Improve the partition split prediction model

Include the sizes of the above and left partition block as additional
features.

This affects speed 0 and 1.

Compression change is almost neutral(about 0.03% on average).

Average encoding speedup is 3~6% depending on QP and resolution.

Change-Id: I8bddfadf6072ae757c124da0819302850d8c6fe7

2 files changed, 152 insertions, 107 deletions

diff --git a/vp9/encoder/vp9_encodeframe.c b/vp9/encoder/vp9_encodeframe.c
index 236567f94..ee06b4364 100644
--- a/vp9/encoder/vp9_encodeframe.c
+++ b/vp9/encoder/vp9_encodeframe.c
@@ -3481,9 +3481,9 @@ static void simple_motion_search(const VP9_COMP *const cpi, MACROBLOCK *const x,
 }
 
 // Use a neural net model to prune partition-none and partition-split search.
-// The model uses prediction residue variance and quantization step size as
-// input features.
-#define FEATURES 6
+// Features used: QP; spatial block size contexts; variance of prediction
+// residue after simple_motion_search.
+#define FEATURES 12
 static void ml_predict_var_rd_paritioning(const VP9_COMP *const cpi,
                                           MACROBLOCK *const x,
                                           PC_TREE *const pc_tree,
@@ -3502,28 +3502,27 @@ static void ml_predict_var_rd_paritioning(const VP9_COMP *const cpi,
   uint8_t *const pred_buf = pred_buffer;
 #endif  // CONFIG_VP9_HIGHBITDEPTH
   const int speed = cpi->oxcf.speed;
-  int i;
   float thresh = 0.0f;
 
   switch (bsize) {
     case BLOCK_64X64:
-      nn_config = &vp9_var_rd_part_nnconfig_64;
-      thresh = speed > 0 ? 3.5f : 3.0f;
+      nn_config = &vp9_part_split_nnconfig_64;
+      thresh = speed > 0 ? 2.8f : 3.0f;
       break;
     case BLOCK_32X32:
-      nn_config = &vp9_var_rd_part_nnconfig_32;
+      nn_config = &vp9_part_split_nnconfig_32;
       thresh = speed > 0 ? 3.5f : 3.0f;
       break;
     case BLOCK_16X16:
-      nn_config = &vp9_var_rd_part_nnconfig_16;
-      thresh = speed > 0 ? 3.5f : 4.0f;
+      nn_config = &vp9_part_split_nnconfig_16;
+      thresh = speed > 0 ? 3.8f : 4.0f;
       break;
     case BLOCK_8X8:
-      nn_config = &vp9_var_rd_part_nnconfig_8;
+      nn_config = &vp9_part_split_nnconfig_8;
       if (cm->width >= 720 && cm->height >= 720)
         thresh = speed > 0 ? 2.5f : 2.0f;
       else
-        thresh = speed > 0 ? 3.5f : 2.0f;
+        thresh = speed > 0 ? 3.8f : 2.0f;
       break;
     default: assert(0 && "Unexpected block size."); return;
   }
@@ -3542,6 +3541,7 @@ static void ml_predict_var_rd_paritioning(const VP9_COMP *const cpi,
       ref_mv.row = ref_mv.col = 0;
     else
       ref_mv = pc_tree->mv;
+    vp9_setup_src_planes(x, cpi->Source, mi_row, mi_col);
     simple_motion_search(cpi, x, bsize, mi_row, mi_col, ref_mv, ref, pred_buf);
     pc_tree->mv = x->e_mbd.mi[0]->mv[0].as_mv;
   }
@@ -3560,8 +3560,8 @@ static void ml_predict_var_rd_paritioning(const VP9_COMP *const cpi,
     float score;
 
     // Generate model input features.
-    features[feature_idx++] = logf((float)(dc_q * dc_q) / 256.0f + 1.0f);
-    vp9_setup_src_planes(x, cpi->Source, mi_row, mi_col);
+    features[feature_idx++] = logf((float)dc_q + 1.0f);
+
     // Get the variance of the residue as input features.
     {
       const int bs = 4 * num_4x4_blocks_wide_lookup[bsize];
@@ -3575,7 +3575,19 @@ static void ml_predict_var_rd_paritioning(const VP9_COMP *const cpi,
       const unsigned int var =
           cpi->fn_ptr[bsize].vf(src, src_stride, pred, pred_stride, &sse);
       const float factor = (var == 0) ? 1.0f : (1.0f / (float)var);
+      const MACROBLOCKD *const xd = &x->e_mbd;
+      const int has_above = !!xd->above_mi;
+      const int has_left = !!xd->left_mi;
+      const BLOCK_SIZE above_bsize = has_above ? xd->above_mi->sb_type : bsize;
+      const BLOCK_SIZE left_bsize = has_left ? xd->left_mi->sb_type : bsize;
+      int i;
 
+      features[feature_idx++] = (float)has_above;
+      features[feature_idx++] = (float)b_width_log2_lookup[above_bsize];
+      features[feature_idx++] = (float)b_height_log2_lookup[above_bsize];
+      features[feature_idx++] = (float)has_left;
+      features[feature_idx++] = (float)b_width_log2_lookup[left_bsize];
+      features[feature_idx++] = (float)b_height_log2_lookup[left_bsize];
       features[feature_idx++] = logf((float)var + 1.0f);
       for (i = 0; i < 4; ++i) {
         const int x_idx = (i & 1) * bs / 2;
@@ -3604,7 +3616,6 @@ static void ml_predict_var_rd_paritioning(const VP9_COMP *const cpi,
   }
 }
 #undef FEATURES
-#undef LABELS
 
 static int get_rdmult_delta(VP9_COMP *cpi, BLOCK_SIZE bsize, int mi_row,
                             int mi_col, int orig_rdmult) {
diff --git a/vp9/encoder/vp9_partition_models.h b/vp9/encoder/vp9_partition_models.h
index 904d21400..76e3b5d7d 100644
--- a/vp9/encoder/vp9_partition_models.h
+++ b/vp9/encoder/vp9_partition_models.h
@@ -966,175 +966,209 @@ static const NN_CONFIG vp9_var_part_nnconfig_16 = {
 #undef FEATURES
 #endif  // CONFIG_ML_VAR_PARTITION
 
-#define FEATURES 6
+#define FEATURES 12
 #define LABELS 1
-static const float vp9_var_rd_part_nn_weights_64_layer0[FEATURES * 8] = {
-  -0.100129f, 0.128867f,  -1.375086f, -2.268096f, -1.470368f, -2.296274f,
-  0.034445f,  -0.062993f, -2.151904f, 0.523215f,  1.611269f,  1.530051f,
-  0.418182f,  -1.330239f, 0.828388f,  0.386546f,  -0.026188f, -0.055459f,
-  -0.474437f, 0.861295f,  -2.208743f, -0.652991f, -2.985873f, -1.728956f,
-  0.388052f,  -0.420720f, 2.015495f,  1.280342f,  3.040914f,  1.760749f,
-  -0.009062f, 0.009623f,  1.579270f,  -2.012891f, 1.629662f,  -1.796016f,
-  -0.279782f, -0.288359f, 1.875618f,  1.639855f,  0.903020f,  0.906438f,
-  0.553394f,  -1.621589f, 0.185063f,  0.605207f,  -0.133560f, 0.588689f,
+#define NODES 8
+static const float vp9_part_split_nn_weights_64_layer0[FEATURES * NODES] = {
+  -0.609728f, -0.409099f, -0.472449f, 0.183769f,  -0.457740f, 0.081089f,
+  0.171003f,  0.578696f,  -0.019043f, -0.856142f, 0.557369f,  -1.779424f,
+  -0.274044f, -0.320632f, -0.392531f, -0.359462f, -0.404106f, -0.288357f,
+  0.200620f,  0.038013f,  -0.430093f, 0.235083f,  -0.487442f, 0.424814f,
+  -0.232758f, -0.442943f, 0.229397f,  -0.540301f, -0.648421f, -0.649747f,
+  -0.171638f, 0.603824f,  0.468497f,  -0.421580f, 0.178840f,  -0.533838f,
+  -0.029471f, -0.076296f, 0.197426f,  -0.187908f, -0.003950f, -0.065740f,
+  0.085165f,  -0.039674f, -5.640702f, 1.909538f,  -1.434604f, 3.294606f,
+  -0.788812f, 0.196864f,  0.057012f,  -0.019757f, 0.336233f,  0.075378f,
+  0.081503f,  0.491864f,  -1.899470f, -1.764173f, -1.888137f, -1.762343f,
+  0.845542f,  0.202285f,  0.381948f,  -0.150996f, 0.556893f,  -0.305354f,
+  0.561482f,  -0.021974f, -0.703117f, 0.268638f,  -0.665736f, 1.191005f,
+  -0.081568f, -0.115653f, 0.272029f,  -0.140074f, 0.072683f,  0.092651f,
+  -0.472287f, -0.055790f, -0.434425f, 0.352055f,  0.048246f,  0.372865f,
+  0.111499f,  -0.338304f, 0.739133f,  0.156519f,  -0.594644f, 0.137295f,
+  0.613350f,  -0.165102f, -1.003731f, 0.043070f,  -0.887896f, -0.174202f,
 };
 
