/* * Copyright (c) 2016, 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. /*! \file * Contains the details of the ML models used for pruning transform size. This * file is only included by av1/encoder/tx_search.c. #ifndef AOM_AV1_ENCODER_TX_PRUNE_MODEL_WEIGHTS_H_#define AOM_AV1_ENCODER_TX_PRUNE_MODEL_WEIGHTS_H_#ifdef __cplusplusextern "C" {#endif #include "av1/encoder/ml.h" /***************************CONFIG_NN_V2 (New)********************************/ #if CONFIG_NN_V2// Tx type model for 4x4 block. static float av1_tx_type_nn_4x4_hor_layer0_weights[32] = {static float av1_tx_type_nn_4x4_hor_layer0_bias[8] = {static float av1_tx_type_nn_4x4_hor_layer1_weights[32] = {static float av1_tx_type_nn_4x4_hor_layer1_bias[4] = {static float av1_tx_type_nn_4x4_hor_layer0_out[8] = { 0 };static float av1_tx_type_nn_4x4_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_4x4_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_4x4_ver_layer0_weights[32] = {static float av1_tx_type_nn_4x4_ver_layer0_bias[8] = {static float av1_tx_type_nn_4x4_ver_layer1_weights[32] = {static float av1_tx_type_nn_4x4_ver_layer1_bias[4] = {static float av1_tx_type_nn_4x4_ver_layer0_out[8] = { 0 };static float av1_tx_type_nn_4x4_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_4x4_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 4x8 block. static float av1_tx_type_nn_4x8_hor_layer0_weights[32] = {static float av1_tx_type_nn_4x8_hor_layer0_bias[8] = {static float av1_tx_type_nn_4x8_hor_layer1_weights[32] = {static float av1_tx_type_nn_4x8_hor_layer1_bias[4] = {static float av1_tx_type_nn_4x8_hor_layer0_out[8] = { 0 };static float av1_tx_type_nn_4x8_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_4x8_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_4x8_ver_layer0_weights[128] = {static float av1_tx_type_nn_4x8_ver_layer0_bias[16] = {static float av1_tx_type_nn_4x8_ver_layer1_weights[64] = {static float av1_tx_type_nn_4x8_ver_layer1_bias[4] = {static float av1_tx_type_nn_4x8_ver_layer0_out[16] = { 0 };static float av1_tx_type_nn_4x8_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_4x8_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 8x4 block. static float av1_tx_type_nn_8x4_hor_layer0_weights[128] = {static float av1_tx_type_nn_8x4_hor_layer0_bias[16] = {static float av1_tx_type_nn_8x4_hor_layer1_weights[64] = {static float av1_tx_type_nn_8x4_hor_layer1_bias[4] = {static float av1_tx_type_nn_8x4_hor_layer0_out[16] = { 0 };static float av1_tx_type_nn_8x4_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_8x4_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_8x4_ver_layer0_weights[32] = {static float av1_tx_type_nn_8x4_ver_layer0_bias[8] = {static float av1_tx_type_nn_8x4_ver_layer1_weights[32] = {static float av1_tx_type_nn_8x4_ver_layer1_bias[4] = {static float av1_tx_type_nn_8x4_ver_layer0_out[8] = { 0 };static float av1_tx_type_nn_8x4_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_8x4_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 8x8 block. static float av1_tx_type_nn_8x8_hor_layer0_weights[128] = {static float av1_tx_type_nn_8x8_hor_layer0_bias[16] = {static float av1_tx_type_nn_8x8_hor_layer1_weights[64] = {static float av1_tx_type_nn_8x8_hor_layer1_bias[4] = {static float av1_tx_type_nn_8x8_hor_layer0_out[16] = { 0 };static float av1_tx_type_nn_8x8_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_8x8_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_8x8_ver_layer0_weights[128] = {static float av1_tx_type_nn_8x8_ver_layer0_bias[16] = {static float av1_tx_type_nn_8x8_ver_layer1_weights[64] = {static float av1_tx_type_nn_8x8_ver_layer1_bias[4] = {static float av1_tx_type_nn_8x8_ver_layer0_out[16] = { 0 };static float av1_tx_type_nn_8x8_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_8x8_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 8x16 block. static float av1_tx_type_nn_8x16_hor_layer0_weights[128] = {static float av1_tx_type_nn_8x16_hor_layer0_bias[16] = {static float av1_tx_type_nn_8x16_hor_layer1_weights[64] = {static float av1_tx_type_nn_8x16_hor_layer1_bias[4] = {static float av1_tx_type_nn_8x16_hor_layer0_out[16] = { 0 };static float