Update optimizer param group strategy (ultralytics#7376)

* Update optimizer param group strategy

Avoid empty lists on missing BathNorm2d models as in ultralytics#7375

* fix init

train.py

@@ -150,27 +150,27 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
     hyp['weight_decay'] *= batch_size * accumulate / nbs  # scale weight_decay
     LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
 
-    g0, g1, g2 = [], [], []  # optimizer parameter groups
+    g = [], [], []  # optimizer parameter groups
     for v in model.modules():
         if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):  # bias
-            g2.append(v.bias)
+            g[2].append(v.bias)
         if isinstance(v, nn.BatchNorm2d):  # weight (no decay)
-            g0.append(v.weight)
+            g[1].append(v.weight)
         elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
-            g1.append(v.weight)
+            g[0].append(v.weight)
 
     if opt.optimizer == 'Adam':
-        optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
+        optimizer = Adam(g[2], lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
     elif opt.optimizer == 'AdamW':
-        optimizer = AdamW(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
+        optimizer = AdamW(g[2], lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
     else:
-        optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
+        optimizer = SGD(g[2], lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
 
-    optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']})  # add g1 with weight_decay
-    optimizer.add_param_group({'params': g2})  # add g2 (biases)
+    optimizer.add_param_group({'params': g[0], 'weight_decay': hyp['weight_decay']})  # add g0 with weight_decay
+    optimizer.add_param_group({'params': g[1]})  # add g1 (BatchNorm2d weights)
     LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
-                f"{len(g0)} weight (no decay), {len(g1)} weight, {len(g2)} bias")
-    del g0, g1, g2
+                f"{len(g[1])} weight (no decay), {len(g[0])} weight, {len(g[2])} bias")
+    del g
 
     # Scheduler
     if opt.cos_lr: