| | |
| | | int locations = l.side*l.side; |
| | | int i,j; |
| | | memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); |
| | | for(i = 0; i < l.batch*locations; ++i){ |
| | | int index = i*((1+l.coords)*l.n + l.classes); |
| | | if(l.softmax){ |
| | | activate_array(l.output + index, l.n*(1+l.coords), LOGISTIC); |
| | | int offset = l.n*(1+l.coords); |
| | | softmax_array(l.output + index + offset, l.classes, |
| | | l.output + index + offset); |
| | | int b; |
| | | if (l.softmax){ |
| | | for(b = 0; b < l.batch; ++b){ |
| | | int index = b*l.inputs; |
| | | for (i = 0; i < locations; ++i) { |
| | | int offset = i*l.classes; |
| | | softmax_array(l.output + index + offset, l.classes, |
| | | l.output + index + offset); |
| | | } |
| | | int offset = locations*l.classes; |
| | | activate_array(l.output + index + offset, locations*l.n*(1+l.coords), LOGISTIC); |
| | | } |
| | | } |
| | | |
| | | if(state.train){ |
| | | float avg_iou = 0; |
| | | float avg_cat = 0; |
| | |
| | | *(l.cost) = 0; |
| | | int size = l.inputs * l.batch; |
| | | memset(l.delta, 0, size * sizeof(float)); |
| | | for (i = 0; i < l.batch*locations; ++i) { |
| | | int index = i*((1+l.coords)*l.n + l.classes); |
| | | for(j = 0; j < l.n; ++j){ |
| | | int prob_index = index + j*(1 + l.coords); |
| | | l.delta[prob_index] = (1./l.n)*(0-l.output[prob_index]); |
| | | if(l.softmax){ |
| | | l.delta[prob_index] = 1./(l.n*l.side)*(0-l.output[prob_index]); |
| | | } |
| | | *(l.cost) += (1./l.n)*pow(l.output[prob_index], 2); |
| | | //printf("%f\n", l.output[prob_index]); |
| | | avg_anyobj += l.output[prob_index]; |
| | | } |
| | | |
| | | int truth_index = i*(1 + l.coords + l.classes); |
| | | int best_index = -1; |
| | | float best_iou = 0; |
| | | float best_rmse = 4; |
| | | |
| | | int bg = !state.truth[truth_index]; |
| | | if(bg) { |
| | | continue; |
| | | } |
| | | |
| | | int class_index = index + l.n*(1+l.coords); |
| | | for(j = 0; j < l.classes; ++j) { |
| | | l.delta[class_index+j] = state.truth[truth_index+1+j] - l.output[class_index+j]; |
| | | *(l.cost) += pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); |
| | | if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; |
| | | } |
| | | truth_index += l.classes + 1; |
| | | box truth = {state.truth[truth_index+0], state.truth[truth_index+1], state.truth[truth_index+2], state.truth[truth_index+3]}; |
| | | truth.x /= l.side; |
| | | truth.y /= l.side; |
| | | |
| | | for(j = 0; j < l.n; ++j){ |
| | | int out_index = index + j*(1+l.coords); |
| | | box out = {l.output[out_index+1], l.output[out_index+2], l.output[out_index+3], l.output[out_index+4]}; |
| | | |
| | | out.x /= l.side; |
| | | out.y /= l.side; |
| | | if (l.sqrt){ |
| | | out.w = out.w*out.w; |
| | | out.h = out.h*out.h; |
| | | for (b = 0; b < l.batch; ++b){ |
| | | int index = b*l.inputs; |
| | | for (i = 0; i < locations; ++i) { |
| | | int truth_index = (b*locations + i)*(1+l.coords+l.classes); |
| | | int is_obj = state.truth[truth_index]; |
| | | for (j = 0; j < l.n; ++j) { |
| | | int p_index = index + locations*l.classes + i*l.n + j; |
| | | l.delta[p_index] = l.noobject_scale*(0 - l.output[p_index]); |
| | | *(l.cost) += l.noobject_scale*pow(l.output[p_index], 2); |
| | | avg_anyobj += l.output[p_index]; |
| | | } |
| | | |
| | | float iou = box_iou(out, truth); |
| | | float rmse = box_rmse(out, truth); |
| | | if(best_iou > 0 || iou > 0){ |
| | | if(iou > best_iou){ |
| | | best_iou = iou; |
| | | best_index = j; |
| | | int best_index = -1; |
| | | float best_iou = 0; |
| | | float best_rmse = 4; |
| | | |
| | | if (!is_obj) continue; |
| | | |
| | | int class_index = index + i*l.classes; |
| | | for(j = 0; j < l.classes; ++j) { |
| | | l.delta[class_index+j] = l.class_scale * (state.truth[truth_index+1+j] - l.output[class_index+j]); |
| | | *(l.cost) += l.class_scale * pow(state.truth[truth_index+1+j] - l.output[class_index+j], 2); |
