#include "detection_layer.h"
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#include "activations.h"
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#include "softmax_layer.h"
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#include "blas.h"
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#include "cuda.h"
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#include "utils.h"
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#include <stdio.h>
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#include <string.h>
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#include <stdlib.h>
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int get_detection_layer_locations(detection_layer layer)
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{
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return layer.inputs / (layer.classes+layer.coords+layer.rescore+layer.background);
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}
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int get_detection_layer_output_size(detection_layer layer)
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{
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return get_detection_layer_locations(layer)*(layer.background + layer.classes + layer.coords);
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}
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detection_layer *make_detection_layer(int batch, int inputs, int classes, int coords, int rescore, int background, int nuisance)
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{
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detection_layer *layer = calloc(1, sizeof(detection_layer));
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layer->batch = batch;
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layer->inputs = inputs;
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layer->classes = classes;
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layer->coords = coords;
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layer->rescore = rescore;
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layer->nuisance = nuisance;
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layer->cost = calloc(1, sizeof(float));
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layer->does_cost=1;
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layer->background = background;
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int outputs = get_detection_layer_output_size(*layer);
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layer->output = calloc(batch*outputs, sizeof(float));
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layer->delta = calloc(batch*outputs, sizeof(float));
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#ifdef GPU
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layer->output_gpu = cuda_make_array(0, batch*outputs);
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layer->delta_gpu = cuda_make_array(0, batch*outputs);
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#endif
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fprintf(stderr, "Detection Layer\n");
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srand(0);
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return layer;
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}
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void dark_zone(detection_layer layer, int class, int start, network_state state)
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{
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int index = start+layer.background+class;
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int size = layer.classes+layer.coords+layer.background;
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int location = (index%(7*7*size)) / size ;
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int r = location / 7;
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int c = location % 7;
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int dr, dc;
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for(dr = -1; dr <= 1; ++dr){
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for(dc = -1; dc <= 1; ++dc){
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if(!(dr || dc)) continue;
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if((r + dr) > 6 || (r + dr) < 0) continue;
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if((c + dc) > 6 || (c + dc) < 0) continue;
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int di = (dr*7 + dc) * size;
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if(state.truth[index+di]) continue;
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layer.output[index + di] = 0;
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//if(!state.truth[start+di]) continue;
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//layer.output[start + di] = 1;
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}
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}
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}
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typedef struct{
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float dx, dy, dw, dh;
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} dbox;
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dbox derivative(box a, box b)
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{
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dbox d;
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d.dx = 0;
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d.dw = 0;
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float l1 = a.x - a.w/2;
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float l2 = b.x - b.w/2;
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if (l1 > l2){
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d.dx -= 1;
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d.dw += .5;
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}
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float r1 = a.x + a.w/2;
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float r2 = b.x + b.w/2;
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if(r1 < r2){
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d.dx += 1;
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d.dw += .5;
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}
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if (l1 > r2) {
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d.dx = -1;
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d.dw = 0;
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}
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if (r1 < l2){
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d.dx = 1;
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d.dw = 0;
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}
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d.dy = 0;
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d.dh = 0;
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float t1 = a.y - a.h/2;
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float t2 = b.y - b.h/2;
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if (t1 > t2){
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d.dy -= 1;
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d.dh += .5;
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}
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float b1 = a.y + a.h/2;
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float b2 = b.y + b.h/2;
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if(b1 < b2){
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d.dy += 1;
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d.dh += .5;
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}
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if (t1 > b2) {
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d.dy = -1;
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d.dh = 0;
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}
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if (b1 < t2){
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d.dy = 1;
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d.dh = 0;
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}
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return d;
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}
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float overlap(float x1, float w1, float x2, float w2)
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{
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float l1 = x1 - w1/2;
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float l2 = x2 - w2/2;
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float left = l1 > l2 ? l1 : l2;
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float r1 = x1 + w1/2;
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float r2 = x2 + w2/2;
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float right = r1 < r2 ? r1 : r2;
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return right - left;
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}
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float box_intersection(box a, box b)
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{
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float w = overlap(a.x, a.w, b.x, b.w);
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float h = overlap(a.y, a.h, b.y, b.h);
