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);
}
}
}
