more cleanup

dnn/nnet.h

@@ -92,16 +92,6 @@ typedef struct {
   int activation;
 } DenseLayer;
 
-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;
@@ -114,17 +104,6 @@ typedef struct {
   int reset_after;
 } GRULayer;
 
-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;
@@ -151,8 +130,6 @@ void compute_activation(float *output, const float *input, int N, int activation
 
 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 conv2d_init(Conv2dLayer *layer, const WeightArray *arrays,
   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 gru_init(GRULayer *layer, const WeightArray *arrays,
   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_ */

dnn/parse_lpcnet_weights.c

@@ -175,24 +175,6 @@ int linear_init(LinearLayer *layer, const WeightArray *arrays,
   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 @@ int gru_init(GRULayer *layer, const WeightArray *arrays,
   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,
@@ -297,17 +240,6 @@ int conv2d_init(Conv2dLayer *layer, const WeightArray *arrays,
   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;
-}
-
 
 
 #if 0