mirror of
https://github.com/xiph/opus.git
synced 2025-05-18 09:28:30 +00:00
Work in progress translation to C
This commit is contained in:
Jean-Marc Valin
parent
8caaa5e917
commit
b9cd61be8b
4 changed files with 482 additions and 0 deletions
130
dnn/dump_lpcnet.py
Executable file
130
dnn/dump_lpcnet.py
Executable file
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#!/usr/bin/python3
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import lpcnet
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import sys
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import numpy as np
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from keras.optimizers import Adam
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from keras.callbacks import ModelCheckpoint
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from keras.layers import Layer, GRU, CuDNNGRU, Dense, Conv1D, Embedding
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from ulaw import ulaw2lin, lin2ulaw
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from mdense import MDense
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import keras.backend as K
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import h5py
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import re
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def printVector(f, vector, name):
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v = np.reshape(vector, (-1));
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#print('static const float ', name, '[', len(v), '] = \n', file=f)
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f.write('static const float {}[{}] = {{\n '.format(name, len(v)))
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for i in range(0, len(v)):
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f.write('{}'.format(v[i]))
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if (i!=len(v)-1):
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f.write(',')
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else:
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break;
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if (i%8==7):
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f.write("\n ")
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else:
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f.write(" ")
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#print(v, file=f)
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f.write('\n};\n\n')
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return;
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def dump_layer_ignore(self, f, hf):
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print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
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False
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Layer.dump_layer = dump_layer_ignore
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def dump_gru_layer(self, f, hf):
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name = self.name
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print("printing layer " + name + " of type " + self.__class__.__name__)
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weights = self.get_weights()
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printVector(f, weights[0], name + '_weights')
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printVector(f, weights[1], name + '_recurrent_weights')
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printVector(f, weights[-1], name + '_bias')
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#activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
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if hasattr(self, 'activation'):
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activation = self.activation.__name__.upper()
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else:
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activation = 'TANH'
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if hasattr(self, 'reset_after'):
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reset_after = self.reset_after
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else:
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reset_after = 1
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f.write('const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
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.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
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hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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hf.write('extern const GRULayer {};\n\n'.format(name));
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True
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CuDNNGRU.dump_layer = dump_gru_layer
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GRU.dump_layer = dump_gru_layer
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def dump_dense_layer(self, f, hf):
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name = self.name
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print("printing layer " + name + " of type " + self.__class__.__name__)
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weights = self.get_weights()
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printVector(f, weights[0], name + '_weights')
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printVector(f, weights[-1], name + '_bias')
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#activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
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if hasattr(self, 'activation'):
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activation = self.activation.__name__.upper()
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else:
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activation = 'TANH'
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f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
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.format(name, name, name, weights[0].shape[0], weights[0].shape[1], activation))
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hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
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hf.write('extern const DenseLayer {};\n\n'.format(name));
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False
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Dense.dump_layer = dump_dense_layer
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def dump_mdense_layer(self, f, hf):
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name = self.name
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print("printing layer " + name + " of type " + self.__class__.__name__)
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weights = self.get_weights()
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printVector(f, weights[0], name + '_weights')
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printVector(f, weights[1], name + '_bias')
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printVector(f, weights[1], name + '_factor')
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#activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
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if hasattr(self, 'activation'):
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activation = self.activation.__name__.upper()
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else:
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activation = 'TANH'
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f.write('const MDenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}_factor,\n {}, {}, ACTIVATION_{}\n}};\n\n'
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.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1], activation))
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hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))
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hf.write('extern const MDenseLayer {};\n\n'.format(name));
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False
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MDense.dump_layer = dump_mdense_layer
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model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=640)
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
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#model.summary()
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model.load_weights(sys.argv[1])
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f = open(sys.argv[2], 'w')
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hf = open(sys.argv[3], 'w')
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f.write('/*This file is automatically generated from a Keras model*/\n\n')
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f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "foo.h"\n\n')
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hf.write('/*This file is automatically generated from a Keras model*/\n\n')
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hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "{}"\n\n'.format(sys.argv[3]))
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layer_list = []
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for i, layer in enumerate(model.layers):
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if layer.dump_layer(f, hf):
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layer_list.append(layer.name)
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hf.write('struct RNNState {\n')
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for i, name in enumerate(layer_list):
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hf.write(' float {}_state[{}_SIZE];\n'.format(name, name.upper()))
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hf.write('};\n')
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hf.write('\n\n#endif\n')
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f.close()
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hf.close()
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213
dnn/nnet.c
Normal file
213
dnn/nnet.c
Normal file
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/* Copyright (c) 2018 Mozilla
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2008-2011 Octasic Inc.
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2012-2017 Jean-Marc Valin */
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/*
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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- Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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- Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR
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CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
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EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
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PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
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PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
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LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
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NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
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SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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*/
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#ifdef HAVE_CONFIG_H
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#include "config.h"
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#endif
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#include <math.h>
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#include "opus_types.h"
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#include "arch.h"
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#include "common.h"
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#include "tansig_table.h"
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#include "nnet.h"
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static OPUS_INLINE float tansig_approx(float x)
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{
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int i;
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float y, dy;
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float sign=1;
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/* Tests are reversed to catch NaNs */
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if (!(x<8))
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return 1;
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if (!(x>-8))
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return -1;
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#ifndef FIXED_POINT
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/* Another check in case of -ffast-math */
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if (celt_isnan(x))
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return 0;
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#endif
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if (x<0)
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{
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x=-x;
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sign=-1;
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}
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i = (int)floor(.5f+25*x);
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x -= .04f*i;
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y = tansig_table[i];
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dy = 1-y*y;
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y = y + x*dy*(1 - y*x);
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return sign*y;
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}
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static OPUS_INLINE float sigmoid_approx(float x)
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{
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return .5f + .5f*tansig_approx(.5f*x);
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}
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static OPUS_INLINE float relu(float x)
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{
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return x < 0 ? 0 : x;
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}
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static void gemm_accum(float *out, const float *weights, int rows, int cols, int col_stride, const float *x)
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{
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int i, j;
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for (i=0;i<rows;i++)
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{
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for (j=0;j<cols;j++)
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out[i] += weights[j*col_stride + i]*x[j];
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}
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}
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void compute_activation(float *output, float *input, int N, int activation)
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{
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int i;
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if (activation == ACTIVATION_SIGMOID) {
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for (i=0;i<N;i++)
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output[i] = sigmoid_approx(input[i]);
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} else if (activation == ACTIVATION_TANH) {
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for (i=0;i<N;i++)
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output[i] = tansig_approx(input[i]);
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} else if (activation == ACTIVATION_RELU) {
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for (i=0;i<N;i++)
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output[i] = relu(input[i]);
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} else if (activation == ACTIVATION_SOFTMAX) {
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float sum = 0;
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for (i=0;i<N;i++) {
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output[i] = exp(input[i]);
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sum += output[i];
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}
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sum = 1.f/(sum+1e-30);
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for (i=0;i<N;i++)
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output[i] = sum*output[i];
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} else {
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celt_assert(layer->activation == ACTIVATION_LINEAR);
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for (i=0;i<N;i++)
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output[i] = input[i];
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}
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}
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void compute_dense(const DenseLayer *layer, float *output, const float *input)
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{
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int i;
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int N, M;
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int stride;
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celt_assert(layer->nb_neurons <= MAX_NEURONS);
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M = layer->nb_inputs;
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N = layer->nb_neurons;
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stride = N;
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for (i=0;i<N;i++)
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output[i] = layer->bias[i];
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gemm_accum(output, layer->input_weights, N, M, stride, input);
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compute_activation(output, output, N, layer->activation);
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}
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void compute_gru(const GRULayer *gru, float *state, const float *input)
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{
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int i;
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int N, M;
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int stride;
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float tmp[MAX_NEURONS];
