shithub: opus

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more cleanup

--- a/dnn/nnet.h

+++ b/dnn/nnet.h

@@ -94,16 +94,6 @@

 typedef struct {

   const float *bias;

-  const float *input_weights;

-  const float *factor;

-  int nb_inputs;

-  int nb_neurons;

-  int nb_channels;

-  int activation;

-} MDenseLayer;

-

-typedef struct {

-  const float *bias;

   const float *subias;

   const qweight *input_weights;

   const int *input_weights_idx;

@@ -116,17 +106,6 @@

 typedef struct {

   const float *bias;

-  const float *subias;

-  const float *diag_weights;

-  const qweight *recurrent_weights;

-  const int *idx;

-  int nb_neurons;

-  int activation;

-  int reset_after;

-} SparseGRULayer;

-

-typedef struct {

-  const float *bias;

   const float *input_weights;

   int nb_inputs;

   int kernel_size;

@@ -151,8 +130,6 @@

 void _lpcnet_compute_dense(const DenseLayer *layer, float *output, const float *input);

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

-

 void compute_gruB(const GRULayer *gru, const float* gru_b_condition, float *state, const float *input);

@@ -184,15 +161,6 @@

   int ktime,

   int kheight);

-int mdense_init(MDenseLayer *layer, const WeightArray *arrays,

-  const char *bias,

-  const char *input_weights,

-  const char *factor,

-  int nb_inputs,

-  int nb_neurons,

-  int nb_channels,

-  int activation);

-

 int dense_init(DenseLayer *layer, const WeightArray *arrays,

   const char *bias,

   const char *input_weights,

@@ -211,30 +179,7 @@

   int activation,

   int reset_after);

-int sparse_gru_init(SparseGRULayer *layer, const WeightArray *arrays,

-  const char *bias,

-  const char *subias,

-  const char *diag_weights,

-  const char *recurrent_weights,

-  const char *idx,

-  int nb_neurons,

-  int activation,

-  int reset_after);

-

-int conv1d_init(Conv1DLayer *layer, const WeightArray *arrays,

-  const char *bias,

-  const char *input_weights,

-  int nb_inputs,

-  int kernel_size,

-  int nb_neurons,

-  int activation);

-

 void compute_conv2d(const Conv2dLayer *conv, float *out, float *mem, const float *in, int height, int hstride, int activation);

-

-int embedding_init(EmbeddingLayer *layer, const WeightArray *arrays,

-  const char *embedding_weights,

-  int nb_inputs,

-  int dim);

 #endif /* _MLP_H_ */

--- a/dnn/parse_lpcnet_weights.c

+++ b/dnn/parse_lpcnet_weights.c

@@ -175,24 +175,6 @@

   return 0;

 }

-int mdense_init(MDenseLayer *layer, const WeightArray *arrays,

-  const char *bias,

-  const char *input_weights,

-  const char *factor,

-  int nb_inputs,

-  int nb_neurons,

-  int nb_channels,

-  int activation)

-{

-  if ((layer->bias = find_array_check(arrays, bias, nb_neurons*nb_channels*sizeof(layer->bias[0]))) == NULL) return 1;

-  if ((layer->input_weights = find_array_check(arrays, input_weights, nb_inputs*nb_channels*nb_neurons*sizeof(layer->input_weights[0]))) == NULL) return 1;

-  if ((layer->factor = find_array_check(arrays, factor, nb_channels*nb_neurons*sizeof(layer->factor[0]))) == NULL) return 1;

-  layer->nb_inputs = nb_inputs;

-  layer->nb_neurons = nb_neurons;

-  layer->nb_channels = nb_channels;

-  layer->activation = activation;

-  return 0;

-}

 int dense_init(DenseLayer *layer, const WeightArray *arrays,

   const char *bias,

@@ -233,45 +215,6 @@

   return 0;

 }

-int sparse_gru_init(SparseGRULayer *layer, const WeightArray *arrays,

-  const char *bias,

-  const char *subias,

-  const char *diag_weights,

-  const char *recurrent_weights,

-  const char *idx,

-  int nb_neurons,

-  int activation,

-  int reset_after)

-{

-  int total_blocks;

-  if ((layer->bias = find_array_check(arrays, bias, 6*nb_neurons*sizeof(layer->bias[0]))) == NULL) return 1;

-  if ((layer->subias = find_array_check(arrays, subias, 6*nb_neurons*sizeof(layer->subias[0]))) == NULL) return 1;

-  if ((layer->diag_weights = find_array_check(arrays, diag_weights, 3*nb_neurons*sizeof(layer->diag_weights[0]))) == NULL) return 1;

-  if ((layer->idx = find_idx_check(arrays, idx, nb_neurons, 3*nb_neurons, &total_blocks)) == NULL) return 1;

-  if ((layer->recurrent_weights = find_array_check(arrays, recurrent_weights, SPARSE_BLOCK_SIZE*total_blocks*sizeof(layer->recurrent_weights[0]))) == NULL) return 1;

-  layer->nb_neurons = nb_neurons;

-  layer->activation = activation;

-  layer->reset_after = reset_after;

-  return 0;

-}

-

-int conv1d_init(Conv1DLayer *layer, const WeightArray *arrays,

-  const char *bias,

-  const char *input_weights,

-  int nb_inputs,

-  int kernel_size,

-  int nb_neurons,

-  int activation)

-{

-  if ((layer->bias = find_array_check(arrays, bias, nb_neurons*sizeof(layer->bias[0]))) == NULL) return 1;

-  if ((layer->input_weights = find_array_check(arrays, input_weights, kernel_size*nb_inputs*nb_neurons*sizeof(layer->input_weights[0]))) == NULL) return 1;

-  layer->nb_inputs = nb_inputs;

-  layer->kernel_size = kernel_size;

-  layer->nb_neurons = nb_neurons;

-  layer->activation = activation;

-  return 0;

-}

-

 int conv2d_init(Conv2dLayer *layer, const WeightArray *arrays,

   const char *bias,

   const char *float_weights,

@@ -294,17 +237,6 @@

   layer->out_channels = out_channels;

   layer->ktime = ktime;

   layer->kheight = kheight;

-  return 0;

-}

-

-int embedding_init(EmbeddingLayer *layer, const WeightArray *arrays,

-  const char *embedding_weights,

-  int nb_inputs,

-  int dim)

-{

-  if ((layer->embedding_weights = find_array_check(arrays, embedding_weights, nb_inputs*dim*sizeof(layer->embedding_weights[0]))) == NULL) return 1;

-  layer->nb_inputs = nb_inputs;

-  layer->dim = dim;

   return 0;

 }

-- 

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