tor-browser

partition_strategy.c (106719B)

---

/*
* 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_config.h"

#include "av1/encoder/encodeframe_utils.h"
#if CONFIG_THREE_PASS
#include "av1/encoder/thirdpass.h"
#endif
#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 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 BLOCK_SIZE 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 (pfile == NULL) return;
 if (!is_test_mode) {
   fprintf(pfile, "%d,%d,%d,%d,%d\n", id, (int)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
static void 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 = log1pf((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
     };

     if (!av1_cnn_predict_img_multi_out_highbd(image, width, height, stride,
                                               cnn_config, &thread_data,
                                               bit_depth, &output)) {
       aom_internal_error(xd->error_info, AOM_CODEC_MEM_ERROR,
                          "Error allocating CNN data");
       return;
     }
   } else {
     uint8_t *image[1] = { x->plane[AOM_PLANE_Y].src.buf - stride - 1 };

     if (!av1_cnn_predict_img_multi_out(image, width, height, stride,
                                        cnn_config, &thread_data, &output)) {
       aom_internal_error(xd->error_info, AOM_CODEC_MEM_ERROR,
                          "Error allocating CNN data");
       return;
     }
   }

   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);
 }
}

static inline int get_simple_motion_search_prune_agg(int qindex,
                                                    int prune_level,
                                                    int is_rect_part) {
 assert(prune_level < TOTAL_AGG_LVLS);
 if (prune_level == NO_PRUNING) {
   return -1;
 }

 // Aggressiveness value for SIMPLE_MOTION_SEARCH_PRUNE_LEVEL except
 // QIDX_BASED_AGG_LVL
 const int sms_prune_agg_levels[TOTAL_SIMPLE_AGG_LVLS] = { 0, 1, 2, 3, 4, 5 };
 if (prune_level < TOTAL_SIMPLE_AGG_LVLS) {
   return sms_prune_agg_levels[prune_level];
 }

 // Map the QIDX_BASED_AGG_LVL to corresponding aggressiveness value.
 // Aggressive pruning for lower quantizers in non-boosted frames to prune
 // rectangular partitions.
 const int qband = is_rect_part ? (qindex <= 90 ? 1 : 0) : 0;
 const int sms_prune_agg_qindex_based[2] = { 3, 4 };
 return sms_prune_agg_qindex_based[qband];
}

// Performs a simple_motion_search with a single reference frame and extract
// the variance of residues. Then use the features to determine whether we want
// to go straight to splitting without trying PARTITION_NONE
static void 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 int agg = get_simple_motion_search_prune_agg(
     x->qindex, cpi->sf.part_sf.simple_motion_search_prune_agg, 0);
 if (agg < 0) {
   return;
 }

 int ml_model_index = (agg == SIMPLE_AGG_LVL1 || agg == SIMPLE_AGG_LVL2);

 const float *ml_mean =
     av1_simple_motion_search_split_mean[ml_model_index][bsize_idx];
 const float *ml_std =
     av1_simple_motion_search_split_std[ml_model_index][bsize_idx];
 const NN_CONFIG *nn_config =
     av1_simple_motion_search_split_nn_config[ml_model_index][bsize_idx];

 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[SIMPLE_AGG_LVL3]
                                                       [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 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;
     const int_mv best_mv = av1_simple_motion_search_sse_var(
         cpi, x, mi_row, mi_col, bsize, ref, start_mvs[ref], num_planes,
         use_subpixel, &curr_sse, &curr_var);
     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 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++] = log1pf((float)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++] = log1pf((float)sub_tree->sms_none_feat[0]);
     features[f_idx++] = log1pf((float)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++] = log1pf((float)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++] = log1pf((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];
}

