diff options
author | Jean-Marc Valin <jmvalin@amazon.com> | 2023-10-20 22:12:42 +0300 |
---|---|---|
committer | Jean-Marc Valin <jmvalin@amazon.com> | 2023-10-20 22:13:43 +0300 |
commit | 1032e47d3f3376947280d2c7769c522b6474c6ad (patch) | |
tree | 318102192efd776fd963a462fbf129e75cbf21ea | |
parent | 7f0d456c4b3c1579f0884f2e26c55fea45d7e00a (diff) |
more cleanup
-rw-r--r-- | dnn/nnet.h | 55 | ||||
-rw-r--r-- | dnn/parse_lpcnet_weights.c | 68 |
2 files changed, 0 insertions, 123 deletions
@@ -94,16 +94,6 @@ typedef struct { 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 { 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 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_ */ diff --git a/dnn/parse_lpcnet_weights.c b/dnn/parse_lpcnet_weights.c index 9805ec8c..be2dafdc 100644 --- a/dnn/parse_lpcnet_weights.c +++ b/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 |