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Precomputing sizes

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Jean-Marc Valin 2018-11-24 15:47:48 -05:00
parent c025744e34
commit 94ac0841df
3 changed files with 25 additions and 16 deletions
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28
dnn/dump_lpcnet.py
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@ -12,6 +12,9 @@ import keras.backend as K
import h5py
import re
max_rnn_neurons = 1
max_conv_inputs = 1
def printVector(f, vector, name):
v = np.reshape(vector, (-1));
#print('static const float ', name, '[', len(v), '] = \n', file=f)
@ -36,6 +39,7 @@ def dump_layer_ignore(self, f, hf):
Layer.dump_layer = dump_layer_ignore
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()
@ -50,9 +54,12 @@ def dump_gru_layer(self, f, hf):
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 {}_weights,\n {}_recurrent_weights,\n {}, {}, ACTIVATION_{}, {}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1]//3, activation, reset_after))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]//3))
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
CuDNNGRU.dump_layer = dump_gru_layer
@ -67,7 +74,7 @@ def dump_dense_layer(self, f, hf):
activation = self.activation.__name__.upper()
f.write('const DenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, weights[0].shape[0], weights[0].shape[1], activation))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
hf.write('extern const DenseLayer {};\n\n'.format(name));
return False
Dense.dump_layer = dump_dense_layer
@ -82,23 +89,26 @@ def dump_mdense_layer(self, f, hf):
activation = self.activation.__name__.upper()
f.write('const MDenseLayer {} = {{\n {}_bias,\n {}_weights,\n {}_factor,\n {}, {}, {}, ACTIVATION_{}\n}};\n\n'
.format(name, name, name, name, weights[0].shape[0], weights[0].shape[1], weights[0].shape[2], activation))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))
hf.write('extern const MDenseLayer {};\n\n'.format(name));
return False
MDense.dump_layer = dump_mdense_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 {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
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('extern const Conv1DLayer {};\n\n'.format(name));
return False
return True
Conv1D.dump_layer = dump_conv1d_layer
@ -109,7 +119,7 @@ def dump_embedding_layer(self, f, hf):
printVector(f, weights[0], name + '_weights')
f.write('const EmbeddingLayer {} = {{\n {}_weights,\n {}, {}\n}};\n\n'
.format(name, name, weights[0].shape[0], weights[0].shape[1]))
hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[1]))
hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
return False
Embedding.dump_layer = dump_embedding_layer
@ -125,7 +135,6 @@ f = open(sys.argv[2], 'w')
hf = 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 "nnet.h"\n#include "foo.h"\n\n')
@ -137,9 +146,12 @@ for i, layer in enumerate(model.layers):
if layer.dump_layer(f, hf):
layer_list.append(layer.name)
hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
hf.write('struct RNNState {\n')
for i, name in enumerate(layer_list):
hf.write(' float {}_state[{}_SIZE];\n'.format(name, name.upper()))
hf.write(' float {}_state[{}_STATE_SIZE];\n'.format(name, name.upper()))
hf.write('};\n')
hf.write('\n\n#endif\n')