av1_tx_type_nn_8x16_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_8x16_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_8x16_ver_layer0_weights[128] = {static float av1_tx_type_nn_8x16_ver_layer0_bias[16] = {static float av1_tx_type_nn_8x16_ver_layer1_weights[64] = {static float av1_tx_type_nn_8x16_ver_layer1_bias[4] = {static float av1_tx_type_nn_8x16_ver_layer0_out[16] = { 0 };static float av1_tx_type_nn_8x16_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_8x16_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 16x8 block. static float av1_tx_type_nn_16x8_hor_layer0_weights[128] = {static float av1_tx_type_nn_16x8_hor_layer0_bias[16] = {static float av1_tx_type_nn_16x8_hor_layer1_weights[64] = {static float av1_tx_type_nn_16x8_hor_layer1_bias[4] = {static float av1_tx_type_nn_16x8_hor_layer0_out[16] = { 0 };static float av1_tx_type_nn_16x8_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_16x8_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_16x8_ver_layer0_weights[128] = {static float av1_tx_type_nn_16x8_ver_layer0_bias[16] = {static float av1_tx_type_nn_16x8_ver_layer1_weights[64] = {static float av1_tx_type_nn_16x8_ver_layer1_bias[4] = {static float av1_tx_type_nn_16x8_ver_layer0_out[16] = { 0 };static float av1_tx_type_nn_16x8_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_16x8_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 16x16 block. static float av1_tx_type_nn_16x16_layer0_weights[128] = {static float av1_tx_type_nn_16x16_layer0_bias[16] = {static float av1_tx_type_nn_16x16_layer1_weights[64] = {static float av1_tx_type_nn_16x16_layer1_bias[4] = {static float av1_tx_type_nn_16x16_layer0_out[16] = { 0 };static float av1_tx_type_nn_16x16_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_16x16 = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 4x16 block. static float av1_tx_type_nn_4x16_hor_layer0_weights[32] = {static float av1_tx_type_nn_4x16_hor_layer0_bias[8] = {static float av1_tx_type_nn_4x16_hor_layer1_weights[32] = {static float av1_tx_type_nn_4x16_hor_layer1_bias[4] = {static float av1_tx_type_nn_4x16_hor_layer0_out[8] = { 0 };static float av1_tx_type_nn_4x16_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_4x16_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_4x16_ver_layer0_weights[128] = {static float av1_tx_type_nn_4x16_ver_layer0_bias[16] = {static float av1_tx_type_nn_4x16_ver_layer1_weights[64] = {static float av1_tx_type_nn_4x16_ver_layer1_bias[4] = {static float av1_tx_type_nn_4x16_ver_layer0_out[16] = { 0 };static float av1_tx_type_nn_4x16_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_4x16_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Tx type model for 16x4 block. static float av1_tx_type_nn_16x4_hor_layer0_weights[128] = {static float av1_tx_type_nn_16x4_hor_layer0_bias[16] = {static float av1_tx_type_nn_16x4_hor_layer1_weights[64] = {static float av1_tx_type_nn_16x4_hor_layer1_bias[4] = {static float av1_tx_type_nn_16x4_hor_layer0_out[16] = { 0 };static float av1_tx_type_nn_16x4_hor_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_16x4_hor = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) static float av1_tx_type_nn_16x4_ver_layer0_weights[32] = {static float av1_tx_type_nn_16x4_ver_layer0_bias[8] = {static float av1_tx_type_nn_16x4_ver_layer1_weights[32] = {static float av1_tx_type_nn_16x4_ver_layer1_bias[4] = {static float av1_tx_type_nn_16x4_ver_layer0_out[8] = { 0 };static float av1_tx_type_nn_16x4_ver_layer1_out[4] = { 0 };static NN_CONFIG_V2 av1_tx_type_nnconfig_16x4_ver = {// num_hidden_layers // fc layer setting // layer 0 // num_inputs // num_outputs // weights // bias // activation // output // num_inputs (!!same as num_outputs of last layer) // num_outputs // logits (!!same as last layer output) /******************************************************************************/ // Map tx_size to its corresponding neural net model for tx type prediction. static NN_CONFIG_V2 *av1_tx_type_nnconfig_map_hor[] = {// 4x4 transform // 8x8 transform // 16x16 transform // 32x32 transform // 64x64 transform // 4x8 transform // 8x4 transform // 8x16 transform // 16x8 transform // 16x32 transform // 32x16 transform // 32x64 transform // 64x32 transform // 4x16 transform // 