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import os.path as osp
import warnings
from collections import OrderedDict
from math import inf
from pathlib import Path
from typing import Callable, Dict, List, Optional, Sequence, Union
from mmengine.dist import is_main_process
from mmengine.fileio import FileClient, get_file_backend
from mmengine.registry import HOOKS
from mmengine.utils import is_list_of, is_seq_of
from .hook import Hook
DATA_BATCH = Optional[Union[dict, tuple, list]]
@HOOKS.register_module()
class CheckpointHook(Hook):
"""Save checkpoints periodically.
Args:
interval (int): The saving period. If ``by_epoch=True``, interval
indicates epochs, otherwise it indicates iterations.
Defaults to -1, which means "never".
by_epoch (bool): Saving checkpoints by epoch or by iteration.
Defaults to True.
save_optimizer (bool): Whether to save optimizer state_dict in the
checkpoint. It is usually used for resuming experiments.
Defaults to True.
save_param_scheduler (bool): Whether to save param_scheduler state_dict
in the checkpoint. It is usually used for resuming experiments.
Defaults to True.
out_dir (str, Path, Optional): The root directory to save checkpoints.
If not specified, ``runner.work_dir`` will be used by default. If
specified, the ``out_dir`` will be the concatenation of ``out_dir``
and the last level directory of ``runner.work_dir``. For example,
if the input ``our_dir`` is ``./tmp`` and ``runner.work_dir`` is
``./work_dir/cur_exp``, then the ckpt will be saved in
``./tmp/cur_exp``. Defaults to None.
max_keep_ckpts (int): The maximum checkpoints to keep.
In some cases we want only the latest few checkpoints and would
like to delete old ones to save the disk space.
Defaults to -1, which means unlimited.
save_last (bool): Whether to force the last checkpoint to be
saved regardless of interval. Defaults to True.
save_best (str, List[str], optional): If a metric is specified, it
would measure the best checkpoint during evaluation. If a list of
metrics is passed, it would measure a group of best checkpoints
corresponding to the passed metrics. The information about best
checkpoint(s) would be saved in ``runner.message_hub`` to keep
best score value and best checkpoint path, which will be also
loaded when resuming checkpoint. Options are the evaluation metrics
on the test dataset. e.g., ``bbox_mAP``, ``segm_mAP`` for bbox
detection and instance segmentation. ``AR@100`` for proposal
recall. If ``save_best`` is ``auto``, the first key of the returned
``OrderedDict`` result will be used. Defaults to None.
rule (str, List[str], optional): Comparison rule for best score. If
set to None, it will infer a reasonable rule. Keys such as 'acc',
'top' .etc will be inferred by 'greater' rule. Keys contain 'loss'
will be inferred by 'less' rule. If ``save_best`` is a list of
metrics and ``rule`` is a str, all metrics in ``save_best`` will
share the comparison rule. If ``save_best`` and ``rule`` are both
lists, their length must be the same, and metrics in ``save_best``
will use the corresponding comparison rule in ``rule``. Options
are 'greater', 'less', None and list which contains 'greater' and
'less'. Defaults to None.
greater_keys (List[str], optional): Metric keys that will be
inferred by 'greater' comparison rule. If ``None``,
_default_greater_keