Made dump_rnn output in nu format.

Author: GregorR

training/dump_rnn.py

@@ -30,7 +30,7 @@ def printVector(f, vector, name):
     f.write('\n};\n\n')
     return;
 
-def printLayer(f, hf, layer):
+def printLayer(f, layer):
     weights = layer.get_weights()
     printVector(f, weights[0], layer.name + '_weights')
     if len(weights) > 2:
@@ -39,19 +39,24 @@ def printLayer(f, hf, layer):
     name = layer.name
     activation = re.search('function (.*) at', str(layer.activation)).group(1).upper()
     if len(weights) > 2:
-        f.write('const GRULayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}_recurrent_weights,\n   {}, {}, ACTIVATION_{}\n}};\n\n'
+        f.write('static 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))
-        hf.write('#define {}_SIZE {}\n'.format(name.upper(), weights[0].shape[1]/3))
-        hf.write('extern const GRULayer {};\n\n'.format(name));
     else:
-        f.write('const DenseLayer {} = {{\n   {}_bias,\n   {}_weights,\n   {}, {}, ACTIVATION_{}\n}};\n\n'
+        f.write('static 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('extern const DenseLayer {};\n\n'.format(name));
+
+def structLayer(f, layer):
+    weights = layer.get_weights()
+    name = layer.name
+    if len(weights) > 2:
+        f.write('    {},\n'.format(weights[0].shape[1]/3))
+    else:
+        f.write('    {},\n'.format(weights[0].shape[1]))
+    f.write('    &{},\n'.format(name))
 
 
 def foo(c, name):
-    return 1
+    return None
 
 def mean_squared_sqrt_error(y_true, y_pred):
     return K.mean(K.square(K.sqrt(y_pred) - K.sqrt(y_true)), axis=-1)
@@ -62,27 +67,26 @@ def mean_squared_sqrt_error(y_true, y_pred):
 weights = model.get_weights()
 
 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 "rnn.h"\n\n')
 
-hf.write('/*This file is automatically generated from a Keras model*/\n\n')
-hf.write('#ifndef RNN_DATA_H\n#define RNN_DATA_H\n\n#include "rnn.h"\n\n')
-
 layer_list = []
 for i, layer in enumerate(model.layers):
     if len(layer.get_weights()) > 0:
-        printLayer(f, hf, layer)
+        printLayer(f, layer)
     if len(layer.get_weights()) > 2:
         layer_list.append(layer.name)
 
-hf.write('struct RNNState {\n')
-for i, name in enumerate(layer_list):
-    hf.write('  float {}_state[{}_SIZE];\n'.format(name, name.upper())) 
-hf.write('};\n')
+f.write('const struct RNNModel rnnoise_model_{} = {{\n'.format(sys.argv[3]))
+for i, layer in enumerate(model.layers):
+    if len(layer.get_weights()) > 0:
+        structLayer(f, layer)
+f.write('};\n')
 
-hf.write('\n\n#endif\n')
+#hf.write('struct RNNState {\n')
+#for i, name in enumerate(layer_list):
+#    hf.write('  float {}_state[{}_SIZE];\n'.format(name, name.upper())) 
+#hf.write('};\n')
 
 f.close()
-hf.close()