16x4 transform // 8x32 transform // 32x8 transform // 16x64 transform // 64x16 transform static NN_CONFIG_V2 *av1_tx_type_nnconfig_map_ver[] = {// 4x4 transform // 8x8 transform // 16x16 transform // 32x32 transform // 64x64 transform // 4x8 transform // 8x4 transform // 8x16 transform // 16x8 transform // 16x32 transform // 32x16 transform // 32x64 transform // 64x32 transform // 4x16 transform // 16x4 transform // 8x32 transform // 32x8 transform // 16x64 transform // 64x16 transform #else /******************************CONFIG_NN***************************************/ // Tx type model for 4x4 block. static const float av1_tx_type_nn_weights_4x4_hor_layer0[32] = {static const float av1_tx_type_nn_bias_4x4_hor_layer0[8] = {static const float av1_tx_type_nn_weights_4x4_hor_layer1[32] = {static const float av1_tx_type_nn_bias_4x4_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_4x4_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_4x4_ver_layer0[32] = {static const float av1_tx_type_nn_bias_4x4_ver_layer0[8] = {static const float av1_tx_type_nn_weights_4x4_ver_layer1[32] = {static const float av1_tx_type_nn_bias_4x4_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_4x4_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 4x8 block. static const float av1_tx_type_nn_weights_4x8_hor_layer0[32] = {static const float av1_tx_type_nn_bias_4x8_hor_layer0[8] = {static const float av1_tx_type_nn_weights_4x8_hor_layer1[32] = {static const float av1_tx_type_nn_bias_4x8_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_4x8_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_4x8_ver_layer0[128] = {static const float av1_tx_type_nn_bias_4x8_ver_layer0[16] = {static const float av1_tx_type_nn_weights_4x8_ver_layer1[64] = {static const float av1_tx_type_nn_bias_4x8_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_4x8_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 8x4 block. static const float av1_tx_type_nn_weights_8x4_hor_layer0[128] = {static const float av1_tx_type_nn_bias_8x4_hor_layer0[16] = {static const float av1_tx_type_nn_weights_8x4_hor_layer1[64] = {static const float av1_tx_type_nn_bias_8x4_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_8x4_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_8x4_ver_layer0[32] = {static const float av1_tx_type_nn_bias_8x4_ver_layer0[8] = {static const float av1_tx_type_nn_weights_8x4_ver_layer1[32] = {static const float av1_tx_type_nn_bias_8x4_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_8x4_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 8x8 block. static const float av1_tx_type_nn_weights_8x8_hor_layer0[128] = {static const float av1_tx_type_nn_bias_8x8_hor_layer0[16] = {static const float av1_tx_type_nn_weights_8x8_hor_layer1[64] = {static const float av1_tx_type_nn_bias_8x8_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_8x8_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_8x8_ver_layer0[128] = {static const float av1_tx_type_nn_bias_8x8_ver_layer0[16] = {static const float av1_tx_type_nn_weights_8x8_ver_layer1[64] = {static const float av1_tx_type_nn_bias_8x8_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_8x8_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 8x16 block. static const float av1_tx_type_nn_weights_8x16_hor_layer0[128] = {static const float av1_tx_type_nn_bias_8x16_hor_layer0[16] = {static const float av1_tx_type_nn_weights_8x16_hor_layer1[64] = {static const float av1_tx_type_nn_bias_8x16_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_8x16_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_8x16_ver_layer0[128] = {static const float av1_tx_type_nn_bias_8x16_ver_layer0[16] = {static const float av1_tx_type_nn_weights_8x16_ver_layer1[64] = {static const float av1_tx_type_nn_bias_8x16_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_8x16_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 16x8 block. static const float av1_tx_type_nn_weights_16x8_hor_layer0[128] = {static const float av1_tx_type_nn_bias_16x8_hor_layer0[16] = {static const float av1_tx_type_nn_weights_16x8_hor_layer1[64] = {static const float av1_tx_type_nn_bias_16x8_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_16x8_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_16x8_ver_layer0[128] = {static