mirror of https://github.com/AlexeyAB/darknet.git
Joseph Redmon
10 years ago
parent
42d7438879
commit
75db98db25
13 changed files with 294 additions and 17 deletions
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#include "normalization_layer.h" |
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#include "blas.h" |
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#include <stdio.h> |
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layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa) |
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{ |
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fprintf(stderr, "Local Response Normalization Layer: %d x %d x %d image, %d size\n", w,h,c,size); |
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layer layer = {0}; |
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layer.type = NORMALIZATION; |
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layer.batch = batch; |
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layer.h = layer.out_h = h; |
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layer.w = layer.out_w = w; |
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layer.c = layer.out_c = c; |
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layer.kappa = kappa; |
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layer.size = size; |
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layer.alpha = alpha; |
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layer.beta = beta; |
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layer.output = calloc(h * w * c * batch, sizeof(float)); |
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layer.delta = calloc(h * w * c * batch, sizeof(float)); |
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layer.squared = calloc(h * w * c * batch, sizeof(float)); |
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layer.norms = calloc(h * w * c * batch, sizeof(float)); |
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layer.inputs = w*h*c; |
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layer.outputs = layer.inputs; |
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#ifdef GPU |
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layer.output_gpu = cuda_make_array(0, h * w * c * batch); |
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layer.delta_gpu = cuda_make_array(0, h * w * c * batch); |
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layer.squared_gpu = cuda_make_array(0, h * w * c * batch); |
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layer.norms_gpu = cuda_make_array(0, h * w * c * batch); |
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#endif |
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return layer; |
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} |
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|
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void resize_normalization_layer(layer *layer, int w, int h) |
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{ |
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int c = layer->c; |
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int batch = layer->batch; |
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layer->h = h; |
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layer->w = w; |
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layer->out_h = h; |
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layer->out_w = w; |
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layer->inputs = w*h*c; |
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layer->outputs = layer->inputs; |
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layer->output = realloc(layer->output, h * w * layer->c * layer->batch * sizeof(float)); |
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layer->delta = realloc(layer->delta, h * w * layer->c * layer->batch * sizeof(float)); |
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layer->squared = realloc(layer->squared, h * w * layer->c * layer->batch * sizeof(float)); |
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layer->norms = realloc(layer->norms, h * w * layer->c * layer->batch * sizeof(float)); |
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#ifdef GPU |
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cuda_free(layer->output_gpu); |
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cuda_free(layer->delta_gpu);
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cuda_free(layer->squared_gpu);
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cuda_free(layer->norms_gpu);
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layer->output_gpu = cuda_make_array(0, h * w * c * batch); |
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layer->delta_gpu = cuda_make_array(0, h * w * c * batch); |
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layer->squared_gpu = cuda_make_array(0, h * w * c * batch); |
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layer->norms_gpu = cuda_make_array(0, h * w * c * batch); |
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#endif |
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} |
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void forward_normalization_layer(const layer layer, network_state state) |
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{ |
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int k,b; |
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int w = layer.w; |
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int h = layer.h; |
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int c = layer.c; |
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scal_cpu(w*h*c*layer.batch, 0, layer.squared, 1); |
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for(b = 0; b < layer.batch; ++b){ |
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float *squared = layer.squared + w*h*c*b; |
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float *norms = layer.norms + w*h*c*b; |
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float *input = state.input + w*h*c*b; |
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pow_cpu(w*h*c, 2, input, 1, squared, 1); |
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const_cpu(w*h, layer.kappa, norms, 1); |
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for(k = 0; k < layer.size/2; ++k){ |
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axpy_cpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); |
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} |
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for(k = 1; k < layer.c; ++k){ |
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copy_cpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); |
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int prev = k - ((layer.size-1)/2) - 1; |
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int next = k + (layer.size/2); |
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if(prev >= 0) axpy_cpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); |
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if(next < layer.c) axpy_cpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); |
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} |
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} |
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pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, layer.output, 1); |
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mul_cpu(w*h*c*layer.batch, state.input, 1, layer.output, 1); |
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} |
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void backward_normalization_layer(const layer layer, network_state state) |
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{ |
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// TODO This is approximate ;-)
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int w = layer.w; |
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int h = layer.h; |
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int c = layer.c; |
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pow_cpu(w*h*c*layer.batch, -layer.beta, layer.norms, 1, state.delta, 1); |
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mul_cpu(w*h*c*layer.batch, layer.delta, 1, state.delta, 1); |
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} |
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#ifdef GPU |
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void forward_normalization_layer_gpu(const layer layer, network_state state) |
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{ |
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int k,b; |
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int w = layer.w; |
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int h = layer.h; |
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int c = layer.c; |
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scal_ongpu(w*h*c*layer.batch, 0, layer.squared_gpu, 1); |
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for(b = 0; b < layer.batch; ++b){ |
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float *squared = layer.squared_gpu + w*h*c*b; |
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float *norms = layer.norms_gpu + w*h*c*b; |
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float *input = state.input + w*h*c*b; |
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pow_ongpu(w*h*c, 2, input, 1, squared, 1); |
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const_ongpu(w*h, layer.kappa, norms, 1); |
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for(k = 0; k < layer.size/2; ++k){ |
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axpy_ongpu(w*h, layer.alpha, squared + w*h*k, 1, norms, 1); |
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} |
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for(k = 1; k < layer.c; ++k){ |
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copy_ongpu(w*h, norms + w*h*(k-1), 1, norms + w*h*k, 1); |
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int prev = k - ((layer.size-1)/2) - 1; |
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int next = k + (layer.size/2); |
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if(prev >= 0) axpy_ongpu(w*h, -layer.alpha, squared + w*h*prev, 1, norms + w*h*k, 1); |
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if(next < layer.c) axpy_ongpu(w*h, layer.alpha, squared + w*h*next, 1, norms + w*h*k, 1); |
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} |
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} |
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pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, layer.output_gpu, 1); |
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mul_ongpu(w*h*c*layer.batch, state.input, 1, layer.output_gpu, 1); |
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} |
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void backward_normalization_layer_gpu(const layer layer, network_state state) |
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{ |
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// TODO This is approximate ;-)
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int w = layer.w; |
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int h = layer.h; |
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int c = layer.c; |
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pow_ongpu(w*h*c*layer.batch, -layer.beta, layer.norms_gpu, 1, state.delta, 1); |
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mul_ongpu(w*h*c*layer.batch, layer.delta_gpu, 1, state.delta, 1); |
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} |
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#endif |
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@ -0,0 +1,19 @@ |
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#ifndef NORMALIZATION_LAYER_H |
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#define NORMALIZATION_LAYER_H |
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#include "image.h" |
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#include "layer.h" |
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#include "params.h" |
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layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa); |
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void resize_normalization_layer(layer *layer, int h, int w); |
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void forward_normalization_layer(const layer layer, network_state state); |
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void backward_normalization_layer(const layer layer, network_state state); |
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void visualize_normalization_layer(layer layer, char *window); |
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#ifdef GPU |
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void forward_normalization_layer_gpu(const layer layer, network_state state); |
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void backward_normalization_layer_gpu(const layer layer, network_state state); |
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#endif |
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#endif |
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