// Performs a simple_motion_search with two reference frames and extract
// the variance of residues. Then use the features to determine whether we want
// to prune some partitions.
static void 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 = get_simple_motion_search_prune_agg(
     x->qindex, cpi->sf.part_sf.simple_motion_search_prune_agg, 1);
 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++] = log1pf((float)none_rdc->rate);
 features[f_idx++] = log1pf((float)none_rdc->dist);
 features[f_idx++] = log1pf((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 = log1pf((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;
     const MV_REFERENCE_FRAME ref =
         cpi->rc.is_src_frame_alt_ref ? ALTREF_FRAME : LAST_FRAME;
     const int_mv best_mv = av1_simple_motion_search_sse_var(
         cpi, x, this_mi_row, this_mi_col, mb_size, ref, start_mv, 1, 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 = log1pf((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;
   // TODO(debargha): x->source_variance is unavailable at this point,
   // so compute. The redundant recomputation later can be removed.
   const unsigned int source_variance = av1_get_perpixel_variance_facade(
       cpi, &x->e_mbd, &x->plane[0].src, sb_size, AOM_PLANE_Y);
   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 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:
     nn_config = &av1_early_term_after_split_nnconfig_64;
     thresh = is_480p_or_larger ? -2.0f : -1.2f;
     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++] = log1pf((float)dc_q / 4.0f);
 features[f_idx++] = log1pf((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++] = log1pf((float)sms_tree->sms_none_feat[1]);

 features[f_idx++] = log1pf((float)sms_tree->split[0]->sms_none_feat[1]);
 features[f_idx++] = log1pf((float)sms_tree->split[1]->sms_none_feat[1]);
 features[f_idx++] = log1pf((float)sms_tree->split[2]->sms_none_feat[1]);
 features[f_idx++] = log1pf((float)sms_tree->split[3]->sms_none_feat[1]);

 features[f_idx++] = log1pf((float)sms_tree->sms_rect_feat[1]);
 features[f_idx++] = log1pf((float)sms_tree->sms_rect_feat[3]);
 features[f_idx++] = log1pf((float)sms_tree->sms_rect_feat[5]);
 features[f_idx++] = log1pf((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;
 whole_block_variance = av1_get_perpixel_variance_facade(
     cpi, xd, &x->plane[0].src, bsize, AOM_PLANE_Y);
 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;
   split_variance[i] =
       av1_get_perpixel_variance_facade(cpi, xd, &buf, subsize, AOM_PLANE_Y);
 }

 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.
static void 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 BLOCK_SIZE 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
#define NEW_LABELS 3
// 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 AV1_COMMON *const cm = &cpi->common;
 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 BLOCK_SIZE 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 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;

 const int bsize_idx = convert_bsize_to_idx(bsize);
 if (bsize_idx < 0) return;
 const float *ml_mean = av1_partition4_nn_mean[bsize_idx];
 const float *ml_std = av1_partition4_nn_std[bsize_idx];

 int ml_model_index = (cpi->sf.part_sf.ml_4_partition_search_level_index < 3);

 const NN_CONFIG *nn_config = NULL;
 // 4-way partitions are only allowed for these three square block sizes.
 switch (bsize) {
   case BLOCK_16X16:
     nn_config = &av1_4_partition_nnconfig_16[ml_model_index];
     break;
   case BLOCK_32X32:
     nn_config = &av1_4_partition_nnconfig_32[ml_model_index];
     break;
   case BLOCK_64X64:
     nn_config = &av1_4_partition_nnconfig_64[ml_model_index];
     break;
   default: assert(0 && "Unexpected bsize.");
 }
 if (!nn_config || !ml_mean || !ml_std) 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);

   assert(horz_4_bs != BLOCK_INVALID);
   assert(vert_4_bs != BLOCK_INVALID);

   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];

     horz_4_source_var[i] = av1_get_perpixel_variance_facade(
         cpi, xd, &horz_4_src, horz_4_bs, AOM_PLANE_Y);
     vert_4_source_var[i] = av1_get_perpixel_variance_facade(
         cpi, xd, &vert_4_src, vert_4_bs, AOM_PLANE_Y);
   }
 }

 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);

 if (ml_model_index) {
   for (int idx = 0; idx < FEATURES; idx++) {
     features[idx] = (features[idx] - ml_mean[idx]) / ml_std[idx];
   }
 }

 // 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);
 }

 if (ml_model_index == 0) {
   // 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;
     }
   }
 } else {
   // Calculate scores using the NN model.
   float score[NEW_LABELS] = { 0.0f };
   float probs[NEW_LABELS] = { 0.0f };
   av1_nn_predict(features, nn_config, 1, score);

   av1_nn_softmax(score, probs, NEW_LABELS);

