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# Copyright (c) OpenMMLab. All rights reserved.
import copy
import warnings
from abc import ABCMeta
from collections import defaultdict
from logging import FileHandler
import torch.nn as nn
from annotator.uniformer.mmcv.runner.dist_utils import master_only
from annotator.uniformer.mmcv.utils.logging import get_logger, logger_initialized, print_log
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``.
Args:
init_cfg (dict, optional): Initialization config dict.
"""
def __init__(self, init_cfg=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(BaseModule, self).__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
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()
# 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
# Get the initialized logger, if not exist,
# create a logger named `mmcv`
logger_names = list(logger_initialized.keys())
logger_name = logger_names[0] if logger_names else 'mmcv'
from ..cnn import initialize
from ..cnn.utils.weight_init import update_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=logger_name)
initialize(self, self.init_cfg)
if isinstance(self.init_cfg, dict):
# prevent the parameters of
# the pre-trained model
# from being overwritten by
# the `init_weights`
if self.init_cfg['type'] == 'Pretrained':
return
for m in self.children():
if hasattr(m, 'init_weights'):
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__} ')
self._is_init = True
else:
warnings.warn(f'init_weights of {self.__class__.__name__} has '
f'been called more than once.')
if is_top_level_module:
self._dump_init_info(logger_name)
for sub_module in self.modules():
del sub_module._params_init_info
@master_only
def _dump_init_info(self, logger_name):
"""Dump the initialization information to a file named
`initialization.log.json` in workdir.
Args:
logger_name (str): The name of logger.
"""
logger = get_logger(logger_name)
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():
print_log(
f'\n{name} - {param.shape}: '
f"\n{self._params_init_info[param]['init_info']} \n ",
logger=logger_name)
def __repr__(self):
s = super().__repr__()
if self.init_cfg:
s += f'\ninit_cfg={self.init_cfg}'
return s
class Sequential(BaseModule, nn.Sequential):
"""Sequential module in openmmlab.
Args:
init_cfg (dict, optional): Initialization config dict.
"""
def __init__(self, *args, init_cfg=None):
BaseModule.__init__(self, init_cfg)
nn.Sequential.__init__(self, *args)
class ModuleList(BaseModule, nn.ModuleList):
"""ModuleList in openmmlab.
Args:
modules (iterable, optional): an iterable of modules to add.
init_cfg (dict, optional): Initialization config dict.
"""
def __init__(self, modules=None, init_cfg=None):
BaseModule.__init__(self, init_cfg)
nn.ModuleList.__init__(self, modules)
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