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# Copyright (c) OpenMMLab. All rights reserved.
import logging
from typing import List, Optional, Union
import torch
import torch.nn as nn
from mmengine.logging import print_log
from mmengine.optim import DefaultOptimWrapperConstructor
from mmengine.utils.dl_utils import mmcv_full_available
from mmengine.utils.dl_utils.parrots_wrapper import _BatchNorm, _InstanceNorm
from torch.nn import GroupNorm, LayerNorm
from mmseg.registry import OPTIM_WRAPPER_CONSTRUCTORS
@OPTIM_WRAPPER_CONSTRUCTORS.register_module()
class ForceDefaultOptimWrapperConstructor(DefaultOptimWrapperConstructor):
"""Default constructor with forced optimizer settings.
This constructor extends the default constructor to add an option for
forcing default optimizer settings. This is useful for ensuring that
certain parameters or layers strictly adhere to pre-defined default
settings, regardless of any custom settings specified.
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 various fields like 'custom_keys',
'bias_lr_mult', etc., as well as the additional field
`force_default_settings` which allows for enforcing default settings on
optimizer parameters.
- ``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.
- ``flat_decay_mult`` (float): It will be multiplied to the weight
decay for all one-dimensional parameters
- ``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. Defaults to False.
- ``force_default_settings`` (bool): If true, this will override any
custom settings defined by ``custom_keys`` and enforce the use of
default settings for optimizer parameters like ``bias_lr_mult``.
This is particularly useful when you want to ensure that certain layers
or parameters adhere strictly to the pre-defined default settings.
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.
3. When the option ``force_default_settings`` is true, it will override
any custom settings provided in ``custom_keys``. This ensures that the
default settings for the optimizer parameters are used.
Args:
optim_wrapper_cfg (dict): The config dict of the optimizer wrapper.
Required fields of ``optim_wrapper_cfg`` are
- ``type``: class name of the OptimizerWrapper
- ``optimizer``: The configuration of optimizer.
Optional fields of ``optim_wrapper_cfg`` are
- any arguments of the corresponding optimizer wrapper type,
e.g., accumulative_counts, clip_grad, etc.
Required fields of ``optimizer`` are
- `type`: class name of the optimizer.
Optional fields of ``optimizer`` 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)
>>> optim_wrapper_cfg = dict(
>>> dict(type='OptimWrapper', optimizer=dict(type='SGD', lr=0.01,
>>> momentum=0.9, weight_decay=0.0001))
>>> 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 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', None)
bias_decay_mult = self.paramwise_cfg.get('bias_decay_mult', None)
norm_decay_mult = self.paramwise_cfg.get('norm_decay_mult', None)
dwconv_decay_mult = self.paramwise_cfg.get('dwconv_decay_mult', None)
flat_decay_mult = self.paramwise_cfg.get('flat_decay_mult', None)
bypass_duplicate = self.paramwise_cfg.get('bypass_duplicate', False)
dcn_offset_lr_mult = self.paramwise_cfg.get('dcn_offset_lr_mult', None)
force_default_settings = self.paramwise_cfg.get(
'force_default_settings', 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 bypass_duplicate and self._is_in(param_group, params):
print_log(
f'{prefix} is duplicate. It is skipped since '
f'bypass_duplicate={bypass_duplicate}',
logger='current',
level=logging.WARNING)
continue
if not param.requires_grad:
params.append(param_group)
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
break
if not is_custom or force_default_settings:
# 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) and bias_lr_mult is not None:
param_group['lr'] = self.base_lr * bias_lr_mult
if (prefix.find('conv_offset') != -1 and is_dcn_module
and dcn_offset_lr_mult is not None
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 and norm_decay_mult is not None:
param_group[
'weight_decay'] = self.base_wd * norm_decay_mult
# bias lr and decay
elif (name == 'bias' and not is_dcn_module
and bias_decay_mult is not None):
param_group[
'weight_decay'] = self.base_wd * bias_decay_mult
# depth-wise conv
elif is_dwconv and dwconv_decay_mult is not None:
param_group[
'weight_decay'] = self.base_wd * dwconv_decay_mult
# flatten parameters except dcn offset
elif (param.ndim == 1 and not is_dcn_module
and flat_decay_mult is not None):
param_group[
'weight_decay'] = self.base_wd * flat_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)
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