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); } }
void forward_detection_layer(const detection_layer l, network_state state) { int locations = l.side*l.side; int i,j; memcpy(l.output, state.input, l.outputs*l.batch*sizeof(float)); 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, 1, l.output + index + offset); } } } if(state.train){ float avg_iou = 0; float avg_cat = 0; float avg_allcat = 0; float avg_obj = 0; float avg_anyobj = 0; int count = 0; *(l.cost) = 0; int size = l.inputs * l.batch; memset(l.delta, 0, size * sizeof(float)); 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]; } int best_index = -1; float best_iou = 0; float best_rmse = 20; 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]; avg_allcat += 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; } float iou = box_iou(out, truth); //iou = 0; 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; } } } if(l.forced){ if(truth.w*truth.h < .1){ best_index = 1; }else{ best_index = 0; } } if(l.random && *(state.net.seen) < 64000){ best_index = rand()%l.n; } int box_index = index + locations*(l.classes + l.n) + (i*l.n + best_index) * l.coords; int tbox_index = truth_index + 1 + l.classes; 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; } float iou = box_iou(out, truth); //printf("%d,", best_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] = l.object_scale * (1.-l.output[p_index]); if(l.rescore){ l.delta[p_index] = l.object_scale * (iou - l.output[p_index]); } 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-iou, 2); avg_iou += iou; ++count; } } if(0){ float *costs = calloc(l.batch*locations*l.n, sizeof(float)); for (b = 0; b < l.batch; ++b) { int index = b*l.inputs; for (i = 0; i < locations; ++i) { for (j = 0; j < l.n; ++j) { int p_index = index + locations*l.classes + i*l.n + j; costs[b*locations*l.n + i*l.n + j] = l.delta[p_index]*l.delta[p_index]; } } } int indexes[100]; top_k(costs, l.batch*locations*l.n, 100, indexes); float cutoff = costs[indexes[99]]; for (b = 0; b < l.batch; ++b) { int index = b*l.inputs; for (i = 0; i < locations; ++i) { for (j = 0; j < l.n; ++j) { int p_index = index + locations*l.classes + i*l.n + j; if (l.delta[p_index]*l.delta[p_index] < cutoff) l.delta[p_index] = 0; } } } free(costs); } *(l.cost) = pow(mag_array(l.delta, l.outputs * l.batch), 2); if ( l.b_debug ) { printf("Detection Avg IOU: %f, Pos Cat: %f, All Cat: %f, Pos Obj: %f, Any Obj: %f, count: %d\n", avg_iou/count, avg_cat/count, avg_allcat/(count*l.classes), avg_obj/count, avg_anyobj/(l.batch*locations*l.n), count); } } }