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more cleanup

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Jean-Marc Valin 2023-10-20 15:12:42 -04:00
parent 7f0d456c4b
commit 1032e47d3f
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2 changed files with 0 additions and 123 deletions
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55
dnn/nnet.h
View file

@ -92,16 +92,6 @@ typedef struct {
int activation; int activation;
} DenseLayer; } 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 { typedef struct {
const float *bias; const float *bias;
const float *subias; const float *subias;
@ -114,17 +104,6 @@ typedef struct {
int reset_after; int reset_after;
} GRULayer; } 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 { typedef struct {
const float *bias; const float *bias;
const float *input_weights; 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 _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); 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 ktime,
int kheight); 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, int dense_init(DenseLayer *layer, const WeightArray *arrays,
const char *bias, const char *bias,
const char *input_weights, const char *input_weights,
@ -211,30 +179,7 @@ int gru_init(GRULayer *layer, const WeightArray *arrays,
int activation, int activation,
int reset_after); 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); 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_ */ #endif /* _MLP_H_ */

68
dnn/parse_lpcnet_weights.c
View file

@ -175,24 +175,6 @@ int linear_init(LinearLayer *layer, const WeightArray *arrays,
return 0; 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, int dense_init(DenseLayer *layer, const WeightArray *arrays,
const char *bias, const char *bias,
@ -233,45 +215,6 @@ int gru_init(GRULayer *layer, const WeightArray *arrays,
return 0; 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, int conv2d_init(Conv2dLayer *layer, const WeightArray *arrays,
const char *bias, const char *bias,
const char *float_weights, const char *float_weights,
@ -297,17 +240,6 @@ int conv2d_init(Conv2dLayer *layer, const WeightArray *arrays,
return 0; 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 #if 0