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float z[MAX_NEURONS];
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float r[MAX_NEURONS];
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float h[MAX_NEURONS];
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celt_assert(gru->nb_neurons <= MAX_NEURONS);
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M = gru->nb_inputs;
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N = gru->nb_neurons;
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stride = 3*N;
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/* Compute update gate. */
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for (i=0;i<N;i++)
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z[i] = gru->bias[i];
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gemm_accum(z, gru->input_weights, N, M, stride, input);
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gemm_accum(z, gru->recurrent_weights, N, N, stride, state);
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for (i=0;i<N;i++)
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z[i] = sigmoid_approx(z[i]);
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/* Compute reset gate. */
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for (i=0;i<N;i++)
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r[i] = gru->bias[N + i];
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gemm_accum(r, &gru->input_weights[N], N, M, stride, input);
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gemm_accum(r, &gru->recurrent_weights[N], N, N, stride, state);
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for (i=0;i<N;i++)
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r[i] = sigmoid_approx(r[i]);
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/* Compute output. */
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for (i=0;i<N;i++)
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h[i] = gru->bias[2*N + i];
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if (gru->reset_after)
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{
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/* WARNING: The reset_after version was never tested. */
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RNN_CLEAR(tmp, N);
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gemm_accum(tmp, &gru->recurrent_weights[2*N], N, N, stride, state);
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for (i=0;i<N;i++)
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h[i] += tmp[i] * r[i];
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gemm_accum(h, &gru->input_weights[2*N], N, M, stride, input);
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} else {
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for (i=0;i<N;i++)
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tmp[i] = state[i] * r[i];
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gemm_accum(h, &gru->input_weights[2*N], N, M, stride, input);
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gemm_accum(h, &gru->recurrent_weights[2*N], N, N, stride, tmp);
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}
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for (i=0;i<N;i++)
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h[i] = z[i]*state[i] + (1-z[i])*tansig_approx(h[i]);
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for (i=0;i<N;i++)
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state[i] = h[i];
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}
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void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const float *input)
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{
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int i;
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int N, M;
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int stride;
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float tmp[MAX_CONV_INPUTS];
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celt_assert(layer->nb_neurons <= MAX_NEURONS);
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celt_assert(layer->nb_inputs*layer->kernel_size <= MAX_CONV_INPUTS);
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M = layer->nb_inputs;
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N = layer->nb_neurons;
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RNN_COPY(tmp, mem, layer->nb_inputs*(layer->kernel_size-1));
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RNN_COPY(tmp, input, layer->nb_inputs);
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M = layer->nb_inputs;
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N = layer->nb_neurons;
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stride = N;
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for (i=0;i<N;i++)
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output[i] = layer->bias[i];
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gemm_accum(output, layer->input_weights, N, M, stride, input);
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compute_activation(output, output, N, layer->activation);
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RNN_COPY(mem, &tmp[layer->nb_inputs], layer->nb_inputs*(layer->kernel_size-1));
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}
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void compute_embedding(const EmbeddingLayer *layer, float *output, int input)
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{
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int i;
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for (i=0;i<layer->dim;i++)
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{
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output[i] = layer->embedding_weights[input*layer->dim + i];
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}
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}
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94
dnn/nnet.h
Normal file
94
dnn/nnet.h
Normal file
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|
@ -0,0 +1,94 @@
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/* Copyright (c) 2018 Mozilla
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Copyright (c) 2017 Jean-Marc Valin */
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|
/*
|
||||||
|
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 _NNET_H_
|
||||||
|
#define _NNET_H_
|
||||||
|
|
||||||
|
#define MAX_NEURONS 1024
|
||||||
|
#define MAX_CONV_INPUTS 1024
|
||||||
|
|
||||||
|
#define ACTIVATION_LINEAR 0
|
||||||
|
#define ACTIVATION_SIGMOID 1
|
||||||
|
#define ACTIVATION_TANH 2
|
||||||
|
#define ACTIVATION_RELU 3
|
||||||
|
#define ACTIVATION_SOFTMAX 4
|
||||||
|
|
||||||
|
typedef struct {
|
||||||
|
const float *bias;
|
||||||
|
const float *input_weights;
|
||||||
|
int nb_inputs;
|
||||||
|
int nb_neurons;
|
||||||
|
int activation;
|
||||||
|
} DenseLayer;
|
||||||
|
|
||||||
|
typedef struct {
|
||||||
|
const float *bias;
|
||||||
|
const float *input_weights;
|
||||||
|
const float *factor;
|
||||||
|
int nb_inputs;
|
||||||
|
int nb_neurons;
|
||||||
|
int nb_channels;
|
||||||
|
int activation;
|
||||||
|
} MDenseLayer;
|
||||||
|
|
||||||
|
typedef struct {
|
||||||
|
const float *bias;
|
||||||
|
const float *input_weights;
|
||||||
|
const float *recurrent_weights;
|
||||||
|
int nb_inputs;
|
||||||
|
int nb_neurons;
|
||||||
|
int activation;
|
||||||
|
int reset_after;
|
||||||
|
} GRULayer;
|
||||||
|
|
||||||
|
typedef struct {
|
||||||
|
const float *bias;
|
||||||
|
const float *input_weights;
|
||||||
|
int nb_inputs;
|
||||||
|
int kernel_size;
|
||||||
|
int nb_neurons;
|
||||||
|
int activation;
|
||||||
|
} Conv1DLayer;
|
||||||
|
|
||||||
|
typedef struct {
|
||||||
|
const float *embedding_weights;
|
||||||
|
int nb_inputs;
|
||||||
|
int dim;
|
||||||
|
} EmbeddingLayer;
|
||||||
|
|
||||||
|
void compute_activation(float *output, float *input, int N, int activation);
|
||||||
|
|
||||||
|
void compute_dense(const DenseLayer *layer, float *output, const float *input);
|
||||||
|
|
||||||
|
void compute_gru(const GRULayer *gru, float *state, const float *input);
|
||||||
|
|
||||||
|
void compute_conv1d(const Conv1DLayer *layer, float *output, float *mem, const float *input);
|
||||||
|
|
||||||
|
void compute_embedding(const EmbeddingLayer *layer, float *output, int input);
|
||||||
|
|
||||||
|
|
||||||
|
#endif /* _MLP_H_ */
|
||||||
45
dnn/tansig_table.h
Normal file
45
dnn/tansig_table.h
Normal file
|
|
@ -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,
|
||||||
|
};
|
||||||
Loading…
Add table
Add a link
Reference in a new issue