Updating the 3 normalizers (box, obj, cls) for each epoch

[AlexeyAB]

Updating the 3 normalizers (box, obj, cls) for each epoch, so each of 3 losses will be equal.

Related to: #4451 (comment)

The normalization part seems to be integrally important to every multi-loss neural network, with a good rule of thumb being that multicomponent losses (like we use in object detection) should contribute proportionally to their importance to the metric (mAP). Hyperparameter evolution is an effective but extremely slow way to do this. I'm thinking a good shortcut might be to train for one epoch with default normalizers in place, then update the 3 normalizers (box, obj, cls) to bring all of the losses back into balance, targeting a median value of say 5 each at the end of the first epoch. Logic like this might help the community in general train more effective models more easily, as I've noticed that COCO hyperparameters are not always optimal on 3rd party datasets.