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# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import List, Optional, Union
import torch
import torch.nn as nn
from torch.nn import GroupNorm, LayerNorm
from mmengine.logging import print_log
from mmengine.registry import (OPTIM_WRAPPER_CONSTRUCTORS, OPTIM_WRAPPERS,
OPTIMIZERS)
from mmengine.utils import is_list_of, mmcv_full_available
from mmengine.utils.parrots_wrapper import _BatchNorm, _InstanceNorm
from .optimizer_wrapper import OptimWrapper
@OPTIM_WRAPPER_CONSTRUCTORS.register_module()
class DefaultOptimWrapperConstructor:
"""Default constructor for optimizers.
By default, each parameter share the same optimizer settings, and we
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provide an argument ``paramwise_cfg`` to specify parameter-wise settings.
It is a dict and may contain the following fields:
- ``custom_keys`` (dict): Specified parameters-wise settings by keys. If
one of the keys in ``custom_keys`` is a substring of the name of one
parameter, then the setting of the parameter will be specified by
``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will
be ignored. It should be noted that the aforementioned ``key`` is the
longest key that is a substring of the name of the parameter. If there
are multiple matched keys with the same length, then the key with lower
alphabet order will be chosen.
``custom_keys[key]`` should be a dict and may contain fields ``lr_mult``
and ``decay_mult``. See Example 2 below.
- ``bias_lr_mult`` (float): It will be multiplied to the learning
rate for all bias parameters (except for those in normalization
layers and offset layers of DCN).
- ``bias_decay_mult`` (float): It will be multiplied to the weight
decay for all bias parameters (except for those in
normalization layers, depthwise conv layers, offset layers of DCN).
- ``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.
- ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning
rate for parameters of offset layer in the deformable convs
of a model.
- ``bypass_duplicate`` (bool): If true, the duplicate parameters
would not be added into optimizer. Default: False.
Note:
1. If the option ``dcn_offset_lr_mult`` is used, the constructor will
override the effect of ``bias_lr_mult`` in the bias of offset layer.
So be careful when using both ``bias_lr_mult`` and
``dcn_offset_lr_mult``. If you wish to apply both of them to the offset
layer in deformable convs, set ``dcn_offset_lr_mult`` to the original
``dcn_offset_lr_mult`` * ``bias_lr_mult``.
2. If the option ``dcn_offset_lr_mult`` is used, the constructor will
apply it to all the DCN layers in the model. So be careful when the
model contains multiple DCN layers in places other than backbone.
Args:
optim_wrapper_cfg (dict): The config dict of the optimizer wrapper.
- ``type``: class name of the OptimizerWrapper
- ``optimizer``: The configuration of optimizer.
- any arguments of the corresponding optimizer wrapper type,
e.g., accumulative_iters, clip_grad, etc.
The positional fields of ``optimizer`` 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 1:
>>> model = torch.nn.modules.Conv1d(1, 1, 1)
>>> dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01,
>>> paramwise_cfg = dict(norm_decay_mult=0.)
>>> optim_wrapper_builder = DefaultOptimWrapperConstructor(
>>> optim_wrapper_cfg, paramwise_cfg)
>>> optim_wrapper = optim_wrapper_builder(model)
Example 2:
>>> # assume model have attribute model.backbone and model.cls_head
>>> optim_wrapper_cfg = dict(type='OptimWrapper', optimizer=dict(
>>> type='SGD', lr=0.01, weight_decay=0.95))
>>> paramwise_cfg = dict(custom_keys={
>>> '.backbone': dict(lr_mult=0.1, decay_mult=0.9)})
>>> optim_wrapper_builder = DefaultOptimWrapperConstructor(
>>> optim_wrapper_cfg, paramwise_cfg)
>>> optim_wrapper = optim_wrapper_builder(model)
>>> # Then the `lr` and `weight_decay` for model.backbone is
>>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for
>>> # model.cls_head is (0.01, 0.95).