const float av1_tx_type_nn_bias_16x8_ver_layer0[16] = {static const float av1_tx_type_nn_weights_16x8_ver_layer1[64] = {static const float av1_tx_type_nn_bias_16x8_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_16x8_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 16x16 block. static const float av1_tx_type_nn_weights_16x16_layer0[128] = {static const float av1_tx_type_nn_bias_16x16_layer0[16] = {static const float av1_tx_type_nn_weights_16x16_layer1[64] = {static const float av1_tx_type_nn_bias_16x16_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_16x16 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 4x16 block. static const float av1_tx_type_nn_weights_4x16_hor_layer0[32] = {static const float av1_tx_type_nn_bias_4x16_hor_layer0[8] = {static const float av1_tx_type_nn_weights_4x16_hor_layer1[32] = {static const float av1_tx_type_nn_bias_4x16_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_4x16_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_4x16_ver_layer0[128] = {static const float av1_tx_type_nn_bias_4x16_ver_layer0[16] = {static const float av1_tx_type_nn_weights_4x16_ver_layer1[64] = {static const float av1_tx_type_nn_bias_4x16_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_4x16_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx type model for 16x4 block. static const float av1_tx_type_nn_weights_16x4_hor_layer0[128] = {static const float av1_tx_type_nn_bias_16x4_hor_layer0[16] = {static const float av1_tx_type_nn_weights_16x4_hor_layer1[64] = {static const float av1_tx_type_nn_bias_16x4_hor_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_16x4_hor = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_tx_type_nn_weights_16x4_ver_layer0[32] = {static const float av1_tx_type_nn_bias_16x4_ver_layer0[8] = {static const float av1_tx_type_nn_weights_16x4_ver_layer1[32] = {static const float av1_tx_type_nn_bias_16x4_ver_layer1[4] = {static const NN_CONFIG av1_tx_type_nnconfig_16x4_ver = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Map tx_size to its corresponding neural net model for tx type prediction. static const NN_CONFIG *const av1_tx_type_nnconfig_map_hor[] = {// 4x4 transform // 8x8 transform // 16x16 transform // 32x32 transform // 64x64 transform // 4x8 transform // 8x4 transform // 8x16 transform // 16x8 transform // 16x32 transform // 32x16 transform // 32x64 transform // 64x32 transform // 4x16 transform // 16x4 transform // 8x32 transform // 32x8 transform // 16x64 transform // 64x16 transform static const NN_CONFIG *const av1_tx_type_nnconfig_map_ver[] = {// 4x4 transform // 8x8 transform // 16x16 transform // 32x32 transform // 64x64 transform // 4x8 transform // 8x4 transform // 8x16 transform // 16x8 transform // 16x32 transform // 32x16 transform // 32x64 transform // 64x32 transform // 4x16 transform // 16x4 transform // 8x32 transform // 32x8 transform // 16x64 transform // 64x16 transform #endif // CONFIG_NN_V2 // Tx split model for 4x8 block. static const float av1_tx_split_nn_weights_4x8_layer0[8 * 16] = {static const float av1_tx_split_nn_bias_4x8_layer0[16] = {static const float av1_tx_split_nn_weights_4x8_layer1[16] = {static const float av1_tx_split_nn_bias_4x8_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_4x8 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 8x8 block. static const float av1_tx_split_nn_weights_8x8_layer0[144] = {static const float av1_tx_split_nn_bias_8x8_layer0[12] = {static const float av1_tx_split_nn_weights_8x8_layer1[12] = {static const float av1_tx_split_nn_bias_8x8_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_8x8 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 8x16 block. static const float av1_tx_split_nn_weights_8x16_layer0[8 * 64] = {static const float av1_tx_split_nn_bias_8x16_layer0[64] = {static const float av1_tx_split_nn_weights_8x16_layer1[64] = {static const float av1_tx_split_nn_bias_8x16_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_8x16 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 16x16 block. static const float av1_tx_split_nn_weights_16x16_layer0[12 * 24] = {static const float