diff options
author | Jean-Marc Valin <jmvalin@jmvalin.ca> | 2017-08-04 03:12:57 +0300 |
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committer | Jean-Marc Valin <jmvalin@jmvalin.ca> | 2017-08-04 03:13:54 +0300 |
commit | 0bcf788e8bbca2a6aed2a6cb986125694c70b288 (patch) | |
tree | 6e86344dfa0357fe388da90d7330be6433247bbd /src | |
parent | cf473ce2c7cae6048e9d92be4f774dd50c65e606 (diff) |
RNN C code
Diffstat (limited to 'src')
-rw-r--r-- | src/rnn.c | 208 | ||||
-rw-r--r-- | src/rnn.h | 61 | ||||
-rw-r--r-- | src/tansig_table.h | 45 |
3 files changed, 314 insertions, 0 deletions
diff --git a/src/rnn.c b/src/rnn.c new file mode 100644 index 0000000..6fb8286 --- /dev/null +++ b/src/rnn.c @@ -0,0 +1,208 @@ +/* Copyright (c) 2008-2011 Octasic Inc. + 2012-2017 Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifdef HAVE_CONFIG_H +#include "config.h" +#endif + +#include <math.h> +#include "opus_types.h" +#include "common.h" +#include "arch.h" +#include "tansig_table.h" +#include "rnn.h" +#include <stdio.h> + +static OPUS_INLINE float tansig_approx(float x) +{ + int i; + float y, dy; + float sign=1; + /* Tests are reversed to catch NaNs */ + if (!(x<8)) + return 1; + if (!(x>-8)) + return -1; +#ifndef FIXED_POINT + /* Another check in case of -ffast-math */ + if (celt_isnan(x)) + return 0; +#endif + if (x<0) + { + x=-x; + sign=-1; + } + i = (int)floor(.5f+25*x); + x -= .04f*i; + y = tansig_table[i]; + dy = 1-y*y; + y = y + x*dy*(1 - y*x); + return sign*y; +} + +static OPUS_INLINE float sigmoid_approx(float x) +{ + return .5 + .5*tansig_approx(.5*x); +} + +static OPUS_INLINE float relu(float x) +{ + return x < 0 ? 0 : x; +} + +void compute_dense(const DenseLayer *layer, float *output, const float *input) +{ + int i, j; + int N, M; + int stride; + M = layer->nb_inputs; + N = layer->nb_neurons; + stride = N; + for (i=0;i<N;i++) + { + /* Compute update gate. */ + float sum = layer->bias[i]; + for (j=0;j<M;j++) + sum += layer->input_weights[j*stride + i]*input[j]; + output[i] = WEIGHTS_SCALE*sum; + } + if (layer->activation == activation_sigmoid) { + for (i=0;i<N;i++) + output[i] = sigmoid_approx(output[i]); + } else if (layer->activation == activation_tanh) { + for (i=0;i<N;i++) + output[i] = tansig_approx(output[i]); + } else if (layer->activation == activation_relu) { + for (i=0;i<N;i++) + output[i] = relu(output[i]); + } else { + *(int*)0=0; + } +} + +void compute_gru(const GRULayer *gru, float *state, const float *input) +{ + int i, j; + int N, M; + int stride; + float z[MAX_NEURONS]; + float r[MAX_NEURONS]; + float h[MAX_NEURONS]; + M = gru->nb_inputs; + N = gru->nb_neurons; + stride = 3*N; + for (i=0;i<N;i++) + { + /* Compute update gate. */ + float sum = gru->bias[i]; + for (j=0;j<M;j++) + sum += gru->input_weights[j*stride + i]*input[j]; + for (j=0;j<N;j++) + sum += gru->recurrent_weights[j*stride + i]*state[j]; + z[i] = sigmoid_approx(WEIGHTS_SCALE*sum); + } + for (i=0;i<N;i++) + { + /* Compute reset gate. */ + float sum = gru->bias[N + i]; + for (j=0;j<M;j++) + sum += gru->input_weights[N + j*stride + i]*input[j]; + for (j=0;j<N;j++) + sum += gru->recurrent_weights[N + j*stride + i]*state[j]; + r[i] = sigmoid_approx(WEIGHTS_SCALE*sum); + } + for (i=0;i<N;i++) + { + /* Compute output. */ + float sum = gru->bias[2*N + i]; + for (j=0;j<M;j++) + sum += gru->input_weights[2*N + j*stride + i]*input[j]; + for (j=0;j<N;j++) + sum += gru->recurrent_weights[2*N + j*stride + i]*state[j]*r[j]; + if (gru->activation == activation_sigmoid) sum = sigmoid_approx(WEIGHTS_SCALE*sum); + else if (gru->activation == activation_tanh) sum = tansig_approx(WEIGHTS_SCALE*sum); + else if (gru->activation == activation_relu) sum = relu(WEIGHTS_SCALE*sum); + else *(int*)0=0; + h[i] = z[i]*state[i] + (1-z[i])*sum; + } + for (i=0;i<N;i++) + state[i] = h[i]; +} + +#if 1 +#define INPUT_SIZE 42 +#define DENSE_SIZE 12 +#define VAD_SIZE 12 +#define NOISE_SIZE 48 +#define DENOISE_SIZE 128 + +extern const DenseLayer input_dense; +extern const GRULayer vad_gru; +extern const GRULayer noise_gru; +extern const GRULayer denoise_gru; +extern const DenseLayer denoise_output; +extern const DenseLayer vad_output; + +int main() { + float vad_state[MAX_NEURONS] = {0}; + float vad_out[MAX_NEURONS] = {0}; + float input[INPUT_SIZE]; + float dense_out[MAX_NEURONS]; + float noise_input[MAX_NEURONS*3]; + float denoise_input[MAX_NEURONS*3]; + float noise_state[MAX_NEURONS] = {0}; + float denoise_state[MAX_NEURONS] = {0}; + float gains[22]; + while (1) + { + int i; + for (i=0;i<INPUT_SIZE;i++) scanf("%f", &input[i]); + for (i=0;i<45;i++) scanf("%f", &vad_out[0]); + if (feof(stdin)) break; + compute_dense(&input_dense, dense_out, input); + compute_gru(&vad_gru, vad_state, dense_out); + compute_dense(&vad_output, vad_out, vad_state); +#if 1 + for (i=0;i<DENSE_SIZE;i++) noise_input[i] = dense_out[i]; + for (i=0;i<VAD_SIZE;i++) noise_input[i+DENSE_SIZE] = vad_state[i]; + for (i=0;i<INPUT_SIZE;i++) noise_input[i+DENSE_SIZE+VAD_SIZE] = input[i]; + compute_gru(&noise_gru, noise_state, noise_input); + + for (i=0;i<VAD_SIZE;i++) denoise_input[i] = vad_state[i]; + for (i=0;i<NOISE_SIZE;i++) denoise_input[i+VAD_SIZE] = noise_state[i]; + for (i=0;i<INPUT_SIZE;i++) denoise_input[i+VAD_SIZE+NOISE_SIZE] = input[i]; + compute_gru(&denoise_gru, denoise_state, denoise_input); + + compute_dense(&denoise_output, gains, denoise_state); + + for (i=0;i<22;i++) printf("%f ", gains[i]); +#endif + printf("%f\n", vad_out[0]); + } +} +#endif diff --git a/src/rnn.h b/src/rnn.h new file mode 100644 index 0000000..0736d5d --- /dev/null +++ b/src/rnn.h @@ -0,0 +1,61 @@ +/* Copyright (c) 2017 Jean-Marc Valin */ +/* + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions + are met: + + - Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + - Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR + CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, + EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, + PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR + PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF + LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING + NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS + SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +*/ + +#ifndef RNN_H_ +#define RNN_H_ + +#include "opus_types.h" + +#define