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+/* 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 "rnn_data.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];
+}
+
+#define INPUT_SIZE 42
+
+void compute_rnn(RNNState *rnn, float *gains, float *vad, const float *input) {
+ int i;
+ float dense_out[MAX_NEURONS];
+ float noise_input[MAX_NEURONS*3];
+ float denoise_input[MAX_NEURONS*3];
+ compute_dense(rnn->model->input_dense, dense_out, input);
+ compute_gru(rnn->model->vad_gru, rnn->vad_gru_state, dense_out);
+ compute_dense(rnn->model->vad_output, vad, rnn->vad_gru_state);
+ for (i=0;i<rnn->model->input_dense_size;i++) noise_input[i] = dense_out[i];
+ for (i=0;i<rnn->model->vad_gru_size;i++) noise_input[i+rnn->model->input_dense_size] = rnn->vad_gru_state[i];
+ for (i=0;i<INPUT_SIZE;i++) noise_input[i+rnn->model->input_dense_size+rnn->model->vad_gru_size] = input[i];
+ compute_gru(rnn->model->noise_gru, rnn->noise_gru_state, noise_input);
+
+ for (i=0;i<rnn->model->vad_gru_size;i++) denoise_input[i] = rnn->vad_gru_state[i];
+ for (i=0;i<rnn->model->noise_gru_size;i++) denoise_input[i+rnn->model->vad_gru_size] = rnn->noise_gru_state[i];
+ for (i=0;i<INPUT_SIZE;i++) denoise_input[i+rnn->model->vad_gru_size+rnn->model->noise_gru_size] = input[i];
+ compute_gru(rnn->model->denoise_gru, rnn->denoise_gru_state, denoise_input);
+ compute_dense(rnn->model->denoise_output, gains, rnn->denoise_gru_state);
+}
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-24 00:23:32 +0000