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if(w < 0 || h < 0) return 0;
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float area = w*h;
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return area;
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}
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float box_union(box a, box b)
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{
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float i = box_intersection(a, b);
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float u = a.w*a.h + b.w*b.h - i;
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return u;
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}
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float box_iou(box a, box b)
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{
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return box_intersection(a, b)/box_union(a, b);
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}
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dbox dintersect(box a, box b)
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{
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float w = overlap(a.x, a.w, b.x, b.w);
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float h = overlap(a.y, a.h, b.y, b.h);
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dbox dover = derivative(a, b);
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dbox di;
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di.dw = dover.dw*h;
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di.dx = dover.dx*h;
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di.dh = dover.dh*w;
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di.dy = dover.dy*w;
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if(h < 0 || w < 0){
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di.dx = dover.dx;
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di.dy = dover.dy;
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}
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return di;
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}
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dbox dunion(box a, box b)
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{
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dbox du = {0,0,0,0};;
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float w = overlap(a.x, a.w, b.x, b.w);
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float h = overlap(a.y, a.h, b.y, b.h);
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if(w > 0 && h > 0){
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dbox di = dintersect(a, b);
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du.dw = h - di.dw;
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du.dh = w - di.dw;
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du.dx = -di.dx;
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du.dy = -di.dy;
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}
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return du;
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}
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dbox diou(box a, box b)
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{
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float u = box_union(a,b);
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float i = box_intersection(a,b);
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dbox di = dintersect(a,b);
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dbox du = dunion(a,b);
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dbox dd = {0,0,0,0};
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if(i < 0) {
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dd.dx = b.x - a.x;
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dd.dy = b.y - a.y;
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dd.dw = b.w - a.w;
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dd.dh = b.h - a.h;
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return dd;
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}
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dd.dx = 2*pow((1-(i/u)),1)*(di.dx*u - du.dx*i)/(u*u);
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dd.dy = 2*pow((1-(i/u)),1)*(di.dy*u - du.dy*i)/(u*u);
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dd.dw = 2*pow((1-(i/u)),1)*(di.dw*u - du.dw*i)/(u*u);
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dd.dh = 2*pow((1-(i/u)),1)*(di.dh*u - du.dh*i)/(u*u);
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return dd;
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}
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void test_box()
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{
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box a = {1, 1, 1, 1};
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box b = {0, 0, .5, .2};
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int count = 0;
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while(count++ < 300){
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dbox d = diou(a, b);
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printf("%f %f %f %f\n", a.x, a.y, a.w, a.h);
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a.x += .1*d.dx;
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a.w += .1*d.dw;
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a.y += .1*d.dy;
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a.h += .1*d.dh;
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printf("inter: %f\n", box_intersection(a, b));
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printf("union: %f\n", box_union(a, b));
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printf("IOU: %f\n", box_iou(a, b));
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if(d.dx==0 && d.dw==0 && d.dy==0 && d.dh==0) {
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printf("break!!!\n");
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break;
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}
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}
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}
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void forward_detection_layer(const detection_layer layer, network_state state)
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{
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int in_i = 0;
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int out_i = 0;
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int locations = get_detection_layer_locations(layer);
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int i,j;
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for(i = 0; i < layer.batch*locations; ++i){
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int mask = (!state.truth || state.truth[out_i + layer.background + layer.classes + 2]);
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float scale = 1;
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if(layer.rescore) scale = state.input[in_i++];
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else if(layer.nuisance){
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layer.output[out_i++] = 1-state.input[in_i++];
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scale = mask;
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}
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else if(layer.background) layer.output[out_i++] = scale*state.input[in_i++];
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for(j = 0; j < layer.classes; ++j){
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layer.output[out_i++] = scale*state.input[in_i++];
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}
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if(layer.nuisance){
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}else if(layer.background){
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softmax_array(layer.output + out_i - layer.classes-layer.background, layer.classes+layer.background, layer.output + out_i - layer.classes-layer.background);
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activate_array(state.input+in_i, layer.coords, LOGISTIC);
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}
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for(j = 0; j < layer.coords; ++j){
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layer.output[out_i++] = mask*state.input[in_i++];
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}
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}
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if(layer.does_cost){
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*(layer.cost) = 0;
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int size = get_detection_layer_output_size(layer) * layer.batch;
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memset(layer.delta, 0, size * sizeof(float));
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for(i = 0; i < layer.batch*locations; ++i){
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int classes = layer.nuisance+layer.classes;
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int offset = i*(classes+layer.coords);
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for(j = offset; j < offset+classes; ++j){
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*(layer.cost) += pow(state.truth[j] - layer.output[j], 2);
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layer.delta[j] = state.truth[j] - layer.output[j];
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}
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box truth;
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truth.x = state.truth[j+0];
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truth.y = state.truth[j+1];
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truth.w = state.truth[j+2];
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truth.h = state.truth[j+3];
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box out;
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out.x = layer.output[j+0];
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out.y = layer.output[j+1];
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out.w = layer.output[j+2];
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out.h = layer.output[j+3];
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if(!(truth.w*truth.h)) continue;
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float iou = box_iou(truth, out);
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//printf("iou: %f\n", iou);
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*(layer.cost) += pow((1-iou), 2);
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dbox d = diou(out, truth);
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layer.delta[j+0] = d.dx;
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layer.delta[j+1] = d.dy;
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layer.delta[j+2] = d.dw;
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layer.delta[j+3] = d.dh;
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}
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}
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/*
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int count = 0;
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for(i = 0; i < layer.batch*locations; ++i){
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for(j = 0; j < layer.classes+layer.background; ++j){
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printf("%f, ", layer.output[count++]);
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}
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printf("\n");
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for(j = 0; j < layer.coords; ++j){
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printf("%f, ", layer.output[count++]);
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}
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printf("\n");
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}
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*/
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/*
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if(layer.background || 1){
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for(i = 0; i < layer.batch*locations; ++i){
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int index = i*(layer.classes+layer.coords+layer.background);
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for(j= 0; j < layer.classes; ++j){
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if(state.truth[index+j+layer.background]){
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//dark_zone(layer, j, index, state);
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}
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}
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}
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}
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*/
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}
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void backward_detection_layer(const detection_layer layer, network_state state)
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{
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int locations = get_detection_layer_locations(layer);
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int i,j;
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int in_i = 0;
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int out_i = 0;
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for(i = 0; i < layer.batch*locations; ++i){
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float scale = 1;
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float latent_delta = 0;
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if(layer.rescore) scale = state.input[in_i++];
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else if (layer.nuisance) state.delta[in_i++] = -layer.delta[out_i++];
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else if (layer.background) state.delta[in_i++] = scale*layer.delta[out_i++];
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for(j = 0; j < layer.classes; ++j){
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latent_delta += state.input[in_i]*layer.delta[out_i];
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state.delta[in_i++] = scale*layer.delta[out_i++];
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}
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if (layer.nuisance) {
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}else if (layer.background) gradient_array(layer.output + out_i, layer.coords, LOGISTIC, layer.delta + out_i);
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for(j = 0; j < layer.coords; ++j){
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state.delta[in_i++] = layer.delta[out_i++];
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}
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if(layer.rescore) state.delta[in_i-layer.coords-layer.classes-layer.rescore-layer.background] = latent_delta;
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}
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}
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#ifdef GPU
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void forward_detection_layer_gpu(const detection_layer layer, network_state state)
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{
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int outputs = get_detection_layer_output_size(layer);
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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float *truth_cpu = 0;
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if(state.truth){
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truth_cpu = calloc(layer.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
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}
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cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
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network_state cpu_state;
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cpu_state.train = state.train;
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cpu_state.truth = truth_cpu;
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cpu_state.input = in_cpu;
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forward_detection_layer(layer, cpu_state);
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cuda_push_array(layer.output_gpu, layer.output, layer.batch*outputs);
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cuda_push_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
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free(cpu_state.input);
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if(cpu_state.truth) free(cpu_state.truth);
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}
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void backward_detection_layer_gpu(detection_layer layer, network_state state)
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{
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int outputs = get_detection_layer_output_size(layer);
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float *in_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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float *delta_cpu = calloc(layer.batch*layer.inputs, sizeof(float));
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float *truth_cpu = 0;
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if(state.truth){
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truth_cpu = calloc(layer.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, layer.batch*outputs);
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}
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network_state cpu_state;
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cpu_state.train = state.train;
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cpu_state.input = in_cpu;
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cpu_state.truth = truth_cpu;
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cpu_state.delta = delta_cpu;
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cuda_pull_array(state.input, in_cpu, layer.batch*layer.inputs);
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cuda_pull_array(layer.delta_gpu, layer.delta, layer.batch*outputs);
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backward_detection_layer(layer, cpu_state);
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cuda_push_array(state.delta, delta_cpu, layer.batch*layer.inputs);
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free(in_cpu);
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free(delta_cpu);
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}
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#endif
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