WEIGHTS_SCALE (1.f/8192) + +#define MAX_NEURONS 128 + +#define activation_tanh 0 +#define activation_sigmoid 1 +#define activation_relu 2 + +typedef struct { + const opus_int16 *bias; + const opus_int16 *input_weights; + int nb_inputs; + int nb_neurons; + int activation; +} DenseLayer; + +typedef struct { + const opus_int16 *bias; + const opus_int16 *input_weights; + const opus_int16 *recurrent_weights; + int nb_inputs; + int nb_neurons; + int activation; +} GRULayer; + +void compute_dense(const DenseLayer *layer, float *output, const float *input); + +void compute_gru(const GRULayer *gru, float *state, const float *input); + +#endif /* _MLP_H_ */ diff --git a/src/tansig_table.h b/src/tansig_table.h new file mode 100644 index 0000000..c76f844 --- /dev/null +++ b/src/tansig_table.h @@ -0,0 +1,45 @@ +/* This file is auto-generated by gen_tables */ + +static const float tansig_table[201] = { +0.000000f, 0.039979f, 0.079830f, 0.119427f, 0.158649f, +0.197375f, 0.235496f, 0.272905f, 0.309507f, 0.345214f, +0.379949f, 0.413644f, 0.446244f, 0.477700f, 0.507977f, +0.537050f, 0.564900f, 0.591519f, 0.616909f, 0.641077f, +0.664037f, 0.685809f, 0.706419f, 0.725897f, 0.744277f, +0.761594f, 0.777888f, 0.793199f, 0.807569f, 0.821040f, +0.833655f, 0.845456f, 0.856485f, 0.866784f, 0.876393f, +0.885352f, 0.893698f, 0.901468f, 0.908698f, 0.915420f, +0.921669f, 0.927473f, 0.932862f, 0.937863f, 0.942503f, +0.946806f, 0.950795f, 0.954492f, 0.957917f, 0.961090f, +0.964028f, 0.966747f, 0.969265f, 0.971594f, 0.973749f, +0.975743f, 0.977587f, 0.979293f, 0.980869f, 0.982327f, +0.983675f, 0.984921f, 0.986072f, 0.987136f, 0.988119f, +0.989027f, 0.989867f, 0.990642f, 0.991359f, 0.992020f, +0.992631f, 0.993196f, 0.993718f, 0.994199f, 0.994644f, +0.995055f, 0.995434f, 0.995784f, 0.996108f, 0.996407f, +0.996682f, 0.996937f, 0.997172f, 0.997389f, 0.997590f, +0.997775f, 0.997946f, 0.998104f, 0.998249f, 0.998384f, +0.998508f, 0.998623f, 0.998728f, 0.998826f, 0.998916f, +0.999000f, 0.999076f, 0.999147f, 0.999213f, 0.999273f, +0.999329f, 0.999381f, 0.999428f, 0.999472f, 0.999513f, +0.999550f, 0.999585f, 0.999617f, 0.999646f, 0.999673f, +0.999699f, 0.999722f, 0.999743f, 0.999763f, 0.999781f, +0.999798f, 0.999813f, 0.999828f, 0.999841f, 0.999853f, +0.999865f, 0.999875f, 0.999885f, 0.999893f, 0.999902f, +0.999909f, 0.999916f, 0.999923f, 0.999929f, 0.999934f, +0.999939f, 0.999944f, 0.999948f, 0.999952f, 0.999956f, +0.999959f, 0.999962f, 0.999965f, 0.999968f, 0.999970f, +0.999973f, 0.999975f, 0.999977f, 0.999978f, 0.999980f, +0.999982f, 0.999983f, 0.999984f, 0.999986f, 0.999987f, +0.999988f, 0.999989f, 0.999990f, 0.999990f, 0.999991f, +0.999992f, 0.999992f, 0.999993f, 0.999994f, 0.999994f, +0.999994f, 0.999995f, 0.999995f, 0.999996f, 0.999996f, +0.999996f, 0.999997f, 0.999997f, 0.999997f, 0.999997f, +0.999997f, 0.999998f, 0.999998f, 0.999998f, 0.999998f, +0.999998f, 0.999998f, 0.999999f, 0.999999f, 0.999999f, +0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f, +0.999999f, 0.999999f, 0.999999f, 0.999999f, 0.999999f, +1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f, +1.000000f, 1.000000f, 1.000000f, 1.000000f, 1.000000f, +1.000000f, +}; |