| | |
| | | return iou; |
| | | } |
| | | |
| | | void delta_region_class(float *output, float *delta, int index, int class_id, int classes, tree *hier, float scale, float *avg_cat) |
| | | void delta_region_class(float *output, float *delta, int index, int class_id, int classes, tree *hier, float scale, float *avg_cat, int focal_loss) |
| | | { |
| | | int i, n; |
| | | if(hier){ |
| | |
| | | } |
| | | *avg_cat += pred; |
| | | } else { |
| | | // Focal loss |
| | | if (focal_loss) { |
| | | // Focal Loss for Dense Object Detection: http://blog.csdn.net/linmingan/article/details/77885832 |
| | | //printf("Used Focal-loss \n"); |
| | | float alpha = 0.5; // 0.25 |
| | | float gamma = 2.0; |
| | | int ti = index + class_id; |
| | | float grad = -gamma * (1 - output[ti])*logf(fmaxf(output[ti], 0.0000001))*output[ti] + (1 - output[ti])*(1 - output[ti]); |
| | | |
| | | for (n = 0; n < classes; ++n) { |
| | | delta[index + n] = scale * (((n == class_id) ? 1 : 0) - output[index + n]); |
| | | |
| | | delta[index + n] *= alpha*grad; |
| | | |
| | | if (n == class_id) *avg_cat += output[index + n]; |
| | | } |
| | | } |
| | | else { |
| | | // default |
| | | for(n = 0; n < classes; ++n){ |
| | | delta[index + n] = scale * (((n == class_id)?1 : 0) - output[index + n]); |
| | | if(n == class_id) *avg_cat += output[index + n]; |
| | | } |
| | | } |
| | | } |
| | | } |
| | | |
| | | float logit(float x) |
| | | { |
| | |
| | | } |
| | | } |
| | | int index = size*maxi + b*l.outputs + 5; |
| | | delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat); |
| | | delta_region_class(l.output, l.delta, index, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss); |
| | | ++class_count; |
| | | onlyclass_id = 1; |
| | | break; |
| | |
| | | if (best_iou > l.thresh) { |
| | | l.delta[index + 4] = 0; |
| | | if(l.classfix > 0){ |
| | | delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat); |
| | | delta_region_class(l.output, l.delta, index + 5, best_class_id, l.classes, l.softmax_tree, l.class_scale*(l.classfix == 2 ? l.output[index + 4] : 1), &avg_cat, l.focal_loss); |
| | | ++class_count; |
| | | } |
| | | } |
| | |
| | | |
| | | int class_id = state.truth[t*5 + b*l.truths + 4]; |
| | | if (l.map) class_id = l.map[class_id]; |
| | | delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat); |
| | | delta_region_class(l.output, l.delta, best_index + 5, class_id, l.classes, l.softmax_tree, l.class_scale, &avg_cat, l.focal_loss); |
| | | ++count; |
| | | ++class_count; |
| | | } |