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build_optimizer() assign all parameters (#2855)

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Glenn Jocher 2023-05-26 21:03:03 +02:00 committed by GitHub
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commit 61fa5efe6d
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1 changed files with 11 additions and 7 deletions
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18
ultralytics/yolo/engine/trainer.py
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@ -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)
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 module_name, module in model.named_modules():
for param_name, param in module.named_parameters(recurse=False):
fullname = f'{module_name}.{param_name}' if module_name else param_name
if 'bias' in fullname: # bias (no decay)
g[2].append(param)
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