-static const float vp9_var_rd_part_nn_bias_64_layer0[8] = {
-  0.659717f, 0.120912f, 0.329894f, -1.586385f,
-  1.715839f, 0.085754f, 2.038774f, 0.268119f,
+static const float vp9_part_split_nn_bias_64_layer0[NODES] = {
+  1.182714f,  0.000000f,  0.902019f,  0.953115f,
+  -1.372486f, -1.288740f, -0.155144f, -3.041362f,
 };
 
-static const float vp9_var_rd_part_nn_weights_64_layer1[8 * LABELS] = {
-  -3.445586f, 2.375620f, 1.236970f, 0.804030f,
-  -2.448384f, 2.827254f, 2.291478f, 0.790252f,
+static const float vp9_part_split_nn_weights_64_layer1[NODES * LABELS] = {
+  0.841214f,  0.456016f,  0.869270f, 1.692999f,
+  -1.700494f, -0.911761f, 0.030111f, -1.447548f,
 };
 
-static const float vp9_var_rd_part_nn_bias_64_layer1[LABELS] = {
-  -1.16608453f,
+static const float vp9_part_split_nn_bias_64_layer1[LABELS] = {
+  1.17782545f,
 };
 
-static const NN_CONFIG vp9_var_rd_part_nnconfig_64 = {
+static const NN_CONFIG vp9_part_split_nnconfig_64 = {
   FEATURES,  // num_inputs
   LABELS,    // num_outputs
   1,         // num_hidden_layers
   {
-      8,
+      NODES,
   },  // num_hidden_nodes
   {
-      vp9_var_rd_part_nn_weights_64_layer0,
-      vp9_var_rd_part_nn_weights_64_layer1,
+      vp9_part_split_nn_weights_64_layer0,
+      vp9_part_split_nn_weights_64_layer1,
   },
   {
-      vp9_var_rd_part_nn_bias_64_layer0,
-      vp9_var_rd_part_nn_bias_64_layer1,
+      vp9_part_split_nn_bias_64_layer0,
+      vp9_part_split_nn_bias_64_layer1,
   },
 };
 
-static const float vp9_var_rd_part_nn_weights_32_layer0[FEATURES * 8] = {
-  0.022420f,  -0.032201f, 1.228065f,  -2.767655f, 1.928743f,  0.566863f,
-  0.459229f,  0.422048f,  0.833395f,  0.822960f,  -0.232227f, 0.586895f,
-  0.442856f,  -0.018564f, 0.227672f,  -1.291306f, 0.119428f,  -0.776563f,
-  -0.042947f, 0.183129f,  0.592231f,  1.174859f,  -0.503868f, 0.270102f,
-  -0.330537f, -0.036340f, 1.144630f,  1.783710f,  1.216929f,  2.038085f,
-  0.373782f,  -0.430258f, 1.957002f,  1.383908f,  2.012261f,  1.585693f,
-  -0.394399f, -0.337523f, -0.238335f, 0.007819f,  -0.368294f, 0.437875f,
-  -0.318923f, -0.242000f, 2.276263f,  1.501432f,  0.645706f,  0.344774f,
+static const float vp9_part_split_nn_weights_32_layer0[FEATURES * NODES] = {
+  -0.105488f, -0.218662f, 0.010980f,  -0.226979f, 0.028076f,  0.743430f,
+  0.789266f,  0.031907f,  -1.464200f, 0.222336f,  -1.068493f, -0.052712f,
+  -0.176181f, -0.102654f, -0.973932f, -0.182637f, -0.198000f, 0.335977f,
+  0.271346f,  0.133005f,  1.674203f,  0.689567f,  0.657133f,  0.283524f,
+  0.115529f,  0.738327f,  0.317184f,  -0.179736f, 0.403691f,  0.679350f,
+  0.048925f,  0.271338f,  -1.538921f, -0.900737f, -1.377845f, 0.084245f,
+  0.803122f,  -0.107806f, 0.103045f,  -0.023335f, -0.098116f, -0.127809f,
+  0.037665f,  -0.523225f, 1.622185f,  1.903999f,  1.358889f,  1.680785f,
+  0.027743f,  0.117906f,  -0.158810f, 0.057775f,  0.168257f,  0.062414f,
+  0.086228f,  -0.087381f, -3.066082f, 3.021855f,  -4.092155f, 2.550104f,
+  -0.230022f, -0.207445f, -0.000347f, 0.034042f,  0.097057f,  0.220088f,
+  -0.228841f, -0.029405f, -1.507174f, -1.455184f, 2.624904f,  2.643355f,
+  0.319912f,  0.585531f,  -1.018225f, -0.699606f, 1.026490f,  0.169952f,
+  -0.093579f, -0.142352f, -0.107256f, 0.059598f,  0.043190f,  0.507543f,
+  -0.138617f, 0.030197f,  0.059574f,  -0.634051f, -0.586724f, -0.148020f,
+  -0.334380f, 0.459547f,  1.620600f,  0.496850f,  0.639480f,  -0.465715f,
 };
 
