ref: efc195cbb9c50925a9479f0a76c594543d22a66e
parent: af355dacd568dfe5c109ae2d6d22104f94cdcf7f
author: Hui Su <[email protected]>
date: Mon May 14 10:35:25 EDT 2018
Improve the ML based partition pruning Add a neural net model that uses the same features as the existing linear model. Make the pruning decision based on both the linear and the neural net model. It provides more accurate predictions, and may improve compression and/or encoding speed. This only affects speed 0. Coding gain: 0.37% on midres 0.34% on hdres 0.50% on jvet8b720p Encoding speed impact(average over locally tested 20 clips from midres and hdres): QP=20: down by 2.5%. QP=30: down by 3.9%. QP=40: donw by 4.5%. QP=50: up by 5.2%. Change-Id: I402ec799745ad3b74abf0789fa5e124fe64e704d
--- a/vp9/encoder/vp9_encodeframe.c
+++ b/vp9/encoder/vp9_encodeframe.c
@@ -52,33 +52,6 @@
int output_enabled, int mi_row, int mi_col,
BLOCK_SIZE bsize, PICK_MODE_CONTEXT *ctx);
-// Machine learning-based early termination parameters.
-static const double train_mean[24] = {- 303501.697372, 3042630.372158, 24.694696, 1.392182,
- 689.413511, 162.027012, 1.478213, 0.0,
- 135382.260230, 912738.513263, 28.845217, 1.515230,
- 544.158492, 131.807995, 1.436863, 0.0,
- 43682.377587, 208131.711766, 28.084737, 1.356677,
- 138.254122, 119.522553, 1.252322, 0.0
-};
-
-static const double train_stdm[24] = {- 673689.212982, 5996652.516628, 0.024449, 1.989792,
- 985.880847, 0.014638, 2.001898, 0.0,
- 208798.775332, 1812548.443284, 0.018693, 1.838009,
- 396.986910, 0.015657, 1.332541, 0.0,
- 55888.847031, 448587.962714, 0.017900, 1.904776,
- 98.652832, 0.016598, 1.320992, 0.0
-};
-
-// Error tolerance: 0.01%-0.0.05%-0.1%
-static const double classifiers[24] = {- 0.111736, 0.289977, 0.042219, 0.204765, 0.120410, -0.143863,
- 0.282376, 0.847811, 0.637161, 0.131570, 0.018636, 0.202134,
- 0.112797, 0.028162, 0.182450, 1.124367, 0.386133, 0.083700,
- 0.050028, 0.150873, 0.061119, 0.109318, 0.127255, 0.625211
-};
-
// This is used as a reference when computing the source variance for the
// purpose of activity masking.
// Eventually this should be replaced by custom no-reference routines,
@@ -3030,14 +3003,232 @@
}
#endif
-// Calculate the score used in machine-learning based partition search early
-// termination.
-static double compute_score(VP9_COMMON *const cm, MACROBLOCKD *const xd,
- PICK_MODE_CONTEXT *ctx, int mi_row, int mi_col,
- BLOCK_SIZE bsize) {- const double *clf;
- const double *mean;
- const double *sd;
+#define NN_MAX_HIDDEN_LAYERS 10
+#define NN_MAX_NODES_PER_LAYER 128
+
+// Neural net model config.
+typedef struct {+ int num_inputs; // Number of input nodes, i.e. features.
+ int num_outputs; // Number of output nodes.
+ int num_hidden_layers; // Number of hidden layers, maximum 10.
+ // Number of nodes for each hidden layer.
+ int num_hidden_nodes[NN_MAX_HIDDEN_LAYERS];
+ // Weight parameters, indexed by layer.
+ const float *weights[NN_MAX_HIDDEN_LAYERS + 1];
+ // Bias parameters, indexed by layer.
+ const float *bias[NN_MAX_HIDDEN_LAYERS + 1];
+} NN_CONFIG;
+
+// Calculate prediction based on the given input features and neural net config.
+// Assume there are no more than NN_MAX_NODES_PER_LAYER nodes in each hidden
+// layer.
+static void nn_predict(const float *features, const NN_CONFIG *nn_config,
+ float *output) {+ int num_input_nodes = nn_config->num_inputs;
+ int buf_index = 0;
+ float buf[2][NN_MAX_NODES_PER_LAYER];
+ const float *input_nodes = features;
+
+ // Propagate hidden layers.
