Clarifying question: Should both losses be minimized in your problem?
If so then it seems to me that you would want something like loss = loss1 + coef. * loss2. Seems to me that your coefficient setup is perfectly reasonable, it will no doubt need to be tuned to produce the right focus for your model though.
Mathematically speaking I think hitting both individual loss functions with backward() is equivalent to this loss: loss = loss1 + loss2 as it will just accumulate the gradients together in the second backwards pass.