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#!/usr/bin/python
from __future__ import print_function
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers import GRU
from keras.models import load_model
from keras import backend as K
import sys
import re
import numpy as np
def printVector(f, ft, vector, name):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const rnn_weight {}[{}] = {{\n '.format(name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(min(127, int(round(256*v[i])))))
ft.write('{}'.format(min(127, int(round(256*v[i])))))
if (i!=len(v)-1):
f.write(',')
else:
break;
ft.write(" ")
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
#print(v, file=f)
f.write('\n};\n\n')
ft.write("\n")
return;
def printLayer(f, ft, layer):
weights = layer.get_weights()
activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
if len(weights) > 2:
ft.write('{} {} '.format(weights[0].shape[0], weights[0].shape[1]/3))
else:
ft.write('{} {} '.format(weights[0].shape[0], weights[0].shape[1]))
if activation == 'SIGMOID':
ft.write('1\n')
elif activation == 'RELU':
ft.write('2\n')
else:
ft.write('0\n')
printVector(f, ft, weights[0], layer.name + '_weights')
if len(weights) > 2:
printVector(f, ft, weights[1], layer.name + '_recurrent_weights')
printVector(f, ft, weights[-1], layer.name + '_bias')
name = layer.name
if len(weights) > 2:
f.write('static const GRULayer {} = {{\n {}_bias,\n {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]/3, activation))
else:
f.write('static const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights[0].shape[0], weights[0].shape[1], activation))
def structLayer(f, layer):
weights = layer.get_weights()
name = layer.name
if len(weights) > 2:
f.write(' {},\n'.format(weights[0].shape[1]/3))
else:
f.write(' {},\n'.format(weights[0].shape[1]))
f.write(' &{},\n'.format(name))
def foo(c, name):
return None
def mean_squared_sqrt_error(y_true, y_pred):
return K.mean(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
model = load_model(sys.argv[1], custom_objects={'msse': mean_squared_sqrt_error, 'mean_squared_sqrt_error': mean_squared_sqrt_error, 'my_crossentropy': mean_squared_sqrt_error, 'mycost': mean_squared_sqrt_error, 'WeightClip': foo})
weights = model.get_weights()
f = open(sys.argv[2], 'w')
ft = open(sys.argv[3], 'w')
f.write('/*This file is automatically generated from a Keras model*/\n\n')
f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "rnn.h"\n#include "rnn_data.h"\n\n')
ft.write('rnnoise-nu model file version 1\n')
layer_list = []
for i, layer in enumerate(model.layers):
if len(layer.get_weights()) > 0:
printLayer(f, ft, layer)
if len(layer.get_weights()) > 2:
layer_list.append(layer.name)
f.write('const struct RNNModel rnnoise_model_{} = {{\n'.format(sys.argv[4]))
for i, layer in enumerate(model.layers):
if len(layer.get_weights()) > 0:
structLayer(f, layer)
f.write('};\n')
#hf.write('struct RNNState {\n')
#for i, name in enumerate(layer_list):
# hf.write(' float {}_state[{}_SIZE];\n'.format(name, name.upper()))
#hf.write('};\n')
f.close()
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