-static const float vp9_var_rd_part_nn_bias_32_layer0[8] = {
-  -0.023846f, -1.348117f, 1.365007f,  -1.644164f,
-  0.062992f,  1.257980f,  -0.098642f, 1.388472f,
+static const float vp9_part_split_nn_bias_32_layer0[NODES] = {
+  -1.125885f, 0.753197f, -0.825808f, 0.004839f,
+  0.583920f,  0.718062f, 0.976741f,  0.796188f,
 };
 
-static const float vp9_var_rd_part_nn_weights_32_layer1[8 * LABELS] = {
-  3.016729f, 0.622684f,  -1.021302f, 1.490383f,
-  1.702046f, -2.964618f, 0.689045f,  1.711754f,
+static const float vp9_part_split_nn_weights_32_layer1[NODES * LABELS] = {
+  -0.458745f, 0.724624f, -0.479720f, -2.199872f,
+  1.162661f,  1.194153f, -0.716896f, 0.824080f,
 };
 
-static const float vp9_var_rd_part_nn_bias_32_layer1[LABELS] = {
-  -1.28798676f,
+static const float vp9_part_split_nn_bias_32_layer1[LABELS] = {
+  0.71644074f,
 };
 
-static const NN_CONFIG vp9_var_rd_part_nnconfig_32 = {
+static const NN_CONFIG vp9_part_split_nnconfig_32 = {
   FEATURES,  // num_inputs
   LABELS,    // num_outputs
   1,         // num_hidden_layers
   {
-      8,
+      NODES,
   },  // num_hidden_nodes
   {
-      vp9_var_rd_part_nn_weights_32_layer0,
-      vp9_var_rd_part_nn_weights_32_layer1,
+      vp9_part_split_nn_weights_32_layer0,
+      vp9_part_split_nn_weights_32_layer1,
   },
   {
-      vp9_var_rd_part_nn_bias_32_layer0,
-      vp9_var_rd_part_nn_bias_32_layer1,
+      vp9_part_split_nn_bias_32_layer0,
+      vp9_part_split_nn_bias_32_layer1,
   },
 };
 
