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198 lines
8 KiB
Python
Executable file
198 lines
8 KiB
Python
Executable file
#!/usr/bin/python3
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'''Copyright (c) 2017-2018 Mozilla
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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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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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max_rnn_neurons = 1
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max_conv_inputs = 1
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max_mdense_tmp = 1
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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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return 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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global max_rnn_neurons
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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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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') and not self.reset_after:
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reset_after = 0
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else:
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reset_after = 1
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neurons = weights[0].shape[1]//3
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max_rnn_neurons = max(max_rnn_neurons, neurons)
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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 {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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hf.write('#define {}_STATE_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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return 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 = self.activation.__name__.upper()
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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 {}_OUT_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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return 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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global max_mdense_tmp
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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, np.transpose(weights[0], (1, 2, 0)), name + '_weights')
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printVector(f, np.transpose(weights[1], (1, 0)), name + '_bias')
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printVector(f, np.transpose(weights[2], (1, 0)), name + '_factor')
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activation = self.activation.__name__.upper()
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max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2])
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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], weights[0].shape[2], activation))
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hf.write('#define {}_OUT_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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return False
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MDense.dump_layer = dump_mdense_layer
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def dump_conv1d_layer(self, f, hf):
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global max_conv_inputs
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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 = self.activation.__name__.upper()
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max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0])
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f.write('const Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
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.format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
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hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
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hf.write('extern const Conv1DLayer {};\n\n'.format(name));
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return True
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Conv1D.dump_layer = dump_conv1d_layer
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def dump_embedding_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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f.write('const EmbeddingLayer {} = {{\n {}_weights,\n {}, {}\n}};\n\n'
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.format(name, name, weights[0].shape[0], weights[0].shape[1]))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
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hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
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return False
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Embedding.dump_layer = dump_embedding_layer
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model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=384, use_gpu=False)
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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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if len(sys.argv) > 2:
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cfile = sys.argv[2];
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hfile = sys.argv[3];
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else:
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cfile = 'nnet_data.c'
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hfile = 'nnet_data.h'
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f = open(cfile, 'w')
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hf = open(hfile, '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 "{}"\n\n'.format(hfile))
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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 "nnet.h"\n\n')
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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('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
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hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
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hf.write('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp))
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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[{}_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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