av1_tx_split_nn_bias_16x16_layer0[24] = {static const float av1_tx_split_nn_weights_16x16_layer1[24] = {static const float av1_tx_split_nn_bias_16x16_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_16x16 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 32x32 block. static const float av1_tx_split_nn_weights_32x32_layer0[12 * 32] = {static const float av1_tx_split_nn_bias_32x32_layer0[32] = {static const float av1_tx_split_nn_weights_32x32_layer1[32] = {static const float av1_tx_split_nn_bias_32x32_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_32x32 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 64x64 block. static const float av1_tx_split_nn_weights_64x64_layer0[12 * 32] = {static const float av1_tx_split_nn_bias_64x64_layer0[32] = {static const float av1_tx_split_nn_weights_64x64_layer1[32] = {static const float av1_tx_split_nn_bias_64x64_layer1[1] = { -0.2307045f };static const NN_CONFIG av1_tx_split_nnconfig_64x64 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 4x16 block. static const float av1_tx_split_nn_weights_4x16_layer0[8 * 16] = {static const float av1_tx_split_nn_bias_4x16_layer0[16] = {static const float av1_tx_split_nn_weights_4x16_layer1[16] = {static const float av1_tx_split_nn_bias_4x16_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_4x16 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 16x32 block. static const float av1_tx_split_nn_weights_16x32_layer0[8 * 32] = {static const float av1_tx_split_nn_bias_16x32_layer0[32] = {static const float av1_tx_split_nn_weights_16x32_layer1[32] = {static const float av1_tx_split_nn_bias_16x32_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_16x32 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 32x64 block. static const float av1_tx_split_nn_weights_32x64_layer0[8 * 32] = {static const float av1_tx_split_nn_bias_32x64_layer0[32] = {static const float av1_tx_split_nn_weights_32x64_layer1[32] = {static const float av1_tx_split_nn_bias_32x64_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_32x64 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 8x32 block. static const float av1_tx_split_nn_weights_8x32_layer0[8 * 24] = {static const float av1_tx_split_nn_bias_8x32_layer0[24] = {static const float av1_tx_split_nn_weights_8x32_layer1[24] = {static const float av1_tx_split_nn_bias_8x32_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_8x32 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Tx split model for 16x32 block. static const float av1_tx_split_nn_weights_16x64_layer0[8 * 16] = {static const float av1_tx_split_nn_bias_16x64_layer0[16] = {static const float av1_tx_split_nn_weights_16x64_layer1[16] = {static const float av1_tx_split_nn_bias_16x64_layer1[1] = {static const NN_CONFIG av1_tx_split_nnconfig_16x64 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes /******************************************************************************/ // Map block size to its corresponding neural net model for tx split prediction. static const NN_CONFIG *const av1_tx_split_nnconfig_map[TX_SIZES_ALL] = {// TX_4X4, // TX_8X8, // TX_16X16, // TX_32X32, // TX_64X64, // TX_4X8, // TX_8X4, // TX_8X16, // TX_16X8, // TX_16X32, // TX_32X16, // TX_32X64, // TX_64X32, // TX_4X16, // TX_16X4, // TX_8X32, // TX_32X8, // TX_16X64, // TX_64X16, #if !CONFIG_REALTIME_ONLY#define NUM_INTRA_TX_SPLIT_FEATURES 14#define NUM_INTRA_TX_SPLIT_HIDDEN_LAYERS 1#define NUM_INTRA_TX_SPLIT_HIDDEN_NODES 16// Model to prune intra transform depth for intra 8x8 block. static const float av1_intra_tx_split_8x8_mean[NUM_INTRA_TX_SPLIT_FEATURES] = {static const float av1_intra_tx_split_8x8_std[NUM_INTRA_TX_SPLIT_FEATURES] = {static const float av1_intra_tx_split_nn_weights_8x8_layer0static const float static const float av1_intra_tx_split_nn_weights_8x8_layer1static const float av1_intra_tx_split_nn_bias_8x8_layer1[1] = {static const NN_CONFIG av1_intra_tx_split_nnconfig_8x8 = {// num_inputs // num_outputs // num_hidden_layers // num_hidden_nodes static const float av1_intra_tx_prune_nn_thresh_8x8[2] = { -0.405465f,#endif // !CONFIG_REALTIME_ONLY #ifdef __cplusplus// extern "C" #endif #endif // AOM_AV1_ENCODER_TX_PRUNE_MODEL_WEIGHTS_H_
Messung V0.5 in Prozent C=92 H=88 G=89