"""
def __init__(self,
paramwise_cfg: Optional[dict] = None):
if not isinstance(optim_wrapper_cfg, dict):
raise TypeError('optimizer_cfg should be a dict',
f'but got {type(optim_wrapper_cfg)}')
assert 'optimizer' in optim_wrapper_cfg, (
'`optim_wrapper_cfg` must contain "optimizer" config')
self.optim_wrapper_cfg = optim_wrapper_cfg.copy()
self.optimizer_cfg = self.optim_wrapper_cfg.pop('optimizer')
self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg
self.base_lr = self.optimizer_cfg.get('lr', None)
self.base_wd = self.optimizer_cfg.get('weight_decay', None)
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self._validate_cfg()
def _validate_cfg(self) -> None:
"""verify the correctness of the config."""
if not isinstance(self.paramwise_cfg, dict):
raise TypeError('paramwise_cfg should be None or a dict, '
f'but got {type(self.paramwise_cfg)}')
if 'custom_keys' in self.paramwise_cfg:
if not isinstance(self.paramwise_cfg['custom_keys'], dict):
raise TypeError(
'If specified, custom_keys must be a dict, '
f'but got {type(self.paramwise_cfg["custom_keys"])}')
if self.base_wd is None:
for key in self.paramwise_cfg['custom_keys']:
if 'decay_mult' in self.paramwise_cfg['custom_keys'][key]:
raise ValueError('base_wd should not be None')
# 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: dict, param_group_list: list) -> bool:
"""check whether the `param_group` is in the`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)
def add_params(self,
params: List[dict],
module: nn.Module,
prefix: str = '',
is_dcn_module: Optional[Union[int, float]] = None) -> None:
"""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
is_dcn_module (int|float|None): If the current module is a
submodule of DCN, `is_dcn_module` will be passed to
control conv_offset layer's learning rate. Defaults to None.
"""
# get param-wise options
custom_keys = self.paramwise_cfg.get('custom_keys', {})
# first sort with alphabet order and then sort with reversed len of str
sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True)
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)
dcn_offset_lr_mult = self.paramwise_cfg.get('dcn_offset_lr_mult', 1.)
# 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
# if the parameter match one of the custom keys, ignore other rules
is_custom = False
for key in sorted_keys:
if key in f'{prefix}.{name}':
is_custom = True
lr_mult = custom_keys[key].get('lr_mult', 1.)
param_group['lr'] = self.base_lr * lr_mult
if self.base_wd is not None:
decay_mult = custom_keys[key].get('decay_mult', 1.)
param_group['weight_decay'] = self.base_wd * decay_mult
# add custom settings to param_group
for k, v in custom_keys[key].items():
param_group[k] = v
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break
if not is_custom:
# bias_lr_mult affects all bias parameters
# except for norm.bias dcn.conv_offset.bias
if name == 'bias' and not (is_norm or is_dcn_module):
param_group['lr'] = self.base_lr * bias_lr_mult
if (prefix.find('conv_offset') != -1 and is_dcn_module
and isinstance(module, torch.nn.Conv2d)):
# deal with both dcn_offset's bias & weight
param_group['lr'] = self.base_lr * dcn_offset_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' and not is_dcn_module:
# TODO: current bias_decay_mult will have affect on DCN
param_group[
'weight_decay'] = self.base_wd * bias_decay_mult
params.append(param_group)
for key, value in param_group.items():
if key == 'params':
continue
full_name = f'{prefix}.{name}' if prefix else name
print_log(
f'paramwise_options -- {full_name}:{key}={value}',
logger='current')
if mmcv_full_available():
from mmcv.ops import DeformConv2d, ModulatedDeformConv2d
is_dcn_module = isinstance(module,
(DeformConv2d, ModulatedDeformConv2d))
else:
is_dcn_module = False
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,
is_dcn_module=is_dcn_module)
def __call__(self, model: nn.Module) -> OptimWrapper:
if hasattr(model, 'module'):
model = model.module
optim_wrapper_cfg = self.optim_wrapper_cfg.copy()
optim_wrapper_cfg.setdefault('type', 'OptimWrapper')
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()
optimizer = OPTIMIZERS.build(optimizer_cfg)
else:
# set param-wise lr and weight decay recursively
params: List = []
self.add_params(params, model)
optimizer_cfg['params'] = params
optimizer = OPTIMIZERS.build(optimizer_cfg)
optim_wrapper = OPTIM_WRAPPERS.build(
optim_wrapper_cfg, default_args=dict(optimizer=optimizer))
return optim_wrapper