shithub: opus

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mdense max size

--- a/dnn/dump_lpcnet.py

+++ b/dnn/dump_lpcnet.py

@@ -14,6 +14,7 @@

 max_rnn_neurons = 1

 max_conv_inputs = 1

+max_mdense_tmp = 1

 def printVector(f, vector, name):

     v = np.reshape(vector, (-1));

@@ -80,6 +81,7 @@

 Dense.dump_layer = dump_dense_layer

 def dump_mdense_layer(self, f, hf):

+    global max_mdense_tmp

     name = self.name

     print("printing layer " + name + " of type " + self.__class__.__name__)

     weights = self.get_weights()

@@ -87,6 +89,7 @@

     printVector(f, weights[1], name + '_bias')

     printVector(f, weights[1], name + '_factor')

     activation = self.activation.__name__.upper()

+    max_mdense_tmp = max(max_mdense_tmp, weights[0].shape[0]*weights[0].shape[2])

     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 {}_OUT_SIZE {}\n'.format(name.upper(), weights[0].shape[0]))

@@ -148,6 +151,8 @@

 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('#define MAX_MDENSE_TMP {}\n\n'.format(max_mdense_tmp))

+

 hf.write('struct RNNState {\n')

 for i, name in enumerate(layer_list):

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -132,11 +132,10 @@

    int i, c;

    int N, M, C;

    int stride;

+   float tmp[MAX_MDENSE_TMP];

    M = layer->nb_inputs;

    N = layer->nb_neurons;

    C = layer->nb_channels;

-   /* FIXME: Make this C90. */

-   float tmp[N*C];

    stride = N*C;

    for (i=0;i<N*C;i++)

       tmp[i] = layer->bias[i];

-- 

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