`build_optimizer()` assign all parameters (#2855)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/trainer.py

@@ -618,15 +618,19 @@ class BaseTrainer:
         Returns:
             optimizer (torch.optim.Optimizer): the built optimizer
         """
+
         g = [], [], []  # optimizer parameter groups
         bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
-        for v in model.modules():
+
-            if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias (no decay)
+        for module_name, module in model.named_modules():
-                g[2].append(v.bias)
+            for param_name, param in module.named_parameters(recurse=False):
-            if isinstance(v, bn):  # weight (no decay)
+                fullname = f'{module_name}.{param_name}' if module_name else param_name
-                g[1].append(v.weight)
+                if 'bias' in fullname:  # bias (no decay)
-            elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
+                    g[2].append(param)
-                g[0].append(v.weight)
+                elif isinstance(module, bn):  # weight (no decay)
+                    g[1].append(param)
+                else:  # weight (with decay)
+                    g[0].append(param)
 
         if name == 'Adam':
             optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum