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# Copyright (c) OpenMMLab. All rights reserved.
from mmcv.runner import build_optimizer
from mmcv.runner.optimizer import OPTIMIZER_BUILDERS as MMCV_OPTIMIZER_BUILDERS
from mmcv.utils import Registry, build_from_cfg
OPTIMIZERS = Registry('optimizers')
OPTIMIZER_BUILDERS = Registry(
'optimizer builder', parent=MMCV_OPTIMIZER_BUILDERS)
def build_optimizer_constructor(cfg):
constructor_type = cfg.get('type')
if constructor_type in OPTIMIZER_BUILDERS:
return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
elif constructor_type in MMCV_OPTIMIZER_BUILDERS:
return build_from_cfg(cfg, MMCV_OPTIMIZER_BUILDERS)
else:
raise KeyError(f'{constructor_type} is not registered '
'in the optimizer builder registry.')
def build_optimizers(model, cfgs):
"""Build multiple optimizers from configs.
If `cfgs` contains several dicts for optimizers, then a dict for each
constructed optimizers will be returned.
If `cfgs` only contains one optimizer config, the constructed optimizer
itself will be returned.
For example,
1) Multiple optimizer configs:
.. code-block:: python
optimizer_cfg = dict(
model1=dict(type='SGD', lr=lr),
model2=dict(type='SGD', lr=lr))
The return dict is
``dict('model1': torch.optim.Optimizer, 'model2': torch.optim.Optimizer)``
2) Single optimizer config:
.. code-block:: python
optimizer_cfg = dict(type='SGD', lr=lr)
The return is ``torch.optim.Optimizer``.
Args:
model (:obj:`nn.Module`): The model with parameters to be optimized.
cfgs (dict): The config dict of the optimizer.
Returns:
dict[:obj:`torch.optim.Optimizer`] | :obj:`torch.optim.Optimizer`:
The initialized optimizers.
"""
optimizers = {}
if hasattr(model, 'module'):
model = model.module
# determine whether 'cfgs' has several dicts for optimizers
if all(isinstance(v, dict) for v in cfgs.values()):
for key, cfg in cfgs.items():
cfg_ = cfg.copy()
module = getattr(model, key)
optimizers[key] = build_optimizer(module, cfg_)
return optimizers
return build_optimizer(model, cfgs)
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