| | | if(state.truth[truth_index + 1 + j]) avg_cat += l.output[class_index+j]; |
| | | } |
| | | |
| | | box truth = float_to_box(state.truth + truth_index + 1 + l.classes); |
| | | truth.x /= l.side; |
| | | truth.y /= l.side; |
| | | |
| | | for(j = 0; j < l.n; ++j){ |
| | | int box_index = index + locations*(l.classes + l.n) + (i*l.n + j) * l.coords; |
| | | box out = float_to_box(l.output + box_index); |
| | | out.x /= l.side; |
| | | out.y /= l.side; |
| | | |
| | | if (l.sqrt){ |
| | | out.w = out.w*out.w; |
| | | out.h = out.h*out.h; |
| | | } |
| | | }else{ |
| | | if(rmse < best_rmse){ |
| | | best_rmse = rmse; |
| | | best_index = j; |
| | | |
| | | float iou = box_iou(out, truth); |
| | | float rmse = box_rmse(out, truth); |
| | | if(best_iou > 0 || iou > 0){ |
| | | if(iou > best_iou){ |
| | | best_iou = iou; |
| | | best_index = j; |
| | | } |
| | | }else{ |
| | | if(rmse < best_rmse){ |
| | | best_rmse = rmse; |
| | | best_index = j; |
| | | } |
| | | } |
| | | } |
| | | } |
| | | //printf("%d", best_index); |
| | | int in_index = index + best_index*(1+l.coords); |
| | | *(l.cost) -= pow(l.output[in_index], 2); |
| | | *(l.cost) += pow(1-l.output[in_index], 2); |
| | | avg_obj += l.output[in_index]; |
| | | l.delta[in_index+0] = (1.-l.output[in_index]); |
| | | if(l.softmax){ |
| | | l.delta[in_index+0] = 5*(1.-l.output[in_index]); |
| | | } |
| | | //printf("%f\n", l.output[in_index]); |
| | | int p_index = index + locations*l.classes + i*l.n + best_index; |
| | | *(l.cost) -= l.noobject_scale * pow(l.output[p_index], 2); |
| | | *(l.cost) += l.object_scale * pow(1-l.output[p_index], 2); |
| | | avg_obj += l.output[p_index]; |
| | | l.delta[p_index+0] = l.object_scale * (1.-l.output[p_index]); |
| | | |
| | | l.delta[in_index+1] = 5*(state.truth[truth_index+0] - l.output[in_index+1]); |
| | | l.delta[in_index+2] = 5*(state.truth[truth_index+1] - l.output[in_index+2]); |
| | | if(l.sqrt){ |
| | | l.delta[in_index+3] = 5*(sqrt(state.truth[truth_index+2]) - l.output[in_index+3]); |
| | | l.delta[in_index+4] = 5*(sqrt(state.truth[truth_index+3]) - l.output[in_index+4]); |
| | | }else{ |
| | | l.delta[in_index+3] = 5*(state.truth[truth_index+2] - l.output[in_index+3]); |
| | | l.delta[in_index+4] = 5*(state.truth[truth_index+3] - l.output[in_index+4]); |
| | | } |
| | | if(l.rescore){ |
| | | l.delta[p_index+0] = l.object_scale * (best_iou - l.output[p_index]); |
| | | } |
| | | |
| | | *(l.cost) += pow(1-best_iou, 2); |
| | | avg_iou += best_iou; |
| | | ++count; |
| | | int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; |
| | | int tbox_index = truth_index + 1 + l.classes; |
| | | l.delta[box_index+0] = l.coord_scale*(state.truth[tbox_index + 0] - l.output[box_index + 0]); |
| | | l.delta[box_index+1] = l.coord_scale*(state.truth[tbox_index + 1] - l.output[box_index + 1]); |
| | | l.delta[box_index+2] = l.coord_scale*(state.truth[tbox_index + 2] - l.output[box_index + 2]); |
| | | l.delta[box_index+3] = l.coord_scale*(state.truth[tbox_index + 3] - l.output[box_index + 3]); |
| | | if(l.sqrt){ |
| | | l.delta[box_index+2] = l.coord_scale*(sqrt(state.truth[tbox_index + 2]) - l.output[box_index + 2]); |
| | | l.delta[box_index+3] = l.coord_scale*(sqrt(state.truth[tbox_index + 3]) - l.output[box_index + 3]); |
| | | } |
| | | |
| | | *(l.cost) += pow(1-best_iou, 2); |
| | | avg_iou += best_iou; |
| | | ++count; |
| | | } |
| | | if(l.softmax){ |
| | | gradient_array(l.output + index, l.n*(1+l.coords), LOGISTIC, l.delta + index); |
| | | gradient_array(l.output + index + locations*l.classes, locations*l.n*(1+l.coords), |
| | | LOGISTIC, l.delta + index + locations*l.classes); |
| | | } |
| | | } |
| | | printf("Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); |
| | | printf("Region Avg IOU: %f, Avg Cat Pred: %f, Avg Obj: %f, Avg Any: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); |
| | | } |
| | | } |
| | | |