void test_box()
{
test_dintersect();
test_dunion();
box a = {0, 0, 1, 1};
box dxa= {0+.00001, 0, 1, 1};
box dya= {0, 0+.00001, 1, 1};
box dwa= {0, 0, 1+.00001, 1};
box dha= {0, 0, 1, 1+.00001};
box b = {.5, 0, .2, .2};
float iou = box_iou(a,b);
iou = (1-iou)*(1-iou);
printf("%f\n", iou);
dbox d = diou(a, b);
printf("%f %f %f %f\n", d.dx, d.dy, d.dw, d.dh);
float xiou = box_iou(dxa, b);
float yiou = box_iou(dya, b);
float wiou = box_iou(dwa, b);
float hiou = box_iou(dha, b);
xiou = ((1-xiou)*(1-xiou) - iou)/(.00001);
yiou = ((1-yiou)*(1-yiou) - iou)/(.00001);
wiou = ((1-wiou)*(1-wiou) - iou)/(.00001);
hiou = ((1-hiou)*(1-hiou) - iou)/(.00001);
printf("manual %f %f %f %f\n", xiou, yiou, wiou, hiou);
}
void forward_detection_layer(const detection_layer l, network_state state)
{
int in_i = 0;
int out_i = 0;
int locations = get_detection_layer_locations(l);
int i,j;
for(i = 0; i < l.batch*locations; ++i){
int mask = (!state.truth || state.truth[out_i + (l.background || l.objectness) + l.classes + 2]);
float scale = 1;
if(l.joint) scale = state.input[in_i++];
else if(l.objectness){
l.output[out_i++] = 1-state.input[in_i++];
scale = mask;
}
else if(l.background) l.output[out_i++] = scale*state.input[in_i++];
for(j = 0; j < l.classes; ++j){
l.output[out_i++] = scale*state.input[in_i++];
}
if(l.objectness){
}else if(l.background){
softmax_array(l.output + out_i - l.classes-l.background, l.classes+l.background, l.output + out_i - l.classes-l.background);
activate_array(state.input+in_i, l.coords, LOGISTIC);
}
for(j = 0; j < l.coords; ++j){
l.output[out_i++] = mask*state.input[in_i++];
}
}
float avg_iou = 0;
int count = 0;
if(l.does_cost && state.train){
*(l.cost) = 0;
int size = get_detection_layer_output_size(l) * l.batch;
memset(l.delta, 0, size * sizeof(float));
for (i = 0; i < l.batch*locations; ++i) {
int classes = l.objectness+l.classes;
int offset = i*(classes+l.coords);
for (j = offset; j < offset+classes; ++j) {
*(l.cost) += pow(state.truth[j] - l.output[j], 2);
l.delta[j] = state.truth[j] - l.output[j];
}
box truth;
truth.x = state.truth[j+0]/7;
truth.y = state.truth[j+1]/7;
truth.w = pow(state.truth[j+2], 2);
truth.h = pow(state.truth[j+3], 2);
box out;
out.x = l.output[j+0]/7;
out.y = l.output[j+1]/7;
out.w = pow(l.output[j+2], 2);
out.h = pow(l.output[j+3], 2);
if(!(truth.w*truth.h)) continue;
float iou = box_iou(out, truth);
avg_iou += iou;
++count;
dbox delta = diou(out, truth);
l.delta[j+0] = 10 * delta.dx/7;
l.delta[j+1] = 10 * delta.dy/7;
l.delta[j+2] = 10 * delta.dw * 2 * sqrt(out.w);
l.delta[j+3] = 10 * delta.dh * 2 * sqrt(out.h);
*(l.cost) += pow((1-iou), 2);
l.delta[j+0] = 4 * (state.truth[j+0] - l.output[j+0]);
l.delta[j+1] = 4 * (state.truth[j+1] - l.output[j+1]);
l.delta[j+2] = 4 * (state.truth[j+2] - l.output[j+2]);
l.delta[j+3] = 4 * (state.truth[j+3] - l.output[j+3]);
if(l.rescore){
for (j = offset; j < offset+classes; ++j) {
if(state.truth[j]) state.truth[j] = iou;
l.delta[j] = state.truth[j] - l.output[j];
}
}
}
printf("Avg IOU: %f\n", avg_iou/count);
}
}
