yolo working w/ regions

Joseph Redmon

2015-09-16 c53e03348c65462bcba33f6352087dd6b9268e8f

 src/old.c

@@ -1,3 +1,254 @@
void save_network(network net, char *filename)
{
    FILE *fp = fopen(filename, "w");
    if(!fp) file_error(filename);
    int i;
    for(i = 0; i < net.n; ++i)
    {
        if(net.types[i] == CONVOLUTIONAL)
            print_convolutional_cfg(fp, (convolutional_layer *)net.layers[i], net, i);
        else if(net.types[i] == DECONVOLUTIONAL)
            print_deconvolutional_cfg(fp, (deconvolutional_layer *)net.layers[i], net, i);
        else if(net.types[i] == CONNECTED)
            print_connected_cfg(fp, (connected_layer *)net.layers[i], net, i);
        else if(net.types[i] == CROP)
            print_crop_cfg(fp, (crop_layer *)net.layers[i], net, i);
        else if(net.types[i] == MAXPOOL)
            print_maxpool_cfg(fp, (maxpool_layer *)net.layers[i], net, i);
        else if(net.types[i] == DROPOUT)
            print_dropout_cfg(fp, (dropout_layer *)net.layers[i], net, i);
        else if(net.types[i] == SOFTMAX)
            print_softmax_cfg(fp, (softmax_layer *)net.layers[i], net, i);
        else if(net.types[i] == DETECTION)
            print_detection_cfg(fp, (detection_layer *)net.layers[i], net, i);
        else if(net.types[i] == COST)
            print_cost_cfg(fp, (cost_layer *)net.layers[i], net, i);
    }
    fclose(fp);
}

void print_convolutional_cfg(FILE *fp, convolutional_layer *l, network net, int count)
{
#ifdef GPU
    if(gpu_index >= 0)  pull_convolutional_layer(*l);
#endif
    int i;
    fprintf(fp, "[convolutional]\n");
    fprintf(fp, "filters=%d\n"
            "size=%d\n"
            "stride=%d\n"
            "pad=%d\n"
            "activation=%s\n",
            l->n, l->size, l->stride, l->pad,
            get_activation_string(l->activation));
    fprintf(fp, "biases=");
    for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
    fprintf(fp, "\n");
    fprintf(fp, "weights=");
    for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
    fprintf(fp, "\n\n");
}

void print_deconvolutional_cfg(FILE *fp, deconvolutional_layer *l, network net, int count)
{
#ifdef GPU
    if(gpu_index >= 0)  pull_deconvolutional_layer(*l);
#endif
    int i;
    fprintf(fp, "[deconvolutional]\n");
    fprintf(fp, "filters=%d\n"
            "size=%d\n"
            "stride=%d\n"
            "activation=%s\n",
            l->n, l->size, l->stride,
            get_activation_string(l->activation));
    fprintf(fp, "biases=");
    for(i = 0; i < l->n; ++i) fprintf(fp, "%g,", l->biases[i]);
    fprintf(fp, "\n");
    fprintf(fp, "weights=");
    for(i = 0; i < l->n*l->c*l->size*l->size; ++i) fprintf(fp, "%g,", l->filters[i]);
    fprintf(fp, "\n\n");
}

void print_dropout_cfg(FILE *fp, dropout_layer *l, network net, int count)
{
    fprintf(fp, "[dropout]\n");
    fprintf(fp, "probability=%g\n\n", l->probability);
}

void print_connected_cfg(FILE *fp, connected_layer *l, network net, int count)
{
#ifdef GPU
    if(gpu_index >= 0) pull_connected_layer(*l);
#endif
    int i;
    fprintf(fp, "[connected]\n");
    fprintf(fp, "output=%d\n"
            "activation=%s\n",
            l->outputs,
            get_activation_string(l->activation));
    fprintf(fp, "biases=");
    for(i = 0; i < l->outputs; ++i) fprintf(fp, "%g,", l->biases[i]);
    fprintf(fp, "\n");
    fprintf(fp, "weights=");
    for(i = 0; i < l->outputs*l->inputs; ++i) fprintf(fp, "%g,", l->weights[i]);
    fprintf(fp, "\n\n");
}

void print_crop_cfg(FILE *fp, crop_layer *l, network net, int count)
{
    fprintf(fp, "[crop]\n");
    fprintf(fp, "crop_height=%d\ncrop_width=%d\nflip=%d\n\n", l->crop_height, l->crop_width, l->flip);
}

void print_maxpool_cfg(FILE *fp, maxpool_layer *l, network net, int count)
{
    fprintf(fp, "[maxpool]\n");
    fprintf(fp, "size=%d\nstride=%d\n\n", l->size, l->stride);
}

void print_softmax_cfg(FILE *fp, softmax_layer *l, network net, int count)
{
    fprintf(fp, "[softmax]\n");
    fprintf(fp, "\n");
}

void print_detection_cfg(FILE *fp, detection_layer *l, network net, int count)
{
    fprintf(fp, "[detection]\n");
    fprintf(fp, "classes=%d\ncoords=%d\nrescore=%d\nnuisance=%d\n", l->classes, l->coords, l->rescore, l->nuisance);
    fprintf(fp, "\n");
}

void print_cost_cfg(FILE *fp, cost_layer *l, network net, int count)
{
    fprintf(fp, "[cost]\ntype=%s\n", get_cost_string(l->type));
    fprintf(fp, "\n");
}


#ifndef NORMALIZATION_LAYER_H
#define NORMALIZATION_LAYER_H

#include "image.h"
#include "params.h"

typedef struct {
    int batch;
    int h,w,c;
    int size;
    float alpha;
    float beta;
    float kappa;
    float *delta;
    float *output;
    float *sums;
} normalization_layer;

image get_normalization_image(normalization_layer layer);
normalization_layer *make_normalization_layer(int batch, int h, int w, int c, int size, float alpha, float beta, float kappa);
void resize_normalization_layer(normalization_layer *layer, int h, int w);
void forward_normalization_layer(const normalization_layer layer, network_state state);
void backward_normalization_layer(const normalization_layer layer, network_state state);
void visualize_normalization_layer(normalization_layer layer, char *window);

#endif
#include "normalization_layer.h"
#include <stdio.h>

image get_normalization_image(normalization_layer layer)
{
    int h = layer.h;
    int w = layer.w;
    int c = layer.c;
    return float_to_image(w,h,c,layer.output);
}

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);
}

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));
    return layer;
}

void resize_normalization_layer(normalization_layer *layer, int h, int w)
{
    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));
}

void add_square_array(float *src, float *dest, int n)
{
    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];
    }
}

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);
        }
    }
}

void backward_normalization_layer(const normalization_layer layer, network_state state)
{
    // TODO!
    // OR NOT TODO!!
}

void visualize_normalization_layer(normalization_layer layer, char *window)
{
    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);
}

void test_load()
{