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from ditk import logging
import signal
import sys
import traceback
from typing import Callable
import torch
import torch.utils.data # torch1.1.0 compatibility
from ding.utils import read_file, save_file
logger = logging.getLogger('default_logger')
def build_checkpoint_helper(cfg):
"""
Overview:
Use config to build checkpoint helper.
Arguments:
- cfg (:obj:`dict`): ckpt_helper config
Returns:
- (:obj:`CheckpointHelper`): checkpoint_helper created by this function
"""
return CheckpointHelper()
class CheckpointHelper:
"""
Overview:
Help to save or load checkpoint by give args.
Interfaces:
``__init__``, ``save``, ``load``, ``_remove_prefix``, ``_add_prefix``, ``_load_matched_model_state_dict``
"""
def __init__(self):
pass
def _remove_prefix(self, state_dict: dict, prefix: str = 'module.') -> dict:
"""
Overview:
Remove prefix in state_dict
Arguments:
- state_dict (:obj:`dict`): model's state_dict
- prefix (:obj:`str`): this prefix will be removed in keys
Returns:
- new_state_dict (:obj:`dict`): new state_dict after removing prefix
"""
new_state_dict = {}
for k, v in state_dict.items():
if k.startswith(prefix):
new_k = ''.join(k.split(prefix))
else:
new_k = k
new_state_dict[new_k] = v
return new_state_dict
def _add_prefix(self, state_dict: dict, prefix: str = 'module.') -> dict:
"""
Overview:
Add prefix in state_dict
Arguments:
- state_dict (:obj:`dict`): model's state_dict
- prefix (:obj:`str`): this prefix will be added in keys
Returns:
- (:obj:`dict`): new state_dict after adding prefix
"""
return {prefix + k: v for k, v in state_dict.items()}
def save(
self,
path: str,
model: torch.nn.Module,
optimizer: torch.optim.Optimizer = None,
last_iter: 'CountVar' = None, # noqa
last_epoch: 'CountVar' = None, # noqa
last_frame: 'CountVar' = None, # noqa
dataset: torch.utils.data.Dataset = None,
collector_info: torch.nn.Module = None,
prefix_op: str = None,
prefix: str = None,
) -> None:
"""
Overview:
Save checkpoint by given args
Arguments:
- path (:obj:`str`): the path of saving checkpoint
- model (:obj:`torch.nn.Module`): model to be saved
- optimizer (:obj:`torch.optim.Optimizer`): optimizer obj
- last_iter (:obj:`CountVar`): iter num, default None
- last_epoch (:obj:`CountVar`): epoch num, default None
- last_frame (:obj:`CountVar`): frame num, default None
- dataset (:obj:`torch.utils.data.Dataset`): dataset, should be replaydataset
- collector_info (:obj:`torch.nn.Module`): attr of checkpoint, save collector info
- prefix_op (:obj:`str`): should be ['remove', 'add'], process on state_dict
- prefix (:obj:`str`): prefix to be processed on state_dict
"""
checkpoint = {}
model = model.state_dict()
if prefix_op is not None: # remove or add prefix to model.keys()
prefix_func = {'remove': self._remove_prefix, 'add': self._add_prefix}
if prefix_op not in prefix_func.keys():
raise KeyError('invalid prefix_op:{}'.format(prefix_op))
else:
model = prefix_func[prefix_op](model, prefix)
checkpoint['model'] = model
if optimizer is not None: # save optimizer
assert (last_iter is not None or last_epoch is not None)
checkpoint['last_iter'] = last_iter.val
if last_epoch is not None:
checkpoint['last_epoch'] = last_epoch.val
if last_frame is not None:
checkpoint['last_frame'] = last_frame.val
checkpoint['optimizer'] = optimizer.state_dict()
if dataset is not None:
checkpoint['dataset'] = dataset.state_dict()
if collector_info is not None:
