#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 l)
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{
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return l.inputs / (l.classes+l.coords+l.joint+(l.background || l.objectness));
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}
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int get_detection_layer_output_size(detection_layer l)
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{
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return get_detection_layer_locations(l)*((l.background || l.objectness) + l.classes + l.coords);
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}
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detection_layer make_detection_layer(int batch, int inputs, int classes, int coords, int joint, int rescore, int background, int objectness)
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{
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detection_layer l = {0};
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l.type = DETECTION;
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l.batch = batch;
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l.inputs = inputs;
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l.classes = classes;
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l.coords = coords;
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l.rescore = rescore;
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l.objectness = objectness;
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l.joint = joint;
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l.cost = calloc(1, sizeof(float));
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l.does_cost=1;
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l.background = background;
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int outputs = get_detection_layer_output_size(l);
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l.outputs = outputs;
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l.output = calloc(batch*outputs, sizeof(float));
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l.delta = calloc(batch*outputs, sizeof(float));
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#ifdef GPU
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l.output_gpu = cuda_make_array(0, batch*outputs);
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l.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 l;
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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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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;
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dbox di = dintersect(a, b);
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du.dw = a.h - di.dw;
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du.dh = a.w - di.dh;
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du.dx = -di.dx;
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du.dy = -di.dy;
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return du;
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}
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dbox diou(box a, box b);
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void test_dunion()
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{
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box a = {0, 0, 1, 1};
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box dxa= {0+.0001, 0, 1, 1};
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box dya= {0, 0+.0001, 1, 1};
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box dwa= {0, 0, 1+.0001, 1};
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box dha= {0, 0, 1, 1+.0001};
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box b = {.5, .5, .2, .2};
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dbox di = dunion(a,b);
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printf("Union: %f %f %f %f\n", di.dx, di.dy, di.dw, di.dh);
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float inter = box_union(a, b);
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float xinter = box_union(dxa, b);
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float yinter = box_union(dya, b);
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float winter = box_union(dwa, b);
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float hinter = box_union(dha, b);
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xinter = (xinter - inter)/(.0001);
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yinter = (yinter - inter)/(.0001);
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winter = (winter - inter)/(.0001);
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hinter = (hinter - inter)/(.0001);
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printf("Union Manual %f %f %f %f\n", xinter, yinter, winter, hinter);
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}
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void test_dintersect()
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{
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box a = {0, 0, 1, 1};
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box dxa= {0+.0001, 0, 1, 1};
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box dya= {0, 0+.0001, 1, 1};
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box dwa= {0, 0, 1+.0001, 1};
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box dha= {0, 0, 1, 1+.0001};
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box b = {.5, .5, .2, .2};
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dbox di = dintersect(a,b);
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printf("Inter: %f %f %f %f\n", di.dx, di.dy, di.dw, di.dh);
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float inter = box_intersection(a, b);
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float xinter = box_intersection(dxa, b);
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float yinter = box_intersection(dya, b);
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float winter = box_intersection(dwa, b);
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float hinter = box_intersection(dha, b);
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xinter = (xinter - inter)/(.0001);
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yinter = (yinter - inter)/(.0001);
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winter = (winter - inter)/(.0001);
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hinter = (hinter - inter)/(.0001);
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printf("Inter Manual %f %f %f %f\n", xinter, yinter, winter, hinter);
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}
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void test_box()
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{
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test_dintersect();
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test_dunion();
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box a = {0, 0, 1, 1};
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box dxa= {0+.00001, 0, 1, 1};
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box dya= {0, 0+.00001, 1, 1};
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box dwa= {0, 0, 1+.00001, 1};
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box dha= {0, 0, 1, 1+.00001};
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box b = {.5, 0, .2, .2};
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float iou = box_iou(a,b);
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iou = (1-iou)*(1-iou);
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printf("%f\n", iou);
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dbox d = diou(a, b);
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printf("%f %f %f %f\n", d.dx, d.dy, d.dw, d.dh);
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float xiou = box_iou(dxa, b);
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float yiou = box_iou(dya, b);
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float wiou = box_iou(dwa, b);
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float hiou = box_iou(dha, b);
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xiou = ((1-xiou)*(1-xiou) - iou)/(.00001);
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yiou = ((1-yiou)*(1-yiou) - iou)/(.00001);
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wiou = ((1-wiou)*(1-wiou) - iou)/(.00001);
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hiou = ((1-hiou)*(1-hiou) - iou)/(.00001);
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printf("manual %f %f %f %f\n", xiou, yiou, wiou, hiou);
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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 || 1) {
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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 forward_detection_layer(const detection_layer l, 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(l);
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int i,j;
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for(i = 0; i < l.batch*locations; ++i){
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int mask = (!state.truth || state.truth[out_i + (l.background || l.objectness) + l.classes + 2]);
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float scale = 1;
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if(l.joint) scale = state.input[in_i++];
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else if(l.objectness){
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l.output[out_i++] = 1-state.input[in_i++];
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scale = mask;
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}
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else if(l.background) l.output[out_i++] = scale*state.input[in_i++];
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for(j = 0; j < l.classes; ++j){
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l.output[out_i++] = scale*state.input[in_i++];
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}
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if(l.objectness){
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}else if(l.background){
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softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background);
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activate_array(state.input+in_i, l.coords, LOGISTIC);
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}
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for(j = 0; j < l.coords; ++j){
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l.output[out_i++] = mask*state.input[in_i++];
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}
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}
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float avg_iou = 0;
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int count = 0;
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if(l.does_cost && state.train){
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*(l.cost) = 0;
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int size = get_detection_layer_output_size(l) * l.batch;
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memset(l.delta, 0, size * sizeof(float));
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for (i = 0; i < l.batch*locations; ++i) {
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int classes = l.objectness+l.classes;
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int offset = i*(classes+l.coords);
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for (j = offset; j < offset+classes; ++j) {
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*(l.cost) += pow(state.truth[j] - l.output[j], 2);
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l.delta[j] = state.truth[j] - l.output[j];
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}
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box truth;
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truth.x = state.truth[j+0]/7;
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truth.y = state.truth[j+1]/7;
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truth.w = pow(state.truth[j+2], 2);
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truth.h = pow(state.truth[j+3], 2);
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box out;
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out.x = l.output[j+0]/7;
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out.y = l.output[j+1]/7;
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out.w = pow(l.output[j+2], 2);
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out.h = pow(l.output[j+3], 2);
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if(!(truth.w*truth.h)) continue;
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float iou = box_iou(out, truth);
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avg_iou += iou;
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++count;
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dbox delta = diou(out, truth);
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l.delta[j+0] = 10 * delta.dx/7;
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l.delta[j+1] = 10 * delta.dy/7;
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l.delta[j+2] = 10 * delta.dw * 2 * sqrt(out.w);
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l.delta[j+3] = 10 * delta.dh * 2 * sqrt(out.h);
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*(l.cost) += pow((1-iou), 2);
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l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]);
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l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
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l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
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l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
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if(l.rescore){
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for (j = offset; j < offset+classes; ++j) {
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if(state.truth[j]) state.truth[j] = iou;
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l.delta[j] = state.truth[j] - l.output[j];
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}
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}
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}
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printf("Avg IOU: %f\n", avg_iou/count);
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}
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}
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void backward_detection_layer(const detection_layer l, network_state state)
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{
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int locations = get_detection_layer_locations(l);
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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 < l.batch*locations; ++i){
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float scale = 1;
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float latent_delta = 0;
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if(l.joint) scale = state.input[in_i++];
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else if (l.objectness) state.delta[in_i++] = -l.delta[out_i++];
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else if (l.background) state.delta[in_i++] = scale*l.delta[out_i++];
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for(j = 0; j < l.classes; ++j){
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latent_delta += state.input[in_i]*l.delta[out_i];
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state.delta[in_i++] = scale*l.delta[out_i++];
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}
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if (l.objectness) {
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}else if (l.background) gradient_array(l.output + out_i, l.coords, LOGISTIC, l.delta + out_i);
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for(j = 0; j < l.coords; ++j){
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state.delta[in_i++] = l.delta[out_i++];
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}
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if(l.joint) state.delta[in_i-l.coords-l.classes-l.joint] = 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 l, network_state state)
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{
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int outputs = get_detection_layer_output_size(l);
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float *in_cpu = calloc(l.batch*l.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(l.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, l.batch*outputs);
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}
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cuda_pull_array(state.input, in_cpu, l.batch*l.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(l, cpu_state);
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cuda_push_array(l.output_gpu, l.output, l.batch*outputs);
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cuda_push_array(l.delta_gpu, l.delta, l.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 l, network_state state)
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{
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int outputs = get_detection_layer_output_size(l);
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float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
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float *delta_cpu = calloc(l.batch*l.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(l.batch*outputs, sizeof(float));
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cuda_pull_array(state.truth, truth_cpu, l.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, l.batch*l.inputs);
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cuda_pull_array(l.delta_gpu, l.delta, l.batch*outputs);
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backward_detection_layer(l, cpu_state);
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cuda_push_array(state.delta, delta_cpu, l.batch*l.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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