+ const int num_layers = nn_config->num_hidden_layers;
+ int layer, node, i;
+ assert(num_layers <= NN_MAX_HIDDEN_LAYERS);
+ for (layer = 0; layer < num_layers; ++layer) {+ const float *weights = nn_config->weights[layer];
+ const float *bias = nn_config->bias[layer];
+ float *output_nodes = buf[buf_index];
+ const int num_output_nodes = nn_config->num_hidden_nodes[layer];
+ assert(num_output_nodes < NN_MAX_NODES_PER_LAYER);
+ for (node = 0; node < num_output_nodes; ++node) {+ float val = 0.0f;
+ for (i = 0; i < num_input_nodes; ++i) val += weights[i] * input_nodes[i];
+ val += bias[node];
+ // ReLU as activation function.
+ val = VPXMAX(val, 0.0f);
+ output_nodes[node] = val;
+ weights += num_input_nodes;
+ }
+ num_input_nodes = num_output_nodes;
+ input_nodes = output_nodes;
+ buf_index = 1 - buf_index;
+ }
+
+ // Final output layer.
+ {+ const float *weights = nn_config->weights[num_layers];
+ for (node = 0; node < nn_config->num_outputs; ++node) {+ const float *bias = nn_config->bias[num_layers];
+ float val = 0.0f;
+ for (i = 0; i < num_input_nodes; ++i) val += weights[i] * input_nodes[i];
+ output[node] = val + bias[node];
+ weights += num_input_nodes;
+ }
+ }
+}
+
+static const float partition_nn_weights_64x64_layer0[7 * 8] = {+ -3.571348f, 0.014835f, -3.255393f, -0.098090f, -0.013120f, 0.000221f,
+ 0.056273f, 0.190179f, -0.268130f, -1.828242f, -0.010655f, 0.937244f,
+ -0.435120f, 0.512125f, 1.610679f, 0.190816f, -0.799075f, -0.377348f,
+ -0.144232f, 0.614383f, -0.980388f, 1.754150f, -0.185603f, -0.061854f,
+ -0.807172f, 1.240177f, 1.419531f, -0.438544f, -5.980774f, 0.139045f,
+ -0.032359f, -0.068887f, -1.237918f, 0.115706f, 0.003164f, 2.924212f,
+ 1.246838f, -0.035833f, 0.810011f, -0.805894f, 0.010966f, 0.076463f,
+ -4.226380f, -2.437764f, -0.010619f, -0.020935f, -0.451494f, 0.300079f,
+ -0.168961f, -3.326450f, -2.731094f, 0.002518f, 0.018840f, -1.656815f,
+ 0.068039f, 0.010586f,
+};
+
+static const float partition_nn_bias_64x64_layer0[8] = {+ -3.469882f, 0.683989f, 0.194010f, 0.313782f,
+ -3.153335f, 2.245849f, -1.946190f, -3.740020f,
+};
+
+static const float partition_nn_weights_64x64_layer1[8] = {+ -8.058566f, 0.108306f, -0.280620f, -0.818823f,
+ -6.445117f, 0.865364f, -1.127127f, -8.808660f,
+};
+
+static const float partition_nn_bias_64x64_layer1[1] = {+ 6.46909416f,
+};
+
+static const NN_CONFIG partition_nnconfig_64x64 = {+ 7, // num_inputs
+ 1, // num_outputs
+ 1, // num_hidden_layers
+ {+ 8,
+ }, // num_hidden_nodes
+ {+ partition_nn_weights_64x64_layer0,
+ partition_nn_weights_64x64_layer1,
+ },
+ {+ partition_nn_bias_64x64_layer0,
+ partition_nn_bias_64x64_layer1,
+ },
+};
+
+static const float partition_nn_weights_32x32_layer0[7 * 8] = {+ -0.295437f, -4.002648f, -0.205399f, -0.060919f, 0.708037f, 0.027221f,
+ -0.039137f, -0.907724f, -3.151662f, 0.007106f, 0.018726f, -0.534928f,
+ 0.022744f, 0.000159f, -1.717189f, -3.229031f, -0.027311f, 0.269863f,
+ -0.400747f, -0.394366f, -0.108878f, 0.603027f, 0.455369f, -0.197170f,
+ 1.241746f, -1.347820f, -0.575636f, -0.462879f, -2.296426f, 0.196696f,
+ -0.138347f, -0.030754f, -0.200774f, 0.453795f, 0.055625f, -3.163116f,
+ -0.091003f, -0.027028f, -0.042984f, -0.605185f, 0.143240f, -0.036439f,
+ -0.801228f, 0.313409f, -0.159942f, 0.031267f, 0.886454f, -1.531644f,
+ -0.089655f, 0.037683f, -0.163441f, -0.130454f, -0.058344f, 0.060011f,
