def setup_schedulers(self):
"""Set up schedulers."""
train_opt = self.opt['train']
scheduler_type = train_opt['scheduler'].pop('type')
if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.MultiStepRestartLR(optimizer,
**train_opt['scheduler']))
elif scheduler_type == 'CosineAnnealingRestartLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.CosineAnnealingRestartLR(
optimizer, **train_opt['scheduler']))
elif scheduler_type == 'TrueCosineAnnealingLR':
print('..', 'cosineannealingLR')
for optimizer in self.optimizers:
self.schedulers.append(
torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, **train_opt['scheduler']))
elif scheduler_type == 'LinearLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.LinearLR(optimizer, train_opt['total_iter']))
elif scheduler_type == 'VibrateLR':
for optimizer in self.optimizers:
self.schedulers.append(
lr_scheduler.VibrateLR(optimizer, train_opt['total_iter']))
else:
raise NotImplementedError(
f'Scheduler {scheduler_type} is not implemented yet.')
def setup_schedulers(self):
"""Set up schedulers."""
train_opt = self.opt['train']
scheduler_type = train_opt['scheduler'].pop('type')
if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']:
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.MultiStepRestartLR(optimizer, **train_opt['scheduler']))
elif scheduler_type == 'CosineAnnealingRestartLR':
for optimizer in self.optimizers:
self.schedulers.append(lr_scheduler.CosineAnnealingRestartLR(optimizer, **train_opt['scheduler']))
else:
raise NotImplementedError(f'Scheduler {scheduler_type} is not implemented yet.')