   // Make decisions based on the model scores.
   const float search_thresh = av1_partition4_search_thresh
       [cpi->sf.part_sf.ml_4_partition_search_level_index][res_idx][bsize_idx];
   const float not_search_thresh = av1_partition4_not_search_thresh
       [cpi->sf.part_sf.ml_4_partition_search_level_index][res_idx][bsize_idx];

   for (int i = 1; i < NEW_LABELS; ++i) {
     if (probs[i] >= search_thresh) {
       if (i == 1) part4_allowed[HORZ4] = 1;
       if (i == 2) part4_allowed[VERT4] = 1;
     }
     if (probs[i] < not_search_thresh) {
       if (i == 1) part4_allowed[HORZ4] = 0;
       if (i == 2) part4_allowed[VERT4] = 0;
     }
   }
 }
}
#undef FEATURES
#undef LABELS
#undef NEW_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 int bsize_idx = convert_bsize_to_idx(bsize);
 if (bsize_idx < 0) return;
 const float *ml_mean = av1_hd_partition_breakout_nn_mean[bsize_idx];
 const float *ml_std = av1_hd_partition_breakout_nn_std[bsize_idx];

 const NN_CONFIG *nn_config = NULL;
 float thresh = 0;
 switch (bsize) {
   case BLOCK_8X8:
     nn_config =
         &av1_partition_breakout_nnconfig_8
             [cpi->sf.part_sf.ml_partition_search_breakout_model_index];
     thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[bsize_idx];
     break;
   case BLOCK_16X16:
     nn_config =
         &av1_partition_breakout_nnconfig_16
             [cpi->sf.part_sf.ml_partition_search_breakout_model_index];
     thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[bsize_idx];
     break;
   case BLOCK_32X32:
     nn_config =
         &av1_partition_breakout_nnconfig_32
             [cpi->sf.part_sf.ml_partition_search_breakout_model_index];
     thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[bsize_idx];
     break;
   case BLOCK_64X64:
     nn_config =
         &av1_partition_breakout_nnconfig_64
             [cpi->sf.part_sf.ml_partition_search_breakout_model_index];
     thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[bsize_idx];
     break;
   case BLOCK_128X128:
     nn_config =
         &av1_partition_breakout_nnconfig_128
             [cpi->sf.part_sf.ml_partition_search_breakout_model_index];
     thresh = cpi->sf.part_sf.ml_partition_search_breakout_thresh[bsize_idx];
     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 = 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);

 if (cpi->sf.part_sf.ml_partition_search_breakout_model_index) {
   for (int idx = 0; idx < FEATURES; idx++) {
     features[idx] = (features[idx] - ml_mean[idx]) / ml_std[idx];
   }
 }

 // 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);

 float thresh_score = (float)log(thresh / (1 - thresh));

 // Make decision.
 if (score >= thresh_score) {
   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;

#if CONFIG_THREE_PASS
 if (cpi->third_pass_ctx) {
   int mi_row = blk_params->mi_row;
   int mi_col = blk_params->mi_col;
   double ratio_h, ratio_w;
   av1_get_third_pass_ratio(cpi->third_pass_ctx, 0, cm->height, cm->width,
                            &ratio_h, &ratio_w);
   THIRD_PASS_MI_INFO *this_mi = av1_get_third_pass_mi(
       cpi->third_pass_ctx, 0, mi_row, mi_col, ratio_h, ratio_w);
   BLOCK_SIZE third_pass_bsize =
       av1_get_third_pass_adjusted_blk_size(this_mi, ratio_h, ratio_w);
   // check the actual partition of this block in the second pass
   PARTITION_TYPE third_pass_part =
       av1_third_pass_get_sb_part_type(cpi->third_pass_ctx, this_mi);

   int is_edge = (mi_row + mi_size_high[bsize] >= cm->mi_params.mi_rows) ||
                 (mi_col + mi_size_wide[bsize] >= cm->mi_params.mi_cols);

   if (!is_edge && block_size_wide[bsize] >= 16) {
     // If in second pass we used rectangular partition, then do not search for
     // rectangular partition in the different direction.
     if (third_pass_part != PARTITION_NONE) {
       if (third_pass_part == PARTITION_HORZ ||
           third_pass_part == PARTITION_HORZ_4 ||
           third_pass_part == PARTITION_HORZ_A ||
           third_pass_part == PARTITION_HORZ_B) {
         part_state->partition_rect_allowed[VERT] = 0;
       } else if (third_pass_part == PARTITION_VERT ||
                  third_pass_part == PARTITION_VERT_4 ||
                  third_pass_part == PARTITION_VERT_A ||
                  third_pass_part == PARTITION_VERT_B) {
         part_state->partition_rect_allowed[HORZ] = 0;
       }
     }

