shithub: opus

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Implement MDense

--- a/dnn/dump_lpcnet.py

+++ b/dnn/dump_lpcnet.py

@@ -80,8 +80,8 @@

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

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

     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], activation))

+    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('extern const MDenseLayer {};\n\n'.format(name));

     return False

--- a/dnn/nnet.c

+++ b/dnn/nnet.c

@@ -118,7 +118,6 @@

    int i;

    int N, M;

    int stride;

-   celt_assert(layer->nb_neurons <= MAX_NEURONS);

    M = layer->nb_inputs;

    N = layer->nb_neurons;

    stride = N;

@@ -130,6 +129,27 @@

 void compute_mdense(const MDenseLayer *layer, float *output, const float *input)

 {

+   int i, c;

+   int N, M, C;

+   int stride;

+   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];

+   gemm_accum(tmp, layer->input_weights, N*C, M, stride, input);

+   compute_activation(tmp, tmp, N*C, ACTIVATION_TANH);

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

+      output[i] = 0;

+   for (c=0;c<C;c++)

+   {

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

+         output[i] += tmp[c*N + i]*layer->factor[c*N + i];

+   }

+   compute_activation(output, output, N, layer->activation);

 }

 void compute_gru(const GRULayer *gru, float *state, const float *input)

@@ -189,7 +209,6 @@

    int N, M;

    int stride;

    float tmp[MAX_CONV_INPUTS];

-   celt_assert(layer->nb_neurons <= MAX_NEURONS);

    celt_assert(layer->nb_inputs*layer->kernel_size <= MAX_CONV_INPUTS);

    M = layer->nb_inputs;

    N = layer->nb_neurons;

-- 

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