diff options
Diffstat (limited to 'media/libopus/src/mlp.c')
| -rw-r--r-- | media/libopus/src/mlp.c | 144 |
1 files changed, 144 insertions, 0 deletions
diff --git a/media/libopus/src/mlp.c b/media/libopus/src/mlp.c new file mode 100644 index 0000000000..964c6a98f6 --- /dev/null +++ b/media/libopus/src/mlp.c @@ -0,0 +1,144 @@ +/* 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 "opus_defines.h" +#include "arch.h" +#include "tansig_table.h" +#include "mlp.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 .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) +{ + 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]); + } +} + +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]; +} + |