+ 0.275387f, 1.552226f,
+};
+
+static const float partition_nn_bias_32x32_layer0[8] = {+ -0.838372f, -2.609089f, -0.055763f, 1.329485f,
+ -1.297638f, -2.636622f, -0.826909f, 1.012644f,
+};
+
+static const float partition_nn_weights_32x32_layer1[8] = {+ -1.792632f, -7.322353f, -0.683386f, 0.676564f,
+ -1.488118f, -7.527719f, 1.240163f, 0.614309f,
+};
+
+static const float partition_nn_bias_32x32_layer1[1] = {+ 4.97422546f,
+};
+
+static const NN_CONFIG partition_nnconfig_32x32 = {+ 7, // num_inputs
+ 1, // num_outputs
+ 1, // num_hidden_layers
+ {+ 8,
+ }, // num_hidden_nodes
+ {+ partition_nn_weights_32x32_layer0,
+ partition_nn_weights_32x32_layer1,
+ },
+ {+ partition_nn_bias_32x32_layer0,
+ partition_nn_bias_32x32_layer1,
+ },
+};
+
+static const float partition_nn_weights_16x16_layer0[7 * 8] = {+ -1.717673f, -4.718130f, -0.125725f, -0.183427f, -0.511764f, 0.035328f,
+ 0.130891f, -3.096753f, 0.174968f, -0.188769f, -0.640796f, 1.305661f,
+ 1.700638f, -0.073806f, -4.006781f, -1.630999f, -0.064863f, -0.086410f,
+ -0.148617f, 0.172733f, -0.018619f, 2.152595f, 0.778405f, -0.156455f,
+ 0.612995f, -0.467878f, 0.152022f, -0.236183f, 0.339635f, -0.087119f,
+ -3.196610f, -1.080401f, -0.637704f, -0.059974f, 1.706298f, -0.793705f,
+ -6.399260f, 0.010624f, -0.064199f, -0.650621f, 0.338087f, -0.001531f,
+ 1.023655f, -3.700272f, -0.055281f, -0.386884f, 0.375504f, -0.898678f,
+ 0.281156f, -0.314611f, 0.863354f, -0.040582f, -0.145019f, 0.029329f,
+ -2.197880f, -0.108733f,
+};
+
+static const float partition_nn_bias_16x16_layer0[8] = {+ 0.411516f, -2.143737f, -3.693192f, 2.123142f,
+ -1.356910f, -3.561016f, -0.765045f, -2.417082f,
+};
+
+static const float partition_nn_weights_16x16_layer1[8] = {+ -0.619755f, -2.202391f, -4.337171f, 0.611319f,
+ 0.377677f, -4.998723f, -1.052235f, 1.949922f,
+};
+
+static const float partition_nn_bias_16x16_layer1[1] = {+ 3.20981717f,
+};
+
+static const NN_CONFIG partition_nnconfig_16x16 = {+ 7, // num_inputs
+ 1, // num_outputs
+ 1, // num_hidden_layers
+ {+ 8,
+ }, // num_hidden_nodes
+ {+ partition_nn_weights_16x16_layer0,
+ partition_nn_weights_16x16_layer1,
+ },
+ {+ partition_nn_bias_16x16_layer0,
+ partition_nn_bias_16x16_layer1,
+ },
+};
+
+static const float partition_feature_mean[24] = {+ 303501.697372f, 3042630.372158f, 24.694696f, 1.392182f,
+ 689.413511f, 162.027012f, 1.478213f, 0.0,
+ 135382.260230f, 912738.513263f, 28.845217f, 1.515230f,
+ 544.158492f, 131.807995f, 1.436863f, 0.0f,
+ 43682.377587f, 208131.711766f, 28.084737f, 1.356677f,
+ 138.254122f, 119.522553f, 1.252322f, 0.0f,
+};
+
+static const float partition_feature_std[24] = {+ 673689.212982f, 5996652.516628f, 0.024449f, 1.989792f,
+ 985.880847f, 0.014638f, 2.001898f, 0.0f,
+ 208798.775332f, 1812548.443284f, 0.018693f, 1.838009f,
+ 396.986910f, 0.015657f, 1.332541f, 0.0f,
+ 55888.847031f, 448587.962714f, 0.017900f, 1.904776f,
+ 98.652832f, 0.016598f, 1.320992f, 0.0f,
+};
+
+// Error tolerance: 0.01%-0.0.05%-0.1%
+static const float partition_linear_weights[24] = {+ 0.111736f, 0.289977f, 0.042219f, 0.204765f, 0.120410f, -0.143863f,
+ 0.282376f, 0.847811f, 0.637161f, 0.131570f, 0.018636f, 0.202134f,
+ 0.112797f, 0.028162f, 0.182450f, 1.124367f, 0.386133f, 0.083700f,
+ 0.050028f, 0.150873f, 0.061119f, 0.109318f, 0.127255f, 0.625211f,
+};
+
+// Machine-learning based partition search early termination.
