import warnings
import torch
from mmcv.utils import build_from_cfg, is_list_of
from torch.nn import GroupNorm, LayerNorm
from torch.nn.modules.batchnorm import _BatchNorm
from torch.nn.modules.instancenorm import _InstanceNorm
from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS
[docs]@OPTIMIZER_BUILDERS.register_module()
class DefaultOptimizerConstructor(object):
"""Default constructor for optimizers.
By default each parameter share the same optimizer settings, and we
provide an argument ``paramwise_cfg`` to specify parameter-wise settings.
It is a dict and may contain the following fields:
- ``bias_lr_mult`` (float): It will be multiplied to the learning
rate for all bias parameters (except for those in normalization
layers).
- ``bias_decay_mult`` (float): It will be multiplied to the weight
decay for all bias parameters (except for those in
normalization layers and depthwise conv layers).
- ``norm_decay_mult`` (float): It will be multiplied to the weight
decay for all weight and bias parameters of normalization
layers.
- ``dwconv_decay_mult`` (float): It will be multiplied to the weight
decay for all weight and bias parameters of depthwise conv
layers.
- ``bypass_duplicate`` (bool): If true, the duplicate parameters
would not be added into optimizer. Default: False
Args:
model (:obj:`nn.Module`): The model with parameters to be optimized.
optimizer_cfg (dict): The config dict of the optimizer.
Positional fields are
- `type`: class name of the optimizer.
Optional fields are
- any arguments of the corresponding optimizer type, e.g.,
lr, weight_decay, momentum, etc.
paramwise_cfg (dict, optional): Parameter-wise options.
Example:
>>> model = torch.nn.modules.Conv1d(1, 1, 1)
>>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9,
>>> weight_decay=0.0001)
>>> paramwise_cfg = dict(norm_decay_mult=0.)
>>> optim_builder = DefaultOptimizerConstructor(
>>> optimizer_cfg, paramwise_cfg)
>>> optimizer = optim_builder(model)
"""
def __init__(self, optimizer_cfg, paramwise_cfg=None):
if not isinstance(optimizer_cfg, dict):
raise TypeError('optimizer_cfg should be a dict',
f'but got {type(optimizer_cfg)}')
self.optimizer_cfg = optimizer_cfg
self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg
self.base_lr = optimizer_cfg.get('lr', None)
self.base_wd = optimizer_cfg.get('weight_decay', None)
self._validate_cfg()
def _validate_cfg(self):
if not isinstance(self.paramwise_cfg, dict):
raise TypeError('paramwise_cfg should be None or a dict, '
f'but got {type(self.paramwise_cfg)}')
# get base lr and weight decay
# weight_decay must be explicitly specified if mult is specified
if ('bias_decay_mult' in self.paramwise_cfg
or 'norm_decay_mult' in self.paramwise_cfg
or 'dwconv_decay_mult' in self.paramwise_cfg):
if self.base_wd is None:
raise ValueError('base_wd should not be None')
def _is_in(self, param_group, param_group_list):
assert is_list_of(param_group_list, dict)
param = set(param_group['params'])
param_set = set()
for group in param_group_list:
param_set.update(set(group['params']))
return not param.isdisjoint(param_set)
[docs] def add_params(self, params, module, prefix=''):
"""Add all parameters of module to the params list.
The parameters of the given module will be added to the list of param
groups, with specific rules defined by paramwise_cfg.
Args:
params (list[dict]): A list of param groups, it will be modified
in place.
module (nn.Module): The module to be added.
prefix (str): The prefix of the module
"""
# get param-wise options
bias_lr_mult = self.paramwise_cfg.get('bias_lr_mult', 1.)
bias_decay_mult = self.paramwise_cfg.get('bias_decay_mult', 1.)
norm_decay_mult = self.paramwise_cfg.get('norm_decay_mult', 1.)
dwconv_decay_mult = self.paramwise_cfg.get('dwconv_decay_mult', 1.)
bypass_duplicate = self.paramwise_cfg.get('bypass_duplicate', False)
# special rules for norm layers and depth-wise conv layers
is_norm = isinstance(module,
(_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm))
is_dwconv = (
isinstance(module, torch.nn.Conv2d)
and module.in_channels == module.groups)
for name, param in module.named_parameters(recurse=False):
param_group = {'params': [param]}
if not param.requires_grad:
params.append(param_group)
continue
if bypass_duplicate and self._is_in(param_group, params):
warnings.warn(f'{prefix} is duplicate. It is skipped since '
f'bypass_duplicate={bypass_duplicate}')
continue
# bias_lr_mult affects all bias parameters except for norm.bias
if name == 'bias' and not is_norm:
param_group['lr'] = self.base_lr * bias_lr_mult
# apply weight decay policies
if self.base_wd is not None:
# norm decay
if is_norm:
param_group[
'weight_decay'] = self.base_wd * norm_decay_mult
# depth-wise conv
elif is_dwconv:
param_group[
'weight_decay'] = self.base_wd * dwconv_decay_mult
# bias lr and decay
elif name == 'bias':
param_group[
'weight_decay'] = self.base_wd * bias_decay_mult
params.append(param_group)
for child_name, child_mod in module.named_children():
child_prefix = f'{prefix}.{child_name}' if prefix else child_name
self.add_params(params, child_mod, prefix=child_prefix)
def __call__(self, model):
if hasattr(model, 'module'):
model = model.module
optimizer_cfg = self.optimizer_cfg.copy()
# if no paramwise option is specified, just use the global setting
if not self.paramwise_cfg:
optimizer_cfg['params'] = model.parameters()
return build_from_cfg(optimizer_cfg, OPTIMIZERS)
# set param-wise lr and weight decay recursively
params = []
self.add_params(params, model)
optimizer_cfg['params'] = params
return build_from_cfg(optimizer_cfg, OPTIMIZERS)