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Make optimizer static method inside trainer (#103)

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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>
This commit is contained in:
Ayush Chaurasia 2022-12-27 21:32:03 +05:30 committed by GitHub
parent 5c6d11bdb2
commit 8028e2b1b8
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1 changed files with 31 additions and 33 deletions
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64
ultralytics/yolo/engine/trainer.py
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@ -142,11 +142,11 @@ 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,
name=self.args.optimizer,
lr=self.args.lr0,
momentum=self.args.momentum,
decay=self.args.weight_decay)
self.optimizer = self.build_optimizer(model=self.model,
name=self.args.optimizer,
lr=self.args.lr0,
momentum=self.args.momentum,
decay=self.args.weight_decay)
# Scheduler
if self.args.cos_lr:
self.lf = one_cycle(1, self.args.lrf, self.epochs) # cosine 1->hyp['lrf']
@ -459,33 +459,31 @@ 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):
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)
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():
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)
if name == 'Adam':
optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
elif name == 'AdamW':
optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
elif name == 'RMSProp':
optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
elif name == 'SGD':
optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
else:
raise NotImplementedError(f'Optimizer {name} not implemented.')
if name == 'Adam':
optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
elif name == 'AdamW':
optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
elif name == 'RMSProp':
optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
elif name == 'SGD':
optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
else:
raise NotImplementedError(f'Optimizer {name} not implemented.')
optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay
optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights)
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
return optimizer
optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay
optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights)
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias")
return optimizer