mmengine.model.base_module 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import copy
import logging
from abc import ABCMeta
from collections import defaultdict
from logging import FileHandler
from typing import Iterable, List, Optional, Union
import torch.nn as nn
from mmengine.dist import master_only
from mmengine.logging import MMLogger, print_log
from .weight_init import PretrainedInit, initialize, update_init_info
from .wrappers.utils import is_model_wrapper
[文档]class BaseModule(nn.Module, metaclass=ABCMeta):
"""Base module for all modules in openmmlab. ``BaseModule`` is a wrapper of
``torch.nn.Module`` with additional functionality of parameter
initialization. Compared with ``torch.nn.Module``, ``BaseModule`` mainly
adds three attributes.
- ``init_cfg``: the config to control the initialization.
- ``init_weights``: The function of parameter initialization and recording
initialization information.
- ``_params_init_info``: Used to track the parameter initialization
information. This attribute only exists during executing the
``init_weights``.
Note:
:obj:`PretrainedInit` has a higher priority than any other
initializer. The loaded pretrained weights will overwrite
the previous initialized weights.
Args:
init_cfg (dict or List[dict], optional): Initialization config dict.
"""
def __init__(self, init_cfg: Union[dict, List[dict], None] = None):
"""Initialize BaseModule, inherited from `torch.nn.Module`"""
# NOTE init_cfg can be defined in different levels, but init_cfg
# in low levels has a higher priority.
super().__init__()
# define default value of init_cfg instead of hard code
# in init_weights() function
self._is_init = False
self.init_cfg = copy.deepcopy(init_cfg)
# Backward compatibility in derived classes
# if pretrained is not None:
# warnings.warn('DeprecationWarning: pretrained is a deprecated \
# key, please consider using init_cfg')
# self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
@property
def is_init(self):
return self._is_init
@is_init.setter
def is_init(self, value):
self._is_init = value
[文档] def init_weights(self):
"""Initialize the weights."""
is_top_level_module = False
# check if it is top-level module
if not hasattr(self, '_params_init_info'):
# The `_params_init_info` is used to record the initialization
# information of the parameters
# the key should be the obj:`nn.Parameter` of model and the value
# should be a dict containing
# - init_info (str): The string that describes the initialization.
# - tmp_mean_value (FloatTensor): The mean of the parameter,
# which indicates whether the parameter has been modified.
# this attribute would be deleted after all parameters
# is initialized.
self._params_init_info = defaultdict(dict)
is_top_level_module = True
# Initialize the `_params_init_info`,
# When detecting the `tmp_mean_value` of
# the corresponding parameter is changed, update related
# initialization information
for name, param in self.named_parameters():
self._params_init_info[param][
'init_info'] = f'The value is the same before and ' \
f'after calling `init_weights` ' \
f'of {self.__class__.__name__} '
self._params_init_info[param][
'tmp_mean_value'] = param.data.mean().cpu()
# pass `params_init_info` to all submodules
# All submodules share the same `params_init_info`,
# so it will be updated when parameters are
# modified at any level of the model.
for sub_module in self.modules():
sub_module._params_init_info = self._params_init_info
module_name = self.__class__.__name__
if not self._is_init:
if self.init_cfg:
print_log(
f'initialize {module_name} with init_cfg {self.init_cfg}',
logger='current',
level=logging.DEBUG)
init_cfgs = self.init_cfg
if isinstance(self.init_cfg, dict):
init_cfgs = [self.init_cfg]
# PretrainedInit has higher priority than any other init_cfg.
# Therefore we initialize `pretrained_cfg` last to overwrite
# the previous initialized weights.
# See details in https://github.com/open-mmlab/mmengine/issues/691 # noqa E501
other_cfgs = []
pretrained_cfg = []
for init_cfg in init_cfgs:
assert isinstance(init_cfg, dict)
if (init_cfg['type'] == 'Pretrained'
or init_cfg['type'] is PretrainedInit):
pretrained_cfg.append(init_cfg)
else:
other_cfgs.append(init_cfg)
initialize(self, other_cfgs)
for m in self.children():
if is_model_wrapper(m) and not hasattr(m, 'init_weights'):
m = m.module
if hasattr(m, 'init_weights') and not getattr(
m, 'is_init', False):
m.init_weights()
# users may overload the `init_weights`
update_init_info(
m,
init_info=f'Initialized by '
f'user-defined `init_weights`'
f' in {m.__class__.__name__} ')
if self.init_cfg and pretrained_cfg:
initialize(self, pretrained_cfg)
self._is_init = True
else:
print_log(
f'init_weights of {self.__class__.__name__} has '
f'been called more than once.',
logger='current',
level=logging.WARNING)
if is_top_level_module:
self._dump_init_info()
for sub_module in self.modules():
del sub_module._params_init_info
@master_only
def _dump_init_info(self):
"""Dump the initialization information to a file named
`initialization.log.json` in workdir."""
logger = MMLogger.get_current_instance()
with_file_handler = False
# dump the information to the logger file if there is a `FileHandler`
for handler in logger.handlers:
if isinstance(handler, FileHandler):
handler.stream.write(
'Name of parameter - Initialization information\n')
for name, param in self.named_parameters():
handler.stream.write(
f'\n{name} - {param.shape}: '
f"\n{self._params_init_info[param]['init_info']} \n")
handler.stream.flush()
with_file_handler = True
if not with_file_handler:
for name, param in self.named_parameters():
logger.info(
f'\n{name} - {param.shape}: '
f"\n{self._params_init_info[param]['init_info']} \n ")
def __repr__(self):
s = super().__repr__()
if self.init_cfg:
s += f'\ninit_cfg={self.init_cfg}'
return s
[文档]class Sequential(BaseModule, nn.Sequential):
"""Sequential module in openmmlab.
Ensures that all modules in ``Sequential`` have a different initialization
strategy than the outer model
Args:
init_cfg (dict, optional): Initialization config dict.
"""
def __init__(self, *args, init_cfg: Optional[dict] = None):
BaseModule.__init__(self, init_cfg)
nn.Sequential.__init__(self, *args)
[文档]class ModuleList(BaseModule, nn.ModuleList):
"""ModuleList in openmmlab.
Ensures that all modules in ``ModuleList`` have a different initialization
strategy than the outer model
Args:
modules (iterable, optional): An iterable of modules to add.
init_cfg (dict, optional): Initialization config dict.
"""
def __init__(self,
modules: Optional[Iterable] = None,
init_cfg: Optional[dict] = None):
BaseModule.__init__(self, init_cfg)
nn.ModuleList.__init__(self, modules)
[文档]class ModuleDict(BaseModule, nn.ModuleDict):
"""ModuleDict in openmmlab.
Ensures that all modules in ``ModuleDict`` have a different initialization
strategy than the outer model
Args:
modules (dict, optional): A mapping (dictionary) of (string: module)
or an iterable of key-value pairs of type (string, module).
init_cfg (dict, optional): Initialization config dict.
"""
def __init__(self,
modules: Optional[dict] = None,
init_cfg: Optional[dict] = None):
BaseModule.__init__(self, init_cfg)
nn.ModuleDict.__init__(self, modules)