+// Return 1 to skip split and rect partitions.
+static int ml_pruning_partition(VP9_COMMON *const cm, MACROBLOCKD *const xd,
+ PICK_MODE_CONTEXT *ctx, int mi_row, int mi_col,
+ BLOCK_SIZE bsize) {const int mag_mv =
abs(ctx->mic.mv[0].as_mv.col) + abs(ctx->mic.mv[0].as_mv.row);
const int left_in_image = !!xd->left_mi;
@@ -3047,12 +3238,33 @@
int above_par = 0; // above_partitioning
int left_par = 0; // left_partitioning
int last_par = 0; // last_partitioning
- BLOCK_SIZE context_size;
- double score;
int offset = 0;
+ int i;
+ BLOCK_SIZE context_size;
+ const NN_CONFIG *nn_config = NULL;
+ const float *mean, *sd, *linear_weights;
+ float nn_score, linear_score;
+ float features[7];
assert(b_width_log2_lookup[bsize] == b_height_log2_lookup[bsize]);
+ vpx_clear_system_state();
+ switch (bsize) {+ case BLOCK_64X64:
+ offset = 0;
+ nn_config = &partition_nnconfig_64x64;
+ break;
+ case BLOCK_32X32:
+ offset = 8;
+ nn_config = &partition_nnconfig_32x32;
+ break;
+ case BLOCK_16X16:
+ offset = 16;
+ nn_config = &partition_nnconfig_16x16;
+ break;
+ default: assert(0 && "Unexpected block size."); return 0;
+ }
+
if (above_in_image) {context_size = xd->above_mi->sb_type;
if (context_size < bsize)
@@ -3077,25 +3289,27 @@
last_par = 1;
}
- if (bsize == BLOCK_64X64)
- offset = 0;
- else if (bsize == BLOCK_32X32)
- offset = 8;
- else if (bsize == BLOCK_16X16)
- offset = 16;
+ mean = &partition_feature_mean[offset];
+ sd = &partition_feature_std[offset];
+ features[0] = ((float)ctx->rate - mean[0]) / sd[0];
+ features[1] = ((float)ctx->dist - mean[1]) / sd[1];
+ features[2] = ((float)mag_mv / 2 - mean[2]) * sd[2];
+ features[3] = ((float)(left_par + above_par) / 2 - mean[3]) * sd[3];
+ features[4] = ((float)ctx->sum_y_eobs - mean[4]) / sd[4];
+ features[5] = ((float)cm->base_qindex - mean[5]) * sd[5];
+ features[6] = ((float)last_par - mean[6]) * sd[6];
- // early termination score calculation
- clf = &classifiers[offset];
- mean = &train_mean[offset];
- sd = &train_stdm[offset];
- score = clf[0] * (((double)ctx->rate - mean[0]) / sd[0]) +
- clf[1] * (((double)ctx->dist - mean[1]) / sd[1]) +
- clf[2] * (((double)mag_mv / 2 - mean[2]) * sd[2]) +
- clf[3] * (((double)(left_par + above_par) / 2 - mean[3]) * sd[3]) +
- clf[4] * (((double)ctx->sum_y_eobs - mean[4]) / sd[4]) +
- clf[5] * (((double)cm->base_qindex - mean[5]) * sd[5]) +
- clf[6] * (((double)last_par - mean[6]) * sd[6]) + clf[7];
- return score;
+ // Predict using linear model.
+ linear_weights = &partition_linear_weights[offset];
+ linear_score = linear_weights[7];
+ for (i = 0; i < 7; ++i) linear_score += linear_weights[i] * features[i];
+
+ // Predict using neural net model.
+ nn_predict(features, nn_config, &nn_score);
+
+ if (linear_score < -0.0f && nn_score < 0.1f) return 1;
+ if (nn_score < -0.0f && linear_score < 0.1f) return 1;
+ return 0;
}
// TODO(jingning,jimbankoski,rbultje): properly skip partition types that are
@@ -3297,7 +3511,7 @@
if (!x->e_mbd.lossless &&
!segfeature_active(&cm->seg, mi->segment_id, SEG_LVL_SKIP) &&
ctx->mic.mode >= INTRA_MODES && bsize >= BLOCK_16X16) {- if (compute_score(cm, xd, ctx, mi_row, mi_col, bsize) < 0.0) {+ if (ml_pruning_partition(cm, xd, ctx, mi_row, mi_col, bsize)) {do_split = 0;
do_rect = 0;
}