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320 lines
14 KiB
Python
Executable file
320 lines
14 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 tensorflow.keras.optimizers import Adam
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from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding
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from ulaw import ulaw2lin, lin2ulaw
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from mdense import MDense
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from diffembed import diff_Embed
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import h5py
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import re
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# Flag for dumping e2e (differentiable lpc) network weights
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flag_e2e = False
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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, dtype='float', dotp=False):
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if dotp:
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vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
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vector = vector.transpose((2, 0, 3, 1))
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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 {} {}[{}] = {{\n '.format(dtype, 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 printSparseVector(f, A, name, have_diag=True):
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N = A.shape[0]
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M = A.shape[1]
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W = np.zeros((0,), dtype='int')
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W0 = np.zeros((0,))
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if have_diag:
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diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
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A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
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A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
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A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
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printVector(f, diag, name + '_diag')
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AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
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idx = np.zeros((0,), dtype='int')
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for i in range(M//8):
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pos = idx.shape[0]
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idx = np.append(idx, -1)
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nb_nonzero = 0
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for j in range(N//4):
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block = A[j*4:(j+1)*4, i*8:(i+1)*8]
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qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
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if np.sum(np.abs(block)) > 1e-10:
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nb_nonzero = nb_nonzero + 1
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idx = np.append(idx, j*4)
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vblock = qblock.transpose((1,0)).reshape((-1,))
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W0 = np.concatenate([W0, block.reshape((-1,))])
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W = np.concatenate([W, vblock])
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idx[pos] = nb_nonzero
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f.write('#ifdef DOT_PROD\n')
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printVector(f, W, name, dtype='qweight')
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f.write('#else /*DOT_PROD*/\n')
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printVector(f, W0, name, dtype='qweight')
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f.write('#endif /*DOT_PROD*/\n')
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#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
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printVector(f, idx, name + '_idx', dtype='int')
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return AQ
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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_sparse_gru(self, f, hf):
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global max_rnn_neurons
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name = 'sparse_' + self.name
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print("printing layer " + name + " of type sparse " + self.__class__.__name__)
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weights = self.get_weights()
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qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
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printVector(f, weights[-1], name + '_bias')
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subias = weights[-1].copy()
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subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
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printVector(f, subias, name + '_subias')
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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 SparseGRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
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.format(name, name, name, name, name, name, 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 SparseGRULayer {};\n\n'.format(name));
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return True
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def dump_grub(self, f, hf, gru_a_size):
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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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qweight = printSparseVector(f, weights[0][:gru_a_size, :], name + '_weights', have_diag=False)
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f.write('#ifdef DOT_PROD\n')
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qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
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printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
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f.write('#else /*DOT_PROD*/\n')
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printVector(f, weights[1], name + '_recurrent_weights')
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f.write('#endif /*DOT_PROD*/\n')
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printVector(f, weights[-1], name + '_bias')
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subias = weights[-1].copy()
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subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
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subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
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printVector(f, subias, name + '_subias')
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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 {}_subias,\n {}_weights,\n {}_weights_idx,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
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.format(name, name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after))
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hf.write('extern const GRULayer {};\n\n'.format(name));
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return True
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def dump_gru_layer_dummy(self, f, hf):
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name = self.name
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weights = self.get_weights()
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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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return True;
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GRU.dump_layer = dump_gru_layer_dummy
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def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
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printVector(f, weights, name + '_weights')
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printVector(f, bias, name + '_bias')
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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.shape[0], weights.shape[1], activation))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
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hf.write('extern const DenseLayer {};\n\n'.format(name));
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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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activation = self.activation.__name__.upper()
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dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
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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], (0, 2, 1)), 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[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[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('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
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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_impl(name, weights, f, hf):
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printVector(f, weights, name + '_weights')
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f.write('const EmbeddingLayer {} = {{\n {}_weights,\n {}, {}\n}};\n\n'
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.format(name, name, weights.shape[0], weights.shape[1]))
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hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
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hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
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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()[0]
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dump_embedding_layer_impl(name, weights, f, hf)
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return False
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Embedding.dump_layer = dump_embedding_layer
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diff_Embed.dump_layer = dump_embedding_layer
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filename = sys.argv[1]
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with h5py.File(filename, "r") as f:
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units = min(f['model_weights']['gru_a']['gru_a']['recurrent_kernel:0'].shape)
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units2 = min(f['model_weights']['gru_b']['gru_b']['recurrent_kernel:0'].shape)
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cond_size = min(f['model_weights']['feature_dense1']['feature_dense1']['kernel:0'].shape)
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model, _, _ = lpcnet.new_lpcnet_model(rnn_units1=units, rnn_units2=units2, flag_e2e = flag_e2e, cond_size=cond_size)
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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(filename, by_name=True)
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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')
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f.write('/*based on model {}*/\n\n'.format(sys.argv[1]))
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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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embed_size = lpcnet.embed_size
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E = model.get_layer('embed_sig').get_weights()[0]
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W = model.get_layer('gru_a').get_weights()[0][:embed_size,:]
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dump_embedding_layer_impl('gru_a_embed_sig', np.dot(E, W), f, hf)
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W = model.get_layer('gru_a').get_weights()[0][embed_size:2*embed_size,:]
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dump_embedding_layer_impl('gru_a_embed_pred', np.dot(E, W), f, hf)
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W = model.get_layer('gru_a').get_weights()[0][2*embed_size:3*embed_size,:]
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dump_embedding_layer_impl('gru_a_embed_exc', np.dot(E, W), f, hf)
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W = model.get_layer('gru_a').get_weights()[0][3*embed_size:,:]
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#FIXME: dump only half the biases
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b = model.get_layer('gru_a').get_weights()[2]
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dump_dense_layer_impl('gru_a_dense_feature', W, b, 'LINEAR', f, hf)
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W = model.get_layer('gru_b').get_weights()[0][model.rnn_units1:,:]
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b = model.get_layer('gru_b').get_weights()[2]
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# Set biases to zero because they'll be included in the GRU input part
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# (we need regular and SU biases)
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dump_dense_layer_impl('gru_b_dense_feature', W, 0*b, 'LINEAR', f, hf)
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dump_grub(model.get_layer('gru_b'), f, hf, model.rnn_units1)
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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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dump_sparse_gru(model.get_layer('gru_a'), f, hf)
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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('typedef struct {\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('} NNetState;\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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