Make optimizer static method inside trainer (#103)

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

ultralytics/yolo/engine/trainer.py

@@ -142,7 +142,7 @@ class BaseTrainer:
         # Optimizer
         self.accumulate = max(round(self.args.nbs / self.batch_size), 1)  # accumulate loss before optimizing
         self.args.weight_decay *= self.batch_size * self.accumulate / self.args.nbs  # scale weight_decay
-        self.optimizer = build_optimizer(model=self.model,
+        self.optimizer = self.build_optimizer(model=self.model,
                                               name=self.args.optimizer,
                                               lr=self.args.lr0,
                                               momentum=self.args.momentum,
@@ -459,10 +459,8 @@ class BaseTrainer:
         self.best_fitness = best_fitness
         self.start_epoch = start_epoch
 
-
+    @staticmethod
-def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
+    def build_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
-    # TODO: 1. docstring with example? 2. Move this inside Trainer? or utils?
-    # 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()
         for v in model.modules():