Add ml_var_partition experiment

Make partition decisions using machine learning models. The goal is to
achieve better coding quality than the variance-based parititioning
without much encoding speed loss.

To enable this experiment, use --enable-ml-var-partition for config.

When eanbled, the variance-based partitioning is replaced by this ML
based partitioing for speed 6 and above in real time mode(except low
resolution or high bit-depth).

Current coding gains(average PSNR):
                speed 6      speed 7      speed 8
rtc              2.04%        2.65%        3.90%
ytlivehr         3.11%        4.53%       11.57%
hdres(rtc mode)  5.10%

Further testing and tuning is needed to see if the speed and quality
tradeoff is reasonable.

Change-Id: I0da5a2fbc22c3261832b32920ee36d9b19d417af

1 files changed, 130 insertions, 0 deletions

diff --git a/vp9/encoder/vp9_partition_models.h b/vp9/encoder/vp9_partition_models.h
index 19979e531..17c9013da 100644
--- a/vp9/encoder/vp9_partition_models.h
+++ b/vp9/encoder/vp9_partition_models.h
@@ -834,6 +834,136 @@ static const NN_CONFIG vp9_partition_nnconfig_16x16 = {
 };
 #undef FEATURES
 
+#if CONFIG_ML_VAR_PARTITION
+#define FEATURES 6
+static const float vp9_var_part_nn_weights_64_layer0[FEATURES * 8] = {
+  -0.249572f, 0.205532f,  -2.175608f, 1.094836f,  -2.986370f, 0.193160f,
+  -0.143823f, 0.378511f,  -1.997788f, -2.166866f, -1.930158f, -1.202127f,
+  -0.611875f, -0.506422f, -0.432487f, 0.071205f,  0.578172f,  -0.154285f,
+  -0.051830f, 0.331681f,  -1.457177f, -2.443546f, -2.000302f, -1.389283f,
+  0.372084f,  -0.464917f, 2.265235f,  2.385787f,  2.312722f,  2.127868f,
+  -0.403963f, -0.177860f, -0.436751f, -0.560539f, 0.254903f,  0.193976f,
+  -0.305611f, 0.256632f,  0.309388f,  -0.437439f, 1.702640f,  -5.007069f,
+  -0.323450f, 0.294227f,  1.267193f,  1.056601f,  0.387181f,  -0.191215f,
+};
+
+static const float vp9_var_part_nn_bias_64_layer0[8] = {
+  -0.044396f, -0.938166f, 0.000000f,  -0.916375f,
+  1.242299f,  0.000000f,  -0.405734f, 0.014206f,
+};
+
+static const float vp9_var_part_nn_weights_64_layer1[8] = {
+  1.635945f,  0.979557f,  0.455315f, 1.197199f,
+  -2.251024f, -0.464953f, 1.378676f, -0.111927f,
+};
+
+static const float vp9_var_part_nn_bias_64_layer1[1] = {
+  -0.37972447f,
+};
+
+static const NN_CONFIG vp9_var_part_nnconfig_64 = {
+  FEATURES,  // num_inputs
+  1,         // num_outputs
+  1,         // num_hidden_layers
+  {
+      8,
+  },  // num_hidden_nodes
+  {
+      vp9_var_part_nn_weights_64_layer0,
+      vp9_var_part_nn_weights_64_layer1,
+  },
+  {
+      vp9_var_part_nn_bias_64_layer0,
+      vp9_var_part_nn_bias_64_layer1,
+  },
+};
+
+static const float vp9_var_part_nn_weights_32_layer0[FEATURES * 8] = {
+  0.067243f,  -0.083598f, -2.191159f, 2.726434f,  -3.324013f, 3.477977f,
+  0.323736f,  -0.510199f, 2.960693f,  2.937661f,  2.888476f,  2.938315f,
+  -0.307602f, -0.503353f, -0.080725f, -0.473909f, -0.417162f, 0.457089f,
+  0.665153f,  -0.273210f, 0.028279f,  0.972220f,  -0.445596f, 1.756611f,
+  -0.177892f, -0.091758f, 0.436661f,  -0.521506f, 0.133786f,  0.266743f,
+  0.637367f,  -0.160084f, -1.396269f, 1.020841f,  -1.112971f, 0.919496f,
+  -0.235883f, 0.651954f,  0.109061f,  -0.429463f, 0.740839f,  -0.962060f,
+  0.299519f,  -0.386298f, 1.550231f,  2.464915f,  1.311969f,  2.561612f,
+};
+
+static const float vp9_var_part_nn_bias_32_layer0[8] = {
+  0.368242f, 0.736617f, 0.000000f,  0.757287f,
+  0.000000f, 0.613248f, -0.776390f, 0.928497f,
+};
+
+static const float vp9_var_part_nn_weights_32_layer1[8] = {
+  0.939884f, -2.420850f, -0.410489f, -0.186690f,
+  0.063287f, -0.522011f, 0.484527f,  -0.639625f,
+};
+
+static const float vp9_var_part_nn_bias_32_layer1[1] = {
+  -0.6455006f,
+};
+
+static const NN_CONFIG vp9_var_part_nnconfig_32 = {
+  FEATURES,  // num_inputs
+  1,         // num_outputs
+  1,         // num_hidden_layers
+  {
+      8,
+  },  // num_hidden_nodes
+  {
+      vp9_var_part_nn_weights_32_layer0,
+      vp9_var_part_nn_weights_32_layer1,
+  },
+  {
+      vp9_var_part_nn_bias_32_layer0,
+      vp9_var_part_nn_bias_32_layer1,
+  },
+};
+
+static const float vp9_var_part_nn_weights_16_layer0[FEATURES * 8] = {
+  0.742567f,  -0.580624f, -0.244528f, 0.331661f,  -0.113949f, -0.559295f,
+  -0.386061f, 0.438653f,  1.467463f,  0.211589f,  0.513972f,  1.067855f,
+  -0.876679f, 0.088560f,  -0.687483f, -0.380304f, -0.016412f, 0.146380f,
+  0.015318f,  0.000351f,  -2.764887f, 3.269717f,  2.752428f,  -2.236754f,
+  0.561539f,  -0.852050f, -0.084667f, 0.202057f,  0.197049f,  0.364922f,
+  -0.463801f, 0.431790f,  1.872096f,  -0.091887f, -0.055034f, 2.443492f,
+  -0.156958f, -0.189571f, -0.542424f, -0.589804f, -0.354422f, 0.401605f,
+  0.642021f,  -0.875117f, 2.040794f,  1.921070f,  1.792413f,  1.839727f,
+};
+
+static const float vp9_var_part_nn_bias_16_layer0[8] = {
+  2.901234f, -1.940932f, -0.198970f, -0.406524f,
+  0.059422f, -1.879207f, -0.232340f, 2.979821f,
+};
+
+static const float vp9_var_part_nn_weights_16_layer1[8] = {
+  -0.528731f, 0.375234f, -0.088422f, 0.668629f,
+  0.870449f,  0.578735f, 0.546103f,  -1.957207f,
+};
+
+static const float vp9_var_part_nn_bias_16_layer1[1] = {
+  -1.95769405f,
+};
+
+static const NN_CONFIG vp9_var_part_nnconfig_16 = {
+  FEATURES,  // num_inputs
+  1,         // num_outputs
+  1,         // num_hidden_layers
+  {
+      8,
+  },  // num_hidden_nodes
+  {
+      vp9_var_part_nn_weights_16_layer0,
+      vp9_var_part_nn_weights_16_layer1,
+  },
+  {
+      vp9_var_part_nn_bias_16_layer0,
+      vp9_var_part_nn_bias_16_layer1,
+  },
+};
+#undef FEATURES
+#endif  // CONFIG_ML_VAR_PARTITION
+
 // Partition pruning model(linear).
 static const float vp9_partition_feature_mean[24] = {
   303501.697372f, 3042630.372158f, 24.694696f, 1.392182f,