ref: fd45fba905b580cd001637c3b9459e44323f6f0c
dir: /dnn/training_tf2/dump_plc.py/
#!/usr/bin/python3
'''Copyright (c) 2021-2022 Amazon
Copyright (c) 2017-2018 Mozilla
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
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- 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.
'''
import lpcnet_plc
import sys
import numpy as np
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import Layer, GRU, Dense, Conv1D, Embedding
import h5py
import re
# Flag for dumping e2e (differentiable lpc) network weights
flag_e2e = False
max_rnn_neurons = 1
max_conv_inputs = 1
def printVector(f, vector, name, dtype='float', dotp=False):
if dotp:
vector = vector.reshape((vector.shape[0]//4, 4, vector.shape[1]//8, 8))
vector = vector.transpose((2, 0, 3, 1))
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
f.write('static const {} {}[{}] = {{\n '.format(dtype, name, len(v)))
for i in range(0, len(v)):
f.write('{}'.format(v[i]))
if (i!=len(v)-1):
f.write(',')
else:
break;
if (i%8==7):
f.write("\n ")
else:
f.write(" ")
#print(v, file=f)
f.write('\n};\n\n')
return;
def printSparseVector(f, A, name, have_diag=True):
N = A.shape[0]
M = A.shape[1]
W = np.zeros((0,), dtype='int')
W0 = np.zeros((0,))
if have_diag:
diag = np.concatenate([np.diag(A[:,:N]), np.diag(A[:,N:2*N]), np.diag(A[:,2*N:])])
A[:,:N] = A[:,:N] - np.diag(np.diag(A[:,:N]))
A[:,N:2*N] = A[:,N:2*N] - np.diag(np.diag(A[:,N:2*N]))
A[:,2*N:] = A[:,2*N:] - np.diag(np.diag(A[:,2*N:]))
printVector(f, diag, name + '_diag')
AQ = np.minimum(127, np.maximum(-128, np.round(A*128))).astype('int')
idx = np.zeros((0,), dtype='int')
for i in range(M//8):
pos = idx.shape[0]
idx = np.append(idx, -1)
nb_nonzero = 0
for j in range(N//4):
block = A[j*4:(j+1)*4, i*8:(i+1)*8]
qblock = AQ[j*4:(j+1)*4, i*8:(i+1)*8]
if np.sum(np.abs(block)) > 1e-10:
nb_nonzero = nb_nonzero + 1
idx = np.append(idx, j*4)
vblock = qblock.transpose((1,0)).reshape((-1,))
W0 = np.concatenate([W0, block.reshape((-1,))])
W = np.concatenate([W, vblock])
idx[pos] = nb_nonzero
f.write('#ifdef DOT_PROD\n')
printVector(f, W, name, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
printVector(f, W0, name, dtype='qweight')
f.write('#endif /*DOT_PROD*/\n')
#idx = np.tile(np.concatenate([np.array([N]), np.arange(N)]), 3*N//16)
printVector(f, idx, name + '_idx', dtype='int')
return AQ
def dump_layer_ignore(self, f, hf):
print("ignoring layer " + self.name + " of type " + self.__class__.__name__)
return False
Layer.dump_layer = dump_layer_ignore
def dump_sparse_gru(self, f, hf):
global max_rnn_neurons
name = 'sparse_' + self.name
print("printing layer " + name + " of type sparse " + self.__class__.__name__)
weights = self.get_weights()
qweights = printSparseVector(f, weights[1], name + '_recurrent_weights')
printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy()
subias[1,:] = subias[1,:] - np.sum(qweights*(1./128),axis=0)
printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
activation = 'TANH'
if hasattr(self, 'reset_after') and not self.reset_after:
reset_after = 0
else:
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = max(max_rnn_neurons, neurons)
f.write('const SparseGRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_recurrent_weights_diag,\n {}_recurrent_weights,\n {}_recurrent_weights_idx,\n {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const SparseGRULayer {};\n\n'.format(name));
return True
def dump_gru_layer(self, f, hf):
global max_rnn_neurons
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
qweight = printSparseVector(f, weights[0], name + '_weights', have_diag=False)
f.write('#ifdef DOT_PROD\n')
qweight2 = np.clip(np.round(128.*weights[1]).astype('int'), -128, 127)
printVector(f, qweight2, name + '_recurrent_weights', dotp=True, dtype='qweight')
f.write('#else /*DOT_PROD*/\n')
printVector(f, weights[1], name + '_recurrent_weights')
f.write('#endif /*DOT_PROD*/\n')
printVector(f, weights[-1], name + '_bias')
