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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import inspect
import torch
from ...utils import Registry, build_from_cfg
OPTIMIZERS = Registry('optimizer')
OPTIMIZER_BUILDERS = Registry('optimizer builder')
def register_torch_optimizers():
torch_optimizers = []
for module_name in dir(torch.optim):
if module_name.startswith('__'):
continue
_optim = getattr(torch.optim, module_name)
if inspect.isclass(_optim) and issubclass(_optim,
torch.optim.Optimizer):
OPTIMIZERS.register_module()(_optim)
torch_optimizers.append(module_name)
return torch_optimizers
TORCH_OPTIMIZERS = register_torch_optimizers()
def build_optimizer_constructor(cfg):
return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
def build_optimizer(model, cfg):
optimizer_cfg = copy.deepcopy(cfg)
constructor_type = optimizer_cfg.pop('constructor',
'DefaultOptimizerConstructor')
paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
optim_constructor = build_optimizer_constructor(
dict(
type=constructor_type,
optimizer_cfg=optimizer_cfg,
paramwise_cfg=paramwise_cfg))
optimizer = optim_constructor(model)
return optimizer
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