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normalization layer

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Joseph Redmon
2015-07-09 15:22:14 -07:00
parent 42d7438879
commit 75db98db25
13 changed files with 294 additions and 17 deletions
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src/normalization_layer.c Normal file
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#include "normalization_layer.h"
#include "blas.h"
#include <stdio.h>
layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa)
{
fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", w,h,c,size);
layer layer = {0};
layer.type = NORMALIZATION;
layer.batch = batch;
layer.h = layer.out_h = h;
layer.w = layer.out_w = w;
layer.c = layer.out_c = c;
layer.kappa = kappa;
layer.size = size;
layer.alpha = alpha;
layer.beta = beta;
layer.output = calloc(h * w * c * batch, sizeof(float));
layer.delta = calloc(h * w * c * batch, sizeof(float));
layer.squared = calloc(h * w * c * batch, sizeof(float));
layer.norms = calloc(h * w * c * batch, sizeof(float));
layer.inputs = w*h*c;
layer.outputs = layer.inputs;
#ifdef GPU
layer.output_gpu = cuda_make_array(0, h * w * c * batch);
layer.delta_gpu = cuda_make_array(0, h * w * c * batch);
layer.squared_gpu = cuda_make_array(0, h * w * c * batch);
layer.norms_gpu = cuda_make_array(0, h * w * c * batch);
#endif
return layer;
}
void resize_normalization_layer(layer *layer, int w, int h)
{
int c = layer->c;
int batch = layer->batch;
layer->h = h;
layer->w = w;
layer->out_h = h;
layer->out_w = w;
layer->inputs = w*h*c;
layer->outputs = layer->inputs;
layer->output = realloc(layer->output, h * w * layer->c * layer->batch * sizeof(float));
layer->delta = realloc(layer->delta, h * w * layer->c * layer->batch * sizeof(float));
layer->squared = realloc(layer->squared, h * w * layer->c * layer->batch * sizeof(float));
layer->norms = realloc(layer->norms, h * w * layer->c * layer->batch * sizeof(float));
#ifdef GPU
cuda_free(layer->output_gpu);
cuda_free(layer->delta_gpu);
cuda_free(layer->squared_gpu);
cuda_free(layer->norms_gpu);
layer->output_gpu = cuda_make_array(0, h * w * c * batch);
layer->delta_gpu = cuda_make_array(0, h * w * c * batch);
layer->squared_gpu = cuda_make_array(0, h * w * c * batch);
layer->norms_gpu = cuda_make_array(0, h * w * c * batch);
#endif
}
void forward_normalization_layer(const layer layer, network_state state)
{
int k,b;
int w = layer.w;
int h = layer.h;
int c = layer.c;
scal_cpu(w*h*c*layer.batch, 0, layer.squared, 1);
for(b = 0; b < layer.batch; ++b){
float *squared = layer.squared + w*h*c*b;
float *norms = layer.norms + w*h*c*b;
float *input = state.input + w*h*c*b;
pow_cpu(w*h*c, 2, input, 1, squared, 1);
const_cpu(w*h, layer.kappa, norms, 1);
for(k = 0; k < layer.size/2; ++k){
axpy_cpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1);
}
for(k = 1; k < layer.c; ++k){
copy_cpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1);
int prev = k - ((layer.size-1)/2) - 1;
int next = k + (layer.size/2);
if(prev >= 0) axpy_cpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1);
if(next < layer.c) axpy_cpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1);
}
}
pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, layer.output, 1);
mul_cpu(w*h*c*layer.batch, state.input, 1, layer.output, 1);
}
void backward_normalization_layer(const layer layer, network_state state)
{
// TODO This is approximate ;-)
int w = layer.w;
int h = layer.h;
int c = layer.c;
pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, state.delta, 1);
mul_cpu(w*h*c*layer.batch, layer.delta, 1, state.delta, 1);
}
#ifdef GPU
void forward_normalization_layer_gpu(const layer layer, network_state state)
{
int k,b;
int w = layer.w;
int h = layer.h;
int c = layer.c;
scal_ongpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1);
for(b = 0; b < layer.batch; ++b){
float *squared = layer.squared_gpu + w*h*c*b;
float *norms = layer.norms_gpu + w*h*c*b;
float *input = state.input + w*h*c*b;
pow_ongpu(w*h*c, 2, input, 1, squared, 1);
const_ongpu(w*h, layer.kappa, norms, 1);
for(k = 0; k < layer.size/2; ++k){
axpy_ongpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1);
}
for(k = 1; k < layer.c; ++k){
copy_ongpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1);
int prev = k - ((layer.size-1)/2) - 1;
int next = k + (layer.size/2);
if(prev >= 0) axpy_ongpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1);
if(next < layer.c) axpy_ongpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1);
}
}
pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1);
mul_ongpu(w*h*c*layer.batch, state.input, 1, layer.output_gpu, 1);
}
void backward_normalization_layer_gpu(const layer layer, network_state state)
{
// TODO This is approximate ;-)
int w = layer.w;
int h = layer.h;
int c = layer.c;
pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, state.delta, 1);
mul_ongpu(w*h*c*layer.batch, layer.delta_gpu, 1, state.delta, 1);
}
#endif