     int minSize = AOMMIN(block_size_wide[third_pass_bsize],
                          block_size_high[third_pass_bsize]);
     int maxSize = AOMMAX(block_size_wide[third_pass_bsize],
                          block_size_high[third_pass_bsize]);
     if (block_size_wide[bsize] < minSize / 4) {
       // Current partition is too small, just terminate
       part_state->terminate_partition_search = 1;
       return;
     } else if (block_size_wide[bsize] < minSize / 2) {
       if (third_pass_part != PARTITION_NONE) {
         // Current partition is very small, and in second pass we used
         // rectangular partition. Terminate the search here then.
         part_state->terminate_partition_search = 1;
         return;
       } else {
         // Partition is small, but we still check this partition, only disable
         // further splits.
         // TODO(any): check why this is not covered by the termination for <
         // minSize/4.
         av1_disable_square_split_partition(part_state);
         av1_disable_rect_partitions(part_state);
         return;
       }
     } else if (block_size_wide[bsize] > maxSize) {
       // Partition is larger than in the second pass. Only allow split.
       av1_set_square_split_only(part_state);
       return;
     } else if (block_size_wide[bsize] >= minSize &&
                block_size_wide[bsize] <= maxSize) {
       // Partition is within a range where it is very likely to find a good
       // choice, so do not prune anything.
       return;
     }
   }
 }
#endif  // CONFIG_THREE_PASS

 // Prune rectangular partitions for larger blocks.
 if (bsize > cpi->sf.part_sf.rect_partition_eval_thresh) {
   part_state->do_rectangular_split = 0;
   part_state->partition_rect_allowed[HORZ] = 0;
   part_state->partition_rect_allowed[VERT] = 0;
 }

 // 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 == 1) {
   if (bsize == BLOCK_8X8 && x->qindex < 35)
     av1_disable_rect_partitions(part_state);

 } else if (cpi->sf.part_sf.prune_rectangular_split_based_on_qidx == 2) {
   // 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;
   if (cpi->sf.part_sf.prune_sub_8x8_partition_level == 2) {
     prune_sub_8x8 = 1;
   } else {
     assert(cpi->sf.part_sf.prune_sub_8x8_partition_level == 1);
     // Prune if both neighbors are available and either is > BLOCK_8X8
     prune_sub_8x8 = xd->left_available && xd->up_available &&
                     (xd->left_mbmi->bsize > BLOCK_8X8 ||
                      xd->above_mbmi->bsize > BLOCK_8X8);
   }
   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) {
   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) {
   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) {
   simple_motion_search_prune_rect(cpi, x, sms_tree, part_state);
 }
}

#ifndef NDEBUG
static 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
static 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.
   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;
 }

 // 4-way partitions are only allowed for these three square block sizes.
 assert(bsize == BLOCK_16X16 || bsize == BLOCK_32X32 || bsize == BLOCK_64X64);

 // 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);

   assert(horz_4_bs != BLOCK_INVALID);
   assert(vert_4_bs != BLOCK_INVALID);

   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];

     horz_4_source_var[i] = av1_get_perpixel_variance_facade(
         cpi, xd, &horz_4_src, horz_4_bs, AOM_PLANE_Y);
     vert_4_source_var[i] = av1_get_perpixel_variance_facade(
         cpi, xd, &vert_4_src, vert_4_bs, AOM_PLANE_Y);
   }
 }

 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 = AOM_EXT_PART_FEATURE_BEFORE_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 = AOM_EXT_PART_FEATURE_BEFORE_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 = AOM_EXT_PART_FEATURE_AFTER_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 = AOM_EXT_PART_FEATURE_AFTER_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 = AOM_EXT_PART_FEATURE_AFTER_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 = AOM_EXT_PART_FEATURE_AFTER_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 = AOM_EXT_PART_FEATURE_AFTER_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 = AOM_EXT_PART_FEATURE_AFTER_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.
static 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;
 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,
                                          TileDataEnc *tile_data,
                                          const int mi_row, const int mi_col,
                                          const BLOCK_SIZE bsize,
                                          aom_partition_features_t *features) {
 const AV1_COMMON *const cm = &cpi->common;
 if (frame_is_intra_only(cm)) return;

