smart_optimizer() revert to weight with decay (#9817)

If a parameter does not fall into any other category

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

utils/torch_utils.py

@@ -319,12 +319,13 @@ def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
     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)
-            g[2].append(v.bias)
-        if isinstance(v, bn):  # weight (no decay)
-            g[1].append(v.weight)
-        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):  # weight (with decay)
-            g[0].append(v.weight)
+        for p_name, p in v.named_parameters(recurse=0):
+            if p_name == 'bias':  # bias (no decay)
+                g[2].append(p)
+            elif p_name == 'weight' and isinstance(v, bn):  # weight (no decay)
+                g[1].append(p)
+            else:
+                g[0].append(p)  # weight (with decay)
 
     if name == 'Adam':
         optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999))  # adjust beta1 to momentum