| | |
| | | } |
| | | avg_anyobj += l.output[index + 4]; |
| | | l.delta[index + 4] = l.noobject_scale * ((0 - l.output[index + 4]) * logistic_gradient(l.output[index + 4])); |
| | | if(best_iou > .5) l.delta[index + 4] = 0; |
| | | if(best_iou > l.thresh) l.delta[index + 4] = 0; |
| | | |
| | | if(*(state.net.seen) < 12800){ |
| | | box truth = {0}; |
| | |
| | | box truth_shift = truth; |
| | | truth_shift.x = 0; |
| | | truth_shift.y = 0; |
| | | printf("index %d %d\n",i, j); |
| | | //printf("index %d %d\n",i, j); |
| | | for(n = 0; n < l.n; ++n){ |
| | | int index = size*(j*l.w*l.n + i*l.n + n) + b*l.outputs; |
| | | box pred = get_region_box(l.output, l.biases, n, index, i, j, l.w, l.h); |
| | |
| | | pred.w = l.biases[2*n]; |
| | | pred.h = l.biases[2*n+1]; |
| | | } |
| | | printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h); |
| | | //printf("pred: (%f, %f) %f x %f\n", pred.x, pred.y, pred.w, pred.h); |
| | | pred.x = 0; |
| | | pred.y = 0; |
| | | float iou = box_iou(pred, truth_shift); |
| | |
| | | best_n = n; |
| | | } |
| | | } |
| | | printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h); |
| | | //printf("%d %f (%f, %f) %f x %f\n", best_n, best_iou, truth.x, truth.y, truth.w, truth.h); |
| | | |
| | | float iou = delta_region_box(truth, l.output, l.biases, best_n, best_index, i, j, l.w, l.h, l.delta, l.coord_scale); |
| | | if(iou > .5) recall += 1; |
| | |
| | | ++count; |
| | | } |
| | | } |
| | | printf("\n"); |
| | | //printf("\n"); |
| | | reorg(l.delta, l.w*l.h, size*l.n, l.batch, 0); |
| | | *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); |
| | | printf("Region Avg IOU: %f, Class: %f, Obj: %f, No Obj: %f, Avg Recall: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.w*l.h*l.n*l.batch), recall/count, count); |