 MACROBLOCK *const x = &td->mb;
 const BLOCK_SIZE fixed_block_size = BLOCK_16X16;
 const int col_step = mi_size_wide[fixed_block_size];
 const int row_step = mi_size_high[fixed_block_size];
 SIMPLE_MOTION_DATA_TREE *sms_tree = NULL;
 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);
 CHECK_MEM_ERROR(cm, sms_tree, aom_calloc(tree_nodes, sizeof(*sms_tree)));
 SIMPLE_MOTION_DATA_TREE *sms_root = setup_sms_tree(cpi, sms_tree);
 TileInfo *const tile_info = &tile_data->tile_info;
 av1_set_offsets_without_segment_id(cpi, tile_info, x, mi_row, mi_col, bsize);
 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 mi_width =
     AOMMIN(mi_size_wide[bsize], cm->mi_params.mi_cols - mi_col);
 const int mi_height =
     AOMMIN(mi_size_high[bsize], cm->mi_params.mi_rows - mi_row);
 const int col_steps = (mi_width / col_step) + ((mi_width % col_step) > 0);
 const int row_steps = (mi_height / row_step) + ((mi_height % row_step) > 0);
 const int num_blocks = col_steps * row_steps;
 unsigned int *block_sse = aom_calloc(num_blocks, sizeof(*block_sse));
 unsigned int *block_var = aom_calloc(num_blocks, sizeof(*block_var));
 if (!(block_sse && block_var)) {
   aom_free(sms_tree);
   aom_free(block_sse);
   aom_free(block_var);
   aom_internal_error(cm->error, AOM_CODEC_MEM_ERROR,
                      "Error allocating block_sse & 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,
         /*num_refs=*/1, /*use_subpixel=*/1,
         /*save_mv=*/1, &block_sse[idx], &block_var[idx]);
     ++idx;
   }
 }
 if (features == NULL) {
   write_motion_feature_to_file(cpi->oxcf.partition_info_path, cpi->sb_counter,
                                block_sse, block_var, idx, bsize,
                                fixed_block_size, mi_row, mi_col);
 } else {
   features->sb_features.motion_features.unit_length =
       block_size_wide[fixed_block_size];
   features->sb_features.motion_features.num_units = idx;
   for (int i = 0; i < idx; ++i) {
     features->sb_features.motion_features.block_sse[i] = block_sse[i];
     features->sb_features.motion_features.block_var[i] = block_var[i];
   }
 }

 aom_free(block_sse);
 aom_free(block_var);
 aom_free(sms_tree);
}

#if CONFIG_PARTITION_SEARCH_ORDER
void av1_prepare_motion_search_features_block(
   AV1_COMP *const cpi, ThreadData *td, TileDataEnc *tile_data,
   const int mi_row, const int mi_col, const BLOCK_SIZE bsize,
   const int valid_partition_types, unsigned int *block_sse,
   unsigned int *block_var, unsigned int sub_block_sse[4],
   unsigned int sub_block_var[4], unsigned int horz_block_sse[2],
   unsigned int horz_block_var[2], unsigned int vert_block_sse[2],
   unsigned int vert_block_var[2]) {
 const AV1_COMMON *const cm = &cpi->common;
 if (frame_is_intra_only(cm)) return;
 MACROBLOCK *const x = &td->mb;
 SIMPLE_MOTION_DATA_TREE *sms_tree = NULL;
 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);
 CHECK_MEM_ERROR(cm, sms_tree, aom_calloc(tree_nodes, sizeof(*sms_tree)));
 SIMPLE_MOTION_DATA_TREE *sms_root = setup_sms_tree(cpi, sms_tree);
 TileInfo *const tile_info = &tile_data->tile_info;
 av1_set_offsets_without_segment_id(cpi, tile_info, x, mi_row, mi_col, bsize);
 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 sub_mi_width = mi_size_wide[bsize] / 2;
 const int sub_mi_height = sub_mi_width;
 simple_motion_search_get_best_ref(
     cpi, x, sms_root, mi_row, mi_col, bsize, ref_list, /*num_refs=*/1,
     /*use_subpixel=*/1, /*save_mv=*/1, block_sse, block_var);
 // Split to 4 sub blocks.
 if (valid_partition_types & (1 << PARTITION_SPLIT)) {
   const BLOCK_SIZE subsize = get_partition_subsize(bsize, PARTITION_SPLIT);
   for (int i = 0; i < 4; ++i) {
     const int row = mi_row + (i >> 1) * sub_mi_height;
     const int col = mi_col + (i & 1) * sub_mi_width;
     simple_motion_search_get_best_ref(cpi, x, sms_root, row, col, subsize,
                                       ref_list, /*num_refs=*/1,
                                       /*use_subpixel=*/1, /*save_mv=*/1,
                                       &sub_block_sse[i], &sub_block_var[i]);
   }
 }
 // Horizontal split
 if (valid_partition_types & (1 << PARTITION_HORZ)) {
   const BLOCK_SIZE subsize = get_partition_subsize(bsize, PARTITION_HORZ);
   for (int i = 0; i < 2; ++i) {
     const int row = mi_row + (i & 1) * sub_mi_height;
     const int col = mi_col;
     simple_motion_search_get_best_ref(cpi, x, sms_root, row, col, subsize,
                                       ref_list, /*num_refs=*/1,
                                       /*use_subpixel=*/1, /*save_mv=*/1,
                                       &horz_block_sse[i], &horz_block_var[i]);
   }
 }
 // Vertical split
 if (valid_partition_types & (1 << PARTITION_VERT)) {
   const BLOCK_SIZE subsize = get_partition_subsize(bsize, PARTITION_VERT);
   for (int i = 0; i < 2; ++i) {
     const int row = mi_row;
     const int col = mi_col + (i & 1) * sub_mi_width;
     simple_motion_search_get_best_ref(cpi, x, sms_root, row, col, subsize,
                                       ref_list, /*num_refs=*/1,
                                       /*use_subpixel=*/1, /*save_mv=*/1,
                                       &vert_block_sse[i], &vert_block_var[i]);
   }
 }

