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Sometimes the loss_mmod_ layer could experience excessively long runtime during 

early iterations since the model might produce a huge number of false alarms
while the detector is still bad.  Processing all these detections can cause it
to run slowly until the model is good enough to avoid really excessive amounts
of false alarms.  This change puts more of a limit on the number of false
alarms processed during those early iterations and avoids the slowdown.
This commit is contained in:
Davis King 2017-10-20 21:37:54 -04:00
parent 3e48a36ede
commit 6d343f93da
1 changed files with 10 additions and 1 deletions
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11
dlib/dnn/loss.h
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@ -725,6 +725,7 @@ namespace dlib
DLIB_CASSERT(input_tensor.num_samples() == output_tensor.num_samples());
DLIB_CASSERT(output_tensor.k() == (long)options.detector_windows.size());
double det_thresh_speed_adjust = 0;
// we will scale the loss so that it doesn't get really huge
@ -741,9 +742,17 @@ namespace dlib
std::vector<intermediate_detection> dets;
for (long i = 0; i < output_tensor.num_samples(); ++i)
{
tensor_to_dets(input_tensor, output_tensor, i, dets, -options.loss_per_false_alarm, sub);
tensor_to_dets(input_tensor, output_tensor, i, dets, -options.loss_per_false_alarm + det_thresh_speed_adjust, sub);
const unsigned long max_num_dets = 50 + truth->size()*5;
// Prevent calls to tensor_to_dets() from running for a really long time
// due to the production of an obscene number of detections.
const unsigned long max_num_initial_dets = max_num_dets*100;
if (dets.size() >= max_num_initial_dets)
{
det_thresh_speed_adjust = std::max(det_thresh_speed_adjust,dets[max_num_initial_dets].detection_confidence + options.loss_per_false_alarm);
}
// 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