From 22bf10984cb7940e84db4f086ecbc25d9d5d64b5 Mon Sep 17 00:00:00 2001
From: Alexey <AlexeyAB@users.noreply.github.com>
Date: Wed, 17 Jan 2018 23:19:34 +0000
Subject: [PATCH] .circleci/config.yml added make clean
---
src/normalization_layer.c | 196 +++++++++++++++++++++++++++++++-----------------
1 files changed, 125 insertions(+), 71 deletions(-)
diff --git a/src/normalization_layer.c b/src/normalization_layer.c
index 93c2ad9..069a079 100644
--- a/src/normalization_layer.c
+++ b/src/normalization_layer.c
@@ -1,96 +1,150 @@
#include "normalization_layer.h"
+#include "blas.h"
#include <stdio.h>
-image get_normalization_image(normalization_layer layer)
+layer make_normalization_layer(int batch, int w, int h, int c, int size, float alpha, float beta, float kappa)
{
- int h = layer.h;
- int w = layer.w;
- int c = layer.c;
- return float_to_image(w,h,c,layer.output);
-}
+ 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;
-image get_normalization_delta(normalization_layer layer)
-{
- int h = layer.h;
- int w = layer.w;
- int c = layer.c;
- return float_to_image(w,h,c,layer.delta);
-}
+ layer.forward = forward_normalization_layer;
+ layer.backward = backward_normalization_layer;
+ #ifdef GPU
+ layer.forward_gpu = forward_normalization_layer_gpu;
+ layer.backward_gpu = backward_normalization_layer_gpu;
-normalization_layer *make_normalization_layer(int batch, int h, int w, 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", h,w,c,size);
- normalization_layer *layer = calloc(1, sizeof(normalization_layer));
- layer->batch = batch;
- layer->h = h;
- layer->w = w;
- layer->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->sums = calloc(h*w, sizeof(float));
+ layer.output_gpu = cuda_make_array(layer.output, h * w * c * batch);
+ layer.delta_gpu = cuda_make_array(layer.delta, h * w * c * batch);
+ layer.squared_gpu = cuda_make_array(layer.squared, h * w * c * batch);
+ layer.norms_gpu = cuda_make_array(layer.norms, h * w * c * batch);
+ #endif
return layer;
}
-void resize_normalization_layer(normalization_layer *layer, int h, int w)
+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->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->sums = realloc(layer->sums, h*w * sizeof(float));
+ layer->out_h = h;
+ layer->out_w = w;
+ layer->inputs = w*h*c;
+ layer->outputs = layer->inputs;
+ layer->output = realloc(layer->output, h * w * c * batch * sizeof(float));
+ layer->delta = realloc(layer->delta, h * w * c * batch * sizeof(float));
+ layer->squared = realloc(layer->squared, h * w * c * batch * sizeof(float));
+ layer->norms = realloc(layer->norms, h * w * c * 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(layer->output, h * w * c * batch);
+ layer->delta_gpu = cuda_make_array(layer->delta, h * w * c * batch);
+ layer->squared_gpu = cuda_make_array(layer->squared, h * w * c * batch);
+ layer->norms_gpu = cuda_make_array(layer->norms, h * w * c * batch);
+#endif
}
-void add_square_array(float *src, float *dest, int n)
+void forward_normalization_layer(const layer layer, network_state state)
{
- int i;
- for(i = 0; i < n; ++i){
- dest[i] += src[i]*src[i];
- }
-}
-void sub_square_array(float *src, float *dest, int n)
-{
- int i;
- for(i = 0; i < n; ++i){
- dest[i] -= src[i]*src[i];
- }
-}
+ 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);
-void forward_normalization_layer(const normalization_layer layer, network_state state)
-{
- int i,j,k;
- memset(layer.sums, 0, layer.h*layer.w*sizeof(float));
- int imsize = layer.h*layer.w;
- for(j = 0; j < layer.size/2; ++j){
- if(j < layer.c) add_square_array(state.input+j*imsize, layer.sums, imsize);
- }
- for(k = 0; k < layer.c; ++k){
- int next = k+layer.size/2;
- int prev = k-layer.size/2-1;
- if(next < layer.c) add_square_array(state.input+next*imsize, layer.sums, imsize);
- if(prev > 0) sub_square_array(state.input+prev*imsize, layer.sums, imsize);
- for(i = 0; i < imsize; ++i){
- layer.output[k*imsize + i] = state.input[k*imsize+i] / pow(layer.kappa + layer.alpha * layer.sums[i], layer.beta);
+ 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 normalization_layer layer, network_state state)
+void backward_normalization_layer(const layer layer, network_state state)
{
- // TODO!
- // OR NOT TODO!!
+ // TODO This is approximate ;-)
+ // Also this should add in to delta instead of overwritting.
+
+ 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);
}
-void visualize_normalization_layer(normalization_layer layer, char *window)
+#ifdef GPU
+void forward_normalization_layer_gpu(const layer layer, network_state state)
{
- image delta = get_normalization_image(layer);
- image dc = collapse_image_layers(delta, 1);
- char buff[256];
- sprintf(buff, "%s: Output", window);
- show_image(dc, buff);
- save_image(dc, buff);
- free_image(dc);
+ 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
--
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