smart_optimizer() improved 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

utils/torch_utils.py

@@ -276,7 +276,7 @@ def copy_attr(a, b, include=(), exclude=()):
             setattr(a, k, v)
 
 
-def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, weight_decay=1e-5):
+def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
     # YOLOv5 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
     g = [], [], []  # optimizer parameter groups
     bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
@@ -299,10 +299,10 @@ def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, weight_decay=1e-
     else:
         raise NotImplementedError(f'Optimizer {name} not implemented.')
 
-    optimizer.add_param_group({'params': g[0], 'weight_decay': weight_decay})  # add g0 with weight_decay
+    optimizer.add_param_group({'params': g[0], 'weight_decay': decay})  # add g0 with weight_decay
     optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0})  # add g1 (BatchNorm2d weights)
-    LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
-                f"{len(g[1])} weight (no decay), {len(g[0])} weight, {len(g[2])} bias")
+    LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
+                f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
     return optimizer