-static const float vp9_var_rd_part_nn_weights_16_layer0[FEATURES * 8] = {
-  -0.726813f, -0.026748f, 1.376946f,  1.467961f,  1.961810f,  1.690412f,
-  0.596484f,  -0.261486f, -0.310905f, -0.366311f, -1.300086f, -0.534336f,
-  0.040520f,  -0.032391f, -1.194214f, 2.438063f,  -3.915334f, 1.997270f,
-  0.673696f,  -0.676393f, 1.654886f,  1.553838f,  1.129691f,  1.360201f,
-  0.255001f,  0.336442f,  -0.487759f, -0.634555f, 0.479170f,  -0.110475f,
-  -0.661852f, -0.158872f, -0.350243f, -0.303957f, -0.045018f, 0.586151f,
-  -0.262463f, 0.228079f,  -1.688776f, -1.594502f, -2.261078f, -1.802535f,
-  0.034748f,  -0.028476f, 2.713258f,  0.212446f,  -1.529202f, -2.560178f,
+static const float vp9_part_split_nn_weights_16_layer0[FEATURES * NODES] = {
+  -0.003629f, -0.046852f, 0.220428f,  -0.033042f, 0.049365f,  0.112818f,
+  -0.306149f, -0.005872f, 1.066947f,  -2.290226f, 2.159505f,  -0.618714f,
+  -0.213294f, 0.451372f,  -0.199459f, 0.223730f,  -0.321709f, 0.063364f,
+  0.148704f,  -0.293371f, 0.077225f,  -0.421947f, -0.515543f, -0.240975f,
+  -0.418516f, 1.036523f,  -0.009165f, 0.032484f,  1.086549f,  0.220322f,
+  -0.247585f, -0.221232f, -0.225050f, 0.993051f,  0.285907f,  1.308846f,
+  0.707456f,  0.335152f,  0.234556f,  0.264590f,  -0.078033f, 0.542226f,
+  0.057777f,  0.163471f,  0.039245f,  -0.725960f, 0.963780f,  -0.972001f,
+  0.252237f,  -0.192745f, -0.836571f, -0.460539f, -0.528713f, -0.160198f,
+  -0.621108f, 0.486405f,  -0.221923f, 1.519426f,  -0.857871f, 0.411595f,
+  0.947188f,  0.203339f,  0.174526f,  0.016382f,  0.256879f,  0.049818f,
+  0.057836f,  -0.659096f, 0.459894f,  0.174695f,  0.379359f,  0.062530f,
+  -0.210201f, -0.355788f, -0.208432f, -0.401723f, -0.115373f, 0.191336f,
+  -0.109342f, 0.002455f,  -0.078746f, -0.391871f, 0.149892f,  -0.239615f,
+  -0.520709f, 0.118568f,  -0.437975f, 0.118116f,  -0.565426f, -0.206446f,
+  0.113407f,  0.558894f,  0.534627f,  1.154350f,  -0.116833f, 1.723311f,
 };
 
-static const float vp9_var_rd_part_nn_bias_16_layer0[8] = {
-  0.495983f,  1.858545f, 0.162974f, 1.992247f,
-  -2.698863f, 0.110020f, 0.550830f, 0.420941f,
+static const float vp9_part_split_nn_bias_16_layer0[NODES] = {
+  0.013109f,  -0.034341f, 0.679845f,  -0.035781f,
+  -0.104183f, 0.098055f,  -0.041130f, 0.160107f,
 };
 
-static const float vp9_var_rd_part_nn_weights_16_layer1[8 * LABELS] = {
-  1.768409f, -1.394240f, 1.076846f,  -1.762808f,
-  1.517405f, 0.535195f,  -0.426827f, 1.002272f,
+static const float vp9_part_split_nn_weights_16_layer1[NODES * LABELS] = {
+  1.499564f, -0.403259f, 1.366532f, -0.469868f,
+  0.482227f, -2.076697f, 0.527691f, 0.540495f,
 };
 
-static const float vp9_var_rd_part_nn_bias_16_layer1[LABELS] = {
-  -1.65894794f,
+static const float vp9_part_split_nn_bias_16_layer1[LABELS] = {
+  0.01134653f,
 };
 
-static const NN_CONFIG vp9_var_rd_part_nnconfig_16 = {
+static const NN_CONFIG vp9_part_split_nnconfig_16 = {
   FEATURES,  // num_inputs
   LABELS,    // num_outputs
   1,         // num_hidden_layers
   {
-      8,
+      NODES,
   },  // num_hidden_nodes
   {
-      vp9_var_rd_part_nn_weights_16_layer0,
-      vp9_var_rd_part_nn_weights_16_layer1,
+      vp9_part_split_nn_weights_16_layer0,
+      vp9_part_split_nn_weights_16_layer1,
   },
   {
-      vp9_var_rd_part_nn_bias_16_layer0,
-      vp9_var_rd_part_nn_bias_16_layer1,
+      vp9_part_split_nn_bias_16_layer0,
+      vp9_part_split_nn_bias_16_layer1,
   },
 };
 