checkpoint['collector_info'] = collector_info.state_dict()
save_file(path, checkpoint)
logger.info('save checkpoint in {}'.format(path))
def _load_matched_model_state_dict(self, model: torch.nn.Module, ckpt_state_dict: dict) -> None:
"""
Overview:
Load matched model state_dict, and show mismatch keys between model's state_dict and checkpoint's state_dict
Arguments:
- model (:obj:`torch.nn.Module`): model
- ckpt_state_dict (:obj:`dict`): checkpoint's state_dict
"""
assert isinstance(model, torch.nn.Module)
diff = {'miss_keys': [], 'redundant_keys': [], 'mismatch_shape_keys': []}
model_state_dict = model.state_dict()
model_keys = set(model_state_dict.keys())
ckpt_keys = set(ckpt_state_dict.keys())
diff['miss_keys'] = model_keys - ckpt_keys
diff['redundant_keys'] = ckpt_keys - model_keys
intersection_keys = model_keys.intersection(ckpt_keys)
valid_keys = []
for k in intersection_keys:
if model_state_dict[k].shape == ckpt_state_dict[k].shape:
valid_keys.append(k)
else:
diff['mismatch_shape_keys'].append(
'{}\tmodel_shape: {}\tckpt_shape: {}'.format(
k, model_state_dict[k].shape, ckpt_state_dict[k].shape
)
)
valid_ckpt_state_dict = {k: v for k, v in ckpt_state_dict.items() if k in valid_keys}
model.load_state_dict(valid_ckpt_state_dict, strict=False)
for n, keys in diff.items():
for k in keys:
logger.info('{}: {}'.format(n, k))
def load(
self,
load_path: str,
model: torch.nn.Module,
optimizer: torch.optim.Optimizer = None,
last_iter: 'CountVar' = None, # noqa
last_epoch: 'CountVar' = None, # noqa
last_frame: 'CountVar' = None, # noqa
lr_schduler: 'Scheduler' = None, # noqa
dataset: torch.utils.data.Dataset = None,
collector_info: torch.nn.Module = None,
prefix_op: str = None,
prefix: str = None,
strict: bool = True,
logger_prefix: str = '',
state_dict_mask: list = [],
):
"""
Overview:
Load checkpoint by given path
Arguments:
- load_path (:obj:`str`): checkpoint's path
- model (:obj:`torch.nn.Module`): model definition
- optimizer (:obj:`torch.optim.Optimizer`): optimizer obj
- last_iter (:obj:`CountVar`): iter num, default None
- last_epoch (:obj:`CountVar`): epoch num, default None
- last_frame (:obj:`CountVar`): frame num, default None
- lr_schduler (:obj:`Schduler`): lr_schduler obj
- dataset (:obj:`torch.utils.data.Dataset`): dataset, should be replaydataset
- collector_info (:obj:`torch.nn.Module`): attr of checkpoint, save collector info
- prefix_op (:obj:`str`): should be ['remove', 'add'], process on state_dict
- prefix (:obj:`str`): prefix to be processed on state_dict
- strict (:obj:`bool`): args of model.load_state_dict
- logger_prefix (:obj:`str`): prefix of logger
- state_dict_mask (:obj:`list`): A list containing state_dict keys, \
which shouldn't be loaded into model(after prefix op)
.. note::
The checkpoint loaded from load_path is a dict, whose format is like '{'state_dict': OrderedDict(), ...}'
"""
# TODO save config
# Note: for reduce first GPU memory cost and compatible for cpu env
checkpoint = read_file(load_path)
state_dict = checkpoint['model']
if prefix_op is not None:
prefix_func = {'remove': self._remove_prefix, 'add': self._add_prefix}
if prefix_op not in prefix_func.keys():
raise KeyError('invalid prefix_op:{}'.format(prefix_op))
else:
state_dict = prefix_func[prefix_op](state_dict, prefix)
if len(state_dict_mask) > 0:
if strict:
logger.info(
logger_prefix +
'[Warning] non-empty state_dict_mask expects strict=False, but finds strict=True in input argument'
)
strict = False
