diff options
Diffstat (limited to 'thirdparty/opus/mlp.c')
| -rw-r--r-- | thirdparty/opus/mlp.c | 155 |
1 files changed, 78 insertions, 77 deletions
diff --git a/thirdparty/opus/mlp.c b/thirdparty/opus/mlp.c index 964c6a98f6..ff9e50df47 100644 --- a/thirdparty/opus/mlp.c +++ b/thirdparty/opus/mlp.c @@ -1,5 +1,5 @@ /* Copyright (c) 2008-2011 Octasic Inc. - 2012-2017 Jean-Marc Valin */ + Written by Jean-Marc Valin */ /* Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions @@ -29,13 +29,42 @@ #include "config.h" #endif -#include <math.h> #include "opus_types.h" #include "opus_defines.h" + +#include <math.h> +#include "mlp.h" #include "arch.h" #include "tansig_table.h" -#include "mlp.h" +#define MAX_NEURONS 100 +#if 0 +static OPUS_INLINE opus_val16 tansig_approx(opus_val32 _x) /* Q19 */ +{ + int i; + opus_val16 xx; /* Q11 */ + /*double x, y;*/ + opus_val16 dy, yy; /* Q14 */ + /*x = 1.9073e-06*_x;*/ + if (_x>=QCONST32(8,19)) + return QCONST32(1.,14); + if (_x<=-QCONST32(8,19)) + return -QCONST32(1.,14); + xx = EXTRACT16(SHR32(_x, 8)); + /*i = lrint(25*x);*/ + i = SHR32(ADD32(1024,MULT16_16(25, xx)),11); + /*x -= .04*i;*/ + xx -= EXTRACT16(SHR32(MULT16_16(20972,i),8)); + /*x = xx*(1./2048);*/ + /*y = tansig_table[250+i];*/ + yy = tansig_table[250+i]; + /*y = yy*(1./16384);*/ + dy = 16384-MULT16_16_Q14(yy,yy); + yy = yy + MULT16_16_Q14(MULT16_16_Q11(xx,dy),(16384 - MULT16_16_Q11(yy,xx))); + return yy; +} +#else +/*extern const float tansig_table[501];*/ static OPUS_INLINE float tansig_approx(float x) { int i; @@ -63,82 +92,54 @@ static OPUS_INLINE float tansig_approx(float x) y = y + x*dy*(1 - y*x); return sign*y; } +#endif -static OPUS_INLINE float sigmoid_approx(float x) -{ - return .5f + .5f*tansig_approx(.5f*x); -} - -static void gemm_accum(float *out, const opus_int8 *weights, int rows, int cols, int col_stride, const float *x) -{ - int i, j; - for (i=0;i<rows;i++) - { - for (j=0;j<cols;j++) - out[i] += weights[j*col_stride + i]*x[j]; - } -} - -void compute_dense(const DenseLayer *layer, float *output, const float *input) +#if 0 +void mlp_process(const MLP *m, const opus_val16 *in, opus_val16 *out) { - int i; - int N, M; - int stride; - M = layer->nb_inputs; - N = layer->nb_neurons; - stride = N; - for (i=0;i<N;i++) - output[i] = layer->bias[i]; - gemm_accum(output, layer->input_weights, N, M, stride, input); - for (i=0;i<N;i++) - output[i] *= WEIGHTS_SCALE; - if (layer->sigmoid) { - for (i=0;i<N;i++) - output[i] = sigmoid_approx(output[i]); - } else { - for (i=0;i<N;i++) - output[i] = tansig_approx(output[i]); - } + int j; + opus_val16 hidden[MAX_NEURONS]; + const opus_val16 *W = m->weights; + /* Copy to tmp_in */ + for (j=0;j<m->topo[1];j++) + { + int k; + opus_val32 sum = SHL32(EXTEND32(*W++),8); + for (k=0;k<m->topo[0];k++) + sum = MAC16_16(sum, in[k],*W++); + hidden[j] = tansig_approx(sum); + } + for (j=0;j<m->topo[2];j++) + { + int k; + opus_val32 sum = SHL32(EXTEND32(*W++),14); + for (k=0;k<m->topo[1];k++) + sum = MAC16_16(sum, hidden[k], *W++); + out[j] = tansig_approx(EXTRACT16(PSHR32(sum,17))); + } } - -void compute_gru(const GRULayer *gru, float *state, const float *input) +#else +void mlp_process(const MLP *m, const float *in, float *out) { - int i; - int N, M; - int stride; - float tmp[MAX_NEURONS]; - float z[MAX_NEURONS]; - float r[MAX_NEURONS]; - float h[MAX_NEURONS]; - M = gru->nb_inputs; - N = gru->nb_neurons; - stride = 3*N; - /* Compute update gate. */ - for (i=0;i<N;i++) - z[i] = gru->bias[i]; - gemm_accum(z, gru->input_weights, N, M, stride, input); - gemm_accum(z, gru->recurrent_weights, N, N, stride, state); - for (i=0;i<N;i++) - z[i] = sigmoid_approx(WEIGHTS_SCALE*z[i]); - - /* Compute reset gate. */ - for (i=0;i<N;i++) - r[i] = gru->bias[N + i]; - gemm_accum(r, &gru->input_weights[N], N, M, stride, input); - gemm_accum(r, &gru->recurrent_weights[N], N, N, stride, state); - for (i=0;i<N;i++) - r[i] = sigmoid_approx(WEIGHTS_SCALE*r[i]); - - /* Compute output. */ - for (i=0;i<N;i++) - h[i] = gru->bias[2*N + i]; - for (i=0;i<N;i++) - tmp[i] = state[i] * r[i]; - gemm_accum(h, &gru->input_weights[2*N], N, M, stride, input); - gemm_accum(h, &gru->recurrent_weights[2*N], N, N, stride, tmp); - for (i=0;i<N;i++) - h[i] = z[i]*state[i] + (1-z[i])*tansig_approx(WEIGHTS_SCALE*h[i]); - for (i=0;i<N;i++) - state[i] = h[i]; + int j; + float hidden[MAX_NEURONS]; + const float *W = m->weights; + /* Copy to tmp_in */ + for (j=0;j<m->topo[1];j++) + { + int k; + float sum = *W++; + for (k=0;k<m->topo[0];k++) + sum = sum + in[k]**W++; + hidden[j] = tansig_approx(sum); + } + for (j=0;j<m->topo[2];j++) + { + int k; + float sum = *W++; + for (k=0;k<m->topo[1];k++) + sum = sum + hidden[k]**W++; + out[j] = tansig_approx(sum); + } } - +#endif |