two_Two:

my idea is to use 0.7 for regression and 0.3 for classification since I want the regression decrease more.


That’s ok, for non-trainable proportion it is enough to scale one of losses, as total loss will be additionally implicitly rescaled by learning rate. I.e. total_loss = classification_loss + regr_loss * k, with positive k hyperparameter (e.g. 0.7/0.3).
You may try training loss weights, in that case they should sum to 1 indeed.
PS I’m not familiar with retinanet to give more specific tips.