subias = weights[-1].copy()
subias[0,:] = subias[0,:] - np.sum(qweight*(1./128.),axis=0)
subias[1,:] = subias[1,:] - np.sum(qweight2*(1./128.),axis=0)
printVector(f, subias, name + '_subias')
if hasattr(self, 'activation'):
activation = self.activation.__name__.upper()
else:
activation = 'TANH'
if hasattr(self, 'reset_after') and not self.reset_after:
reset_after = 0
else:
reset_after = 1
neurons = weights[0].shape[1]//3
max_rnn_neurons = max(max_rnn_neurons, neurons)
f.write('const GRULayer {} = {{\n {}_bias,\n {}_subias,\n {}_weights,\n {}_weights_idx,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('extern const GRULayer {};\n\n'.format(name));
return True
GRU.dump_layer = dump_gru_layer
def dump_gru_layer_dummy(self, f, hf):
name = self.name
weights = self.get_weights()
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
hf.write('#define {}_STATE_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
return True;
#GRU.dump_layer = dump_gru_layer_dummy
def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
printVector(f, weights, name + '_weights')
printVector(f, bias, name + '_bias')
f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights.shape[0], weights.shape[1], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
hf.write('extern const DenseLayer {};\n\n'.format(name));
def dump_dense_layer(self, f, hf):
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
activation = self.activation.__name__.upper()
dump_dense_layer_impl(name, weights[0], weights[1], activation, f, hf)
return False
Dense.dump_layer = dump_dense_layer
def dump_conv1d_layer(self, f, hf):
global max_conv_inputs
name = self.name
print("printing layer " + name + " of type " + self.__class__.__name__)
weights = self.get_weights()
printVector(f, weights[0], name + '_weights')
printVector(f, weights[-1], name + '_bias')
activation = self.activation.__name__.upper()
max_conv_inputs = max(max_conv_inputs, weights[0].shape[1]*weights[0].shape[0])
f.write('const Conv1DLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[2]))
hf.write('#define {}_STATE_SIZE ({}*{})\n'.format(name.upper(), weights[0].shape[1], (weights[0].shape[0]-1)))
hf.write('#define {}_DELAY {}\n'.format(name.upper(), (weights[0].shape[0]-1)//2))
hf.write('extern const Conv1DLayer {};\n\n'.format(name));
return True
Conv1D.dump_layer = dump_conv1d_layer
filename = sys.argv[1]
with h5py.File(filename, "r") as f:
units = min(f['model_weights']['plc_gru1']['plc_gru1']['recurrent_kernel:0'].shape)
units2 = min(f['model_weights']['plc_gru2']['plc_gru2']['recurrent_kernel:0'].shape)
cond_size = f['model_weights']['plc_dense1']['plc_dense1']['kernel:0'].shape[1]
model = lpcnet_plc.new_lpcnet_plc_model(rnn_units=units, cond_size=cond_size)
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['sparse_categorical_accuracy'])
#model.summary()
model.load_weights(filename, by_name=True)
if len(sys.argv) > 2:
cfile = sys.argv[2];
hfile = sys.argv[3];
else:
cfile = 'plc_data.c'
hfile = 'plc_data.h'
f = open(cfile, 'w')
hf = open(hfile, 'w')
f.write('/*This file is automatically generated from a Keras model*/\n')
f.write('/*based on model {}*/\n\n'.format(sys.argv[1]))
f.write('#ifdef HAVE_CONFIG_H\n#include "config.h"\n#endif\n\n#include "nnet.h"\n#include "{}"\n\n'.format(hfile))
hf.write('/*This file is automatically generated from a Keras model*/\n\n')
hf.write('#ifndef PLC_DATA_H\n#define PLC_DATA_H\n\n#include "nnet.h"\n\n')
layer_list = []
for i, layer in enumerate(model.layers):
if layer.dump_layer(f, hf):
layer_list.append(layer.name)
#dump_sparse_gru(model.get_layer('gru_a'), f, hf)
hf.write('#define PLC_MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
#hf.write('#define PLC_MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
hf.write('typedef struct {\n')
for i, name in enumerate(layer_list):
hf.write(' float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper()))
hf.write('} PLCNetState;\n')
hf.write('\n\n#endif\n')
f.close()
hf.close()