree->start_mvs[ref].row = best_mv.as_mv.row / 8; + pc_tree->start_mvs[ref].col = best_mv.as_mv.col / 8; + + if (bsize >= BLOCK_8X8) { + for (int r_idx = 0; r_idx < 4; r_idx++) { + // Propagate the new motion vectors to a lower level + PC_TREE *sub_tree = pc_tree->split[r_idx]; + sub_tree->start_mvs[ref] = pc_tree->start_mvs[ref]; + } + } + } + } + } + + return best_ref; +} + +// Collects features using simple_motion_search and store them in features. The +// features are also cached in PC_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, PC_TREE *pc_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 (!pc_tree->sms_none_valid && features_to_get & FEATURE_SMS_NONE_FLAG) { + simple_motion_search_get_best_ref(cpi, x, pc_tree, mi_row, mi_col, bsize, + ref_list, num_refs, use_subpixel, 1, + &pc_tree->sms_none_feat[0], + &pc_tree->sms_none_feat[1]); + pc_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; + PC_TREE *sub_tree = pc_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 (!pc_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, pc_tree, sub_mi_row, sub_mi_col, subsize, ref_list, num_refs, + use_subpixel, 0, &pc_tree->sms_rect_feat[2 * r_idx], + &pc_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, pc_tree, sub_mi_row, sub_mi_col, subsize, ref_list, num_refs, + use_subpixel, 0, &pc_tree->sms_rect_feat[4 + 2 * r_idx], + &pc_tree->sms_rect_feat[4 + 2 * r_idx + 1]); + } + pc_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 + pc_tree->sms_none_feat[sub_idx]); + } + } + + if (features_to_get & FEATURE_SMS_SPLIT_FLAG) { + for (int sub_idx = 0; sub_idx < 4; sub_idx++) { + PC_TREE *sub_tree = pc_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 + pc_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->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_rect(AV1_COMP *const cpi, MACROBLOCK *x, + PC_TREE *pc_tree, int mi_row, + int mi_col, BLOCK_SIZE bsize, + int *partition_horz_allowed, + int *partition_vert_allowed, + int *prune_horz, int *prune_vert) { + aom_clear_system_state(); + const AV1_COMMON *const cm = &cpi->common; + 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; + 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, pc_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.part_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; + *prune_vert = probs[PARTITION_VERT] <= prune_thresh; + } +} + +// 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, PC_TREE *pc_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, pc_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, xd->bd) >> (xd->bd - 8); + aom_clear_system_state(); + 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); + + aom_clear_system_state(); + 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; + } + 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.part_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.part_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.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; + 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 +// PC_TREE. +static AOM_INLINE void get_min_bsize(const PC_TREE *pc_tree, int *min_bw, + int *min_bh) { + if (!pc_tree) return; + + const BLOCK_SIZE bsize = pc_tree->block_size; + if (bsize == BLOCK_4X4) { + *min_bw = 0; + *min_bh = 0; + return; + } + + PARTITION_TYPE part_type = pc_tree->partitioning; + if (part_type == PARTITION_INVALID) return; + + if (part_type == PARTITION_SPLIT) { + for (int i = 0; i < 4; ++i) { + get_min_bsize(pc_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, + PC_TREE *const pc_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.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 }; + + aom_clear_system_state(); + + 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 < 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(pc_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, pc_tree, mi_row, mi_col, + bsize, NULL, + FEATURE_SMS_PRUNE_PART_FLAG); + + features[f_idx++] = logf(1.0f + (float)pc_tree->sms_none_feat[1]); + + features[f_idx++] = logf(1.0f + (float)pc_tree->split[0]->sms_none_feat[1]); + features[f_idx++] = logf(1.0f + (float)pc_tree->split[1]->sms_none_feat[1]); + features[f_idx++] = logf(1.0f + (float)pc_tree->split[2]->sms_none_feat[1]); + features[f_idx++] = logf(1.0f + (float)pc_tree->split[3]->sms_none_feat[1]); + + features[f_idx++] = logf(1.0f + (float)pc_tree->sms_rect_feat[1]); + features[f_idx++] = logf(1.0f + (float)pc_tree->sms_rect_feat[3]); + features[f_idx++] = logf(1.0f + (float)pc_tree->sms_rect_feat[5]); + features[f_idx++] = logf(1.0f + (float)pc_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 }; + { + 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 < 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.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 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) / 256.0f; + 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 +#endif // !CONFIG_REALTIME_ONLY |