smart_optimizer() improved reporting (#8887)

Update smart_optimizer() weight_decay reporting

train.py

@@ -152,7 +152,6 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     nbs = 64  # nominal batch size
     accumulate = max(round(nbs / batch_size), 1)  # accumulate loss before optimizing
     hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
-    LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
     optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
 
     # Scheduler