for m in state_dict_mask:
state_dict_keys = list(state_dict.keys())
for k in state_dict_keys:
if k.startswith(m):
state_dict.pop(k) # ignore return value
if strict:
model.load_state_dict(state_dict, strict=True)
else:
self._load_matched_model_state_dict(model, state_dict)
logger.info(logger_prefix + 'load model state_dict in {}'.format(load_path))
if dataset is not None:
if 'dataset' in checkpoint.keys():
dataset.load_state_dict(checkpoint['dataset'])
logger.info(logger_prefix + 'load online data in {}'.format(load_path))
else:
logger.info(logger_prefix + "dataset not in checkpoint, ignore load procedure")
if optimizer is not None:
if 'optimizer' in checkpoint.keys():
optimizer.load_state_dict(checkpoint['optimizer'])
logger.info(logger_prefix + 'load optimizer in {}'.format(load_path))
else:
logger.info(logger_prefix + "optimizer not in checkpoint, ignore load procedure")
if last_iter is not None:
if 'last_iter' in checkpoint.keys():
last_iter.update(checkpoint['last_iter'])
logger.info(
logger_prefix + 'load last_iter in {}, current last_iter is {}'.format(load_path, last_iter.val)
)
else:
logger.info(logger_prefix + "last_iter not in checkpoint, ignore load procedure")
if collector_info is not None:
collector_info.load_state_dict(checkpoint['collector_info'])
logger.info(logger_prefix + 'load collector info in {}'.format(load_path))
if lr_schduler is not None:
assert (last_iter is not None)
raise NotImplementedError
class CountVar(object):
"""
Overview:
Number counter
Interfaces:
``__init__``, ``update``, ``add``
Properties:
- val (:obj:`int`): the value of the counter
"""
def __init__(self, init_val: int) -> None:
"""
Overview:
Init the var counter
Arguments:
- init_val (:obj:`int`): the init value of the counter
"""
self._val = init_val
@property
def val(self) -> int:
"""
Overview:
Get the var counter
"""
return self._val
def update(self, val: int) -> None:
"""
Overview:
Update the var counter
Arguments:
- val (:obj:`int`): the update value of the counter
"""
self._val = val
def add(self, add_num: int):
"""
Overview:
Add the number to counter
Arguments:
- add_num (:obj:`int`): the number added to the counter
"""
self._val += add_num
def auto_checkpoint(func: Callable) -> Callable:
"""
Overview:
Create a wrapper to wrap function, and the wrapper will call the save_checkpoint method
whenever an exception happens.
Arguments:
- func(:obj:`Callable`): the function to be wrapped
Returns:
- wrapper (:obj:`Callable`): the wrapped function
"""
dead_signals = ['SIGILL', 'SIGINT', 'SIGKILL', 'SIGQUIT', 'SIGSEGV', 'SIGSTOP', 'SIGTERM', 'SIGBUS']
all_signals = dead_signals + ['SIGUSR1']
def register_signal_handler(handler):
valid_sig = []
invalid_sig = []
for sig in all_signals:
try:
sig = getattr(signal, sig)
signal.signal(sig, handler)
valid_sig.append(sig)
except Exception:
invalid_sig.append(sig)
logger.info('valid sig: ({})\ninvalid sig: ({})'.format(valid_sig, invalid_sig))
def wrapper(*args, **kwargs):
handle = args[0]
assert (hasattr(handle, 'save_checkpoint'))
def signal_handler(signal_num, frame):
sig = signal.Signals(signal_num)
logger.info("SIGNAL: {}({})".format(sig.name, sig.value))
handle.save_checkpoint('ckpt_interrupt.pth.tar')
sys.exit(1)
register_signal_handler(signal_handler)
try:
return func(*args, **kwargs)
except Exception as e:
handle.save_checkpoint('ckpt_exception.pth.tar')
traceback.print_exc()
return wrapper
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