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Clear truth_idxs between samples (#1870)

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* Clear truth_idxs between samples

* Move truth_idxs inside loop body after all

* Push to truth_idxs even when the box can't be detected; improve formatting
This commit is contained in:
Juha Reunanen 2019-08-31 20:10:05 +03:00 committed by Davis E. King
parent 170877da88
commit e89e28b94e
1 changed files with 3 additions and 2 deletions
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5
dlib/dnn/loss.h
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@ -1125,7 +1125,6 @@ namespace dlib
const float* out_data = output_tensor.host();
std::vector<size_t> truth_idxs; truth_idxs.reserve(truth->size());
std::vector<intermediate_detection> dets;
for (long i = 0; i < output_tensor.num_samples(); ++i)
{
@ -1140,6 +1139,8 @@ namespace dlib
det_thresh_speed_adjust = std::max(det_thresh_speed_adjust,dets[max_num_initial_dets].detection_confidence + options.loss_per_false_alarm);
}
std::vector<size_t> truth_idxs;
truth_idxs.reserve(truth->size());
// The loss will measure the number of incorrect detections. A detection is
// incorrect if it doesn't hit a truth rectangle or if it is a duplicate detection
@ -1155,6 +1156,7 @@ namespace dlib
{
// Ignore boxes that can't be detected by the CNN.
loss -= options.loss_per_missed_target;
truth_idxs.push_back(0);
continue;
}
const size_t idx = (k*output_tensor.nr() + p.y())*output_tensor.nc() + p.x();
@ -1241,7 +1243,6 @@ namespace dlib
hit_truth_table.assign(hit_truth_table.size(), false);
final_dets.clear();
// Now figure out which detections jointly maximize the loss and detection score sum. We
// need to take into account the fact that allowing a true detection in the output, while
// initially reducing the loss, may allow us to increase the loss later with many duplicate