85 lines
3.1 KiB
C
85 lines
3.1 KiB
C
/*
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* Copyright (c) 2016, Alliance for Open Media. All rights reserved.
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*
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* This source code is subject to the terms of the BSD 2 Clause License and
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* the Alliance for Open Media Patent License 1.0. If the BSD 2 Clause License
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* was not distributed with this source code in the LICENSE file, you can
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* obtain it at www.aomedia.org/license/software. If the Alliance for Open
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* Media Patent License 1.0 was not distributed with this source code in the
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* PATENTS file, you can obtain it at www.aomedia.org/license/patent.
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*/
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#ifndef AOM_AV1_ENCODER_ML_H_
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#define AOM_AV1_ENCODER_ML_H_
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#ifdef __cplusplus
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extern "C" {
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#endif
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#include "config/av1_rtcd.h"
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#define NN_MAX_HIDDEN_LAYERS 10
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#define NN_MAX_NODES_PER_LAYER 128
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struct NN_CONFIG {
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int num_inputs; // Number of input nodes, i.e. features.
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int num_outputs; // Number of output nodes.
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int num_hidden_layers; // Number of hidden layers, maximum 10.
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// Number of nodes for each hidden layer.
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int num_hidden_nodes[NN_MAX_HIDDEN_LAYERS];
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// Weight parameters, indexed by layer.
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const float *weights[NN_MAX_HIDDEN_LAYERS + 1];
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// Bias parameters, indexed by layer.
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const float *bias[NN_MAX_HIDDEN_LAYERS + 1];
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};
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// Typedef from struct NN_CONFIG to NN_CONFIG is in rtcd_defs
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#if CONFIG_NN_V2
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// Fully-connectedly layer configuration
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struct FC_LAYER {
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const int num_inputs; // Number of input nodes, i.e. features.
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const int num_outputs; // Number of output nodes.
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float *weights; // Weight parameters.
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float *bias; // Bias parameters.
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const ACTIVATION activation; // Activation function.
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float *output; // The output array.
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float *dY; // Gradient of outputs
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float *dW; // Gradient of weights.
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float *db; // Gradient of bias
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};
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// NN configure structure V2
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struct NN_CONFIG_V2 {
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const int num_hidden_layers; // Number of hidden layers, max = 10.
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FC_LAYER layer[NN_MAX_HIDDEN_LAYERS + 1]; // The layer array
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const int num_logits; // Number of output nodes.
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float *logits; // Raw prediction (same as output of final layer)
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const LOSS loss; // Loss function
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};
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// Calculate prediction based on the given input features and neural net config.
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// Assume there are no more than NN_MAX_NODES_PER_LAYER nodes in each hidden
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// layer.
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void av1_nn_predict_v2(const float *features, NN_CONFIG_V2 *nn_config,
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int reduce_prec, float *output);
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#endif // CONFIG_NN_V2
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// Applies the softmax normalization function to the input
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// to get a valid probability distribution in the output:
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// output[i] = exp(input[i]) / sum_{k \in [0,n)}(exp(input[k]))
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void av1_nn_softmax(const float *input, float *output, int n);
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// A faster but less accurate version of av1_nn_softmax(input, output, 16)
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void av1_nn_fast_softmax_16_c(const float *input, float *output);
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// Applies a precision reduction to output of av1_nn_predict to prevent
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// mismatches between C and SIMD implementations.
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void av1_nn_output_prec_reduce(float *const output, int num_output);
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#ifdef __cplusplus
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} // extern "C"
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#endif
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#endif // AOM_AV1_ENCODER_ML_H_
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