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/*
* 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.
*/
#ifndef AOM_AV1_ENCODER_ML_H_
#define AOM_AV1_ENCODER_ML_H_
#ifdef __cplusplus
extern "C" {
#endif
#include "config/av1_rtcd.h"
#define NN_MAX_HIDDEN_LAYERS 10
#define NN_MAX_NODES_PER_LAYER 128
struct NN_CONFIG {
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];
};
// Typedef from struct NN_CONFIG to NN_CONFIG is in rtcd_defs
#if CONFIG_NN_V2
// Fully-connectedly layer configuration
struct FC_LAYER {
const int num_inputs; // Number of input nodes, i.e. features.
const int num_outputs; // Number of output nodes.
float *weights; // Weight parameters.
float *bias; // Bias parameters.
const ACTIVATION activation; // Activation function.
float *output; // The output array.
float *dY; // Gradient of outputs
float *dW; // Gradient of weights.
float *db; // Gradient of bias
};
// NN configure structure V2
struct NN_CONFIG_V2 {
const int num_hidden_layers; // Number of hidden layers, max = 10.
FC_LAYER layer[NN_MAX_HIDDEN_LAYERS + 1]; // The layer array
const int num_logits; // Number of output nodes.
float *logits; // Raw prediction (same as output of final layer)
const LOSS loss; // Loss function
};
// 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.
void av1_nn_predict_v2(const float *features, NN_CONFIG_V2 *nn_config,
int reduce_prec, float *output);
#endif // CONFIG_NN_V2
// Applies the softmax normalization function to the input
// to get a valid probability distribution in the output:
// output[i] = exp(input[i]) / sum_{k \in [0,n)}(exp(input[k]))
void av1_nn_softmax(const float *input, float *output, int n);
// A faster but less accurate version of av1_nn_softmax(input, output, 16)
void av1_nn_fast_softmax_16_c(const float *input, float *output);
// Applies a precision reduction to output of av1_nn_predict to prevent
// mismatches between C and SIMD implementations.
void av1_nn_output_prec_reduce(float *const output, int num_output);
#ifdef __cplusplus
} // extern "C"
#endif
#endif // AOM_AV1_ENCODER_ML_H_
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