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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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68
dnn/parse_lpcnet_weights.c
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@ -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