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-rw-r--r--media/libopus/src/mlp.c155
1 files changed, 77 insertions, 78 deletions
diff --git a/media/libopus/src/mlp.c b/media/libopus/src/mlp.c
index ff9e50df47..964c6a98f6 100644
--- a/media/libopus/src/mlp.c
+++ b/media/libopus/src/mlp.c
@@ -1,5 +1,5 @@
/* Copyright (c) 2008-2011 Octasic Inc.
- Written by Jean-Marc Valin */
+ 2012-2017 Jean-Marc Valin */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
@@ -29,42 +29,13 @@
#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"
-#define MAX_NEURONS 100
+#include "mlp.h"
-#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;
@@ -92,54 +63,82 @@ static OPUS_INLINE float tansig_approx(float x)
y = y + x*dy*(1 - y*x);
return sign*y;
}
-#endif
-#if 0
-void mlp_process(const MLP *m, const opus_val16 *in, opus_val16 *out)
+static OPUS_INLINE float sigmoid_approx(float x)
{
- 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)));
- }
+ 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];
+ }
}
-#else
-void mlp_process(const MLP *m, const float *in, float *out)
+
+void compute_dense(const DenseLayer *layer, float *output, const float *input)
{
- 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);
- }
+ 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]);
+ }
}
-#endif
+
+void compute_gru(const GRULayer *gru, float *state, const float *input)
+{
+ 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];
+}
+