-static const float vp9_var_rd_part_nn_weights_8_layer0[FEATURES * 8] = {
-  -0.804900f, -1.214983f, 0.840202f, 0.686566f,  0.155804f,  0.025542f,
-  -1.244635f, -0.368403f, 0.364150f, 1.081073f,  0.552387f,  0.452715f,
-  0.652968f,  -0.293058f, 0.048967f, 0.021240f,  -0.662981f, 0.424700f,
-  0.008293f,  -0.013088f, 0.747007f, -1.453907f, -1.498226f, 1.593252f,
-  -0.239557f, -0.143766f, 0.064311f, 1.320998f,  -0.477411f, 0.026374f,
-  0.730884f,  -0.675124f, 0.965521f, 0.863658f,  0.809186f,  0.812280f,
-  0.513131f,  0.185102f,  0.211354f, 0.793666f,  0.121714f,  -0.015383f,
-  -0.650980f, -0.046581f, 0.911141f, 0.806319f,  0.974773f,  0.815893f,
+static const float vp9_part_split_nn_weights_8_layer0[FEATURES * NODES] = {
+  -0.668875f, -0.159078f, -0.062663f, -0.483785f, -0.146814f, -0.608975f,
+  -0.589145f, 0.203704f,  -0.051007f, -0.113769f, -0.477511f, -0.122603f,
+  -1.329890f, 1.403386f,  0.199636f,  -0.161139f, 2.182090f,  -0.014307f,
+  0.015755f,  -0.208468f, 0.884353f,  0.815920f,  0.632464f,  0.838225f,
+  1.369483f,  -0.029068f, 0.570213f,  -0.573546f, 0.029617f,  0.562054f,
+  -0.653093f, -0.211910f, -0.661013f, -0.384418f, -0.574038f, -0.510069f,
+  0.173047f,  -0.274231f, -1.044008f, -0.422040f, -0.810296f, 0.144069f,
+  -0.406704f, 0.411230f,  -0.144023f, 0.745651f,  -0.595091f, 0.111787f,
+  0.840651f,  0.030123f,  -0.242155f, 0.101486f,  -0.017889f, -0.254467f,
+  -0.285407f, -0.076675f, -0.549542f, -0.013544f, -0.686566f, -0.755150f,
+  1.623949f,  -0.286369f, 0.170976f,  0.016442f,  -0.598353f, -0.038540f,
+  0.202597f,  -0.933582f, 0.599510f,  0.362273f,  0.577722f,  0.477603f,
+  0.767097f,  0.431532f,  0.457034f,  0.223279f,  0.381349f,  0.033777f,
+  0.423923f,  -0.664762f, 0.385662f,  0.075744f,  0.182681f,  0.024118f,
+  0.319408f,  -0.528864f, 0.976537f,  -0.305971f, -0.189380f, -0.241689f,
+  -1.318092f, 0.088647f,  -0.109030f, -0.945654f, 1.082797f,  0.184564f,
 };
 
-static const float vp9_var_rd_part_nn_bias_8_layer0[8] = {
-  0.176134f, 0.651308f, 2.007761f,  0.068812f,
-  1.061517f, 1.487161f, -2.308147f, 1.099828f,
+static const float vp9_part_split_nn_bias_8_layer0[NODES] = {
+  -0.237472f, 2.051396f,  0.297062f, -0.730194f,
+  0.060472f,  -0.565959f, 0.560869f, -0.395448f,
 };
 
-static const float vp9_var_rd_part_nn_weights_8_layer1[8 * LABELS] = {
-  0.683032f, 1.326393f,  -1.661539f, 1.438920f,
-  1.118023f, -2.237380f, 1.518468f,  2.010416f,
+static const float vp9_part_split_nn_weights_8_layer1[NODES * LABELS] = {
+  0.568121f,  1.575915f,  -0.544309f, 0.751595f,
+  -0.117911f, -1.340730f, -0.739671f, 0.661216f,
 };
 
-static const float vp9_var_rd_part_nn_bias_8_layer1[LABELS] = {
-  -1.65423989f,
+static const float vp9_part_split_nn_bias_8_layer1[LABELS] = {
+  -0.63375306f,
 };
 
-static const NN_CONFIG vp9_var_rd_part_nnconfig_8 = {
+static const NN_CONFIG vp9_part_split_nnconfig_8 = {
   FEATURES,  // num_inputs
   LABELS,    // num_outputs
   1,         // num_hidden_layers
   {
-      8,
+      NODES,
   },  // num_hidden_nodes
   {
-      vp9_var_rd_part_nn_weights_8_layer0,
-      vp9_var_rd_part_nn_weights_8_layer1,
+      vp9_part_split_nn_weights_8_layer0,
+      vp9_part_split_nn_weights_8_layer1,
   },
   {
-      vp9_var_rd_part_nn_bias_8_layer0,
-      vp9_var_rd_part_nn_bias_8_layer1,
+      vp9_part_split_nn_bias_8_layer0,
+      vp9_part_split_nn_bias_8_layer1,
   },
 };
+#undef NODES
 #undef FEATURES
 #undef LABELS