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Code for building a model struct

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This commit is contained in:
Jean-Marc Valin 2023-05-16 23:15:49 -04:00
parent cc714cc5b0
commit 71c8a23fc1
4 changed files with 200 additions and 4 deletions
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34
dnn/training_tf2/dump_lpcnet.py
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@ -26,6 +26,7 @@
'''
import os
import io
import lpcnet
import sys
import numpy as np
@ -39,7 +40,6 @@ import h5py
import re
import argparse
array_list = []
# no cuda devices needed
os.environ['CUDA_VISIBLE_DEVICES'] = ""
@ -148,6 +148,9 @@ def dump_sparse_gru(self, f, hf):
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));
model_struct.write(' SparseGRULayer {};\n'.format(name));
model_init.write(' if (sparse_gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_recurrent_weights_diag", "{}_recurrent_weights", "{}_recurrent_weights_idx", {}, ACTIVATION_{}, {})) return 1;\n'
.format(name, name, name, name, name, name, weights[0].shape[1]//3, activation, reset_after))
return True
def dump_grub(self, f, hf, gru_a_size):
@ -182,6 +185,9 @@ def dump_grub(self, f, hf, gru_a_size):
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, gru_a_size, weights[0].shape[1]//3, activation, reset_after))
hf.write('extern const GRULayer {};\n\n'.format(name));
model_struct.write(' GRULayer {};\n'.format(name));
model_init.write(' if (gru_init(&model->{}, arrays, "{}_bias", "{}_subias", "{}_weights", "{}_weights_idx", "{}_recurrent_weights", {}, {}, ACTIVATION_{}, {})) return 1;\n'
.format(name, name, name, name, name, name, gru_a_size, weights[0].shape[1]//3, activation, reset_after))
return True
def dump_gru_layer_dummy(self, f, hf):
@ -200,6 +206,9 @@ def dump_dense_layer_impl(name, weights, bias, activation, f, hf):
.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));
model_struct.write(' DenseLayer {};\n'.format(name));
model_init.write(' if (dense_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, ACTIVATION_{})) return 1;\n'
.format(name, name, name, weights.shape[0], weights.shape[1], activation))
def dump_dense_layer(self, f, hf):
name = self.name
@ -225,6 +234,9 @@ def dump_mdense_layer(self, f, hf):
.format(name, 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[0]))
hf.write('extern const MDenseLayer {};\n\n'.format(name));
model_struct.write(' MDenseLayer {};\n'.format(name));
model_init.write(' if (mdense_init(&model->{}, arrays, "{}_bias", "{}_weights", "{}_factor", {}, {}, {}, ACTIVATION_{})) return 1;\n'
.format(name, name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
return False
MDense.dump_layer = dump_mdense_layer
@ -243,6 +255,9 @@ def dump_conv1d_layer(self, f, hf):
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));
model_struct.write(' Conv1DLayer {};\n'.format(name));
model_init.write(' if (conv1d_init(&model->{}, arrays, "{}_bias", "{}_weights", {}, {}, {}, ACTIVATION_{})) return 1;\n'
.format(name, name, name, weights[0].shape[1], weights[0].shape[0], weights[0].shape[2], activation))
return True
Conv1D.dump_layer = dump_conv1d_layer
@ -253,6 +268,9 @@ def dump_embedding_layer_impl(name, weights, f, hf):
.format(name, name, weights.shape[0], weights.shape[1]))
hf.write('#define {}_OUT_SIZE {}\n'.format(name.upper(), weights.shape[1]))
hf.write('extern const EmbeddingLayer {};\n\n'.format(name));
model_struct.write(' EmbeddingLayer {};\n'.format(name));
model_init.write(' if (embedding_init(&model->{}, arrays, "{}_weights", {}, {})) return 1;\n'
.format(name, name, weights.shape[0], weights.shape[1]))
def dump_embedding_layer(self, f, hf):
name = self.name
@ -291,6 +309,11 @@ if __name__ == "__main__":
f = open(cfile, 'w')
hf = open(hfile, 'w')
model_struct = io.StringIO()
model_init = io.StringIO()
model_struct.write('typedef struct {\n')
model_init.write('int init_lpcnet_model(LPCNetModel *model, const WeightArray *arrays) {\n')
array_list = []
f.write('/*This file is automatically generated from a Keras model*/\n')
f.write('/*based on model {}*/\n\n'.format(sys.argv[1]))
@ -359,7 +382,10 @@ if __name__ == "__main__":
f.write(' {{"{}", WEIGHTS_{}_TYPE, sizeof({}), {}}},\n'.format(name, name, name, name))
f.write('#endif\n')
f.write(' {NULL, 0, 0}\n};\n')
f.write('#endif\n')
f.write('#endif\n\n')
model_init.write(' return 0;\n}\n')
f.write(model_init.getvalue())
hf.write('#define MAX_RNN_NEURONS {}\n\n'.format(max_rnn_neurons))
hf.write('#define MAX_CONV_INPUTS {}\n\n'.format(max_conv_inputs))
@ -369,8 +395,10 @@ if __name__ == "__main__":
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('} NNetState;\n')
hf.write('} NNetState;\n\n')
model_struct.write('} LPCNetModel;\n\n')
hf.write(model_struct.getvalue())
hf.write('\n\n#endif\n')
f.close()