1#!/usr/bin/python3 2'''Copyright (c) 2021-2022 Amazon 3 Copyright (c) 2018-2019 Mozilla 4 5 Redistribution and use in source and binary forms, with or without 6 modification, are permitted provided that the following conditions 7 are met: 8 9 - Redistributions of source code must retain the above copyright 10 notice, this list of conditions and the following disclaimer. 11 12 - Redistributions in binary form must reproduce the above copyright 13 notice, this list of conditions and the following disclaimer in the 14 documentation and/or other materials provided with the distribution. 15 16 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS 17 ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT 18 LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR 19 A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE FOUNDATION OR 20 CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, 21 EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 22 PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR 23 PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF 24 LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING 25 NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS 26 SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 27''' 28 29import math 30import tensorflow as tf 31from tensorflow.keras.models import Model 32from tensorflow.keras.layers import Input, GRU, Dense, Embedding, Reshape, Concatenate, Lambda, Conv1D, Multiply, Add, Bidirectional, MaxPooling1D, Activation, GaussianNoise 33from tensorflow.compat.v1.keras.layers import CuDNNGRU 34from tensorflow.keras import backend as K 35from tensorflow.keras.constraints import Constraint 36from tensorflow.keras.initializers import Initializer 37from tensorflow.keras.callbacks import Callback 38import numpy as np 39 40def quant_regularizer(x): 41 Q = 128 42 Q_1 = 1./Q 43 #return .01 * tf.reduce_mean(1 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x)))) 44 return .01 * tf.reduce_mean(K.sqrt(K.sqrt(1.0001 - tf.math.cos(2*3.1415926535897931*(Q*x-tf.round(Q*x)))))) 45 46 47class WeightClip(Constraint): 48 '''Clips the weights incident to each hidden unit to be inside a range 49 ''' 50 def __init__(self, c=2): 51 self.c = c 52 53 def __call__(self, p): 54 # Ensure that abs of adjacent weights don't sum to more than 127. Otherwise there's a risk of 55 # saturation when implementing dot products with SSSE3 or AVX2. 56 return self.c*p/tf.maximum(self.c, tf.repeat(tf.abs(p[:, 1::2])+tf.abs(p[:, 0::2]), 2, axis=1)) 57 #return K.clip(p, -self.c, self.c) 58 59 def get_config(self): 60 return {'name': self.__class__.__name__, 61 'c': self.c} 62 63constraint = WeightClip(0.992) 64 65def new_lpcnet_plc_model(rnn_units=256, nb_used_features=20, nb_burg_features=36, batch_size=128, training=False, adaptation=False, quantize=False, cond_size=128): 66 feat = Input(shape=(None, nb_used_features+nb_burg_features), batch_size=batch_size) 67 lost = Input(shape=(None, 1), batch_size=batch_size) 68 69 fdense1 = Dense(cond_size, activation='tanh', name='plc_dense1') 70 71 cfeat = Concatenate()([feat, lost]) 72 cfeat = fdense1(cfeat) 73 #cfeat = Conv1D(cond_size, 3, padding='causal', activation='tanh', name='plc_conv1')(cfeat) 74 75 quant = quant_regularizer if quantize else None 76 77 if training: 78 rnn = CuDNNGRU(rnn_units, return_sequences=True, return_state=True, name='plc_gru1', stateful=True, 79 kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) 80 rnn2 = CuDNNGRU(rnn_units, return_sequences=True, return_state=True, name='plc_gru2', stateful=True, 81 kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) 82 else: 83 rnn = GRU(rnn_units, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='plc_gru1', stateful=True, 84 kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) 85 rnn2 = GRU(rnn_units, return_sequences=True, return_state=True, recurrent_activation="sigmoid", reset_after='true', name='plc_gru2', stateful=True, 86 kernel_constraint=constraint, recurrent_constraint = constraint, kernel_regularizer=quant, recurrent_regularizer=quant) 87 88 gru_out1, _ = rnn(cfeat) 89 gru_out1 = GaussianNoise(.005)(gru_out1) 90 gru_out2, _ = rnn2(gru_out1) 91 92 out_dense = Dense(nb_used_features, activation='linear', name='plc_out') 93 plc_out = out_dense(gru_out2) 94 95 model = Model([feat, lost], plc_out) 96 model.rnn_units = rnn_units 97 model.cond_size = cond_size 98 model.nb_used_features = nb_used_features 99 model.nb_burg_features = nb_burg_features 100 101 return model 102