 aom_free(sms_tree);
}
#endif  // CONFIG_PARTITION_SEARCH_ORDER
#endif  // !CONFIG_REALTIME_ONLY

static inline void init_simple_motion_search_mvs(
   SIMPLE_MOTION_DATA_TREE *sms_tree, const FULLPEL_MV *start_mvs) {
 memcpy(sms_tree->start_mvs, start_mvs, sizeof(sms_tree->start_mvs));
 av1_zero(sms_tree->sms_none_feat);
 av1_zero(sms_tree->sms_rect_feat);
 av1_zero(sms_tree->sms_none_valid);
 av1_zero(sms_tree->sms_rect_valid);

 if (sms_tree->block_size >= BLOCK_8X8) {
   init_simple_motion_search_mvs(sms_tree->split[0], start_mvs);
   init_simple_motion_search_mvs(sms_tree->split[1], start_mvs);
   init_simple_motion_search_mvs(sms_tree->split[2], start_mvs);
   init_simple_motion_search_mvs(sms_tree->split[3], start_mvs);
 }
}

void av1_init_simple_motion_search_mvs_for_sb(const AV1_COMP *cpi,
                                             const TileInfo *tile_info,
                                             MACROBLOCK *x,
                                             SIMPLE_MOTION_DATA_TREE *sms_root,
                                             int mi_row, int mi_col) {
 // Use the NEARESTMV of the sb as the start mv
 const AV1_COMMON *cm = &cpi->common;
 MACROBLOCKD *const xd = &x->e_mbd;
 FULLPEL_MV ref_mvs[REF_FRAMES];
 const BLOCK_SIZE sb_size = cm->seq_params->sb_size;
 av1_zero(ref_mvs);
 // If tile_info is NULL, assume that the offsets have already been set.
 if (tile_info) {
   av1_set_offsets_without_segment_id(cpi, tile_info, x, mi_row, mi_col,
                                      sb_size);
 }

 MB_MODE_INFO_EXT mbmi_ext;
 const int ref_frame =
     cpi->rc.is_src_frame_alt_ref ? ALTREF_FRAME : LAST_FRAME;
 av1_find_mv_refs(cm, xd, xd->mi[0], ref_frame, mbmi_ext.ref_mv_count,
                  xd->ref_mv_stack, xd->weight, NULL, mbmi_ext.global_mvs,
                  mbmi_ext.mode_context);
 if (mbmi_ext.ref_mv_count[ref_frame] > 0) {
   ref_mvs[ref_frame] =
       get_fullmv_from_mv(&xd->ref_mv_stack[ref_frame][0].this_mv.as_mv);
 } else {
   ref_mvs[ref_frame] =
       get_fullmv_from_mv(&mbmi_ext.global_mvs[ref_frame].as_mv);
 }

 init_simple_motion_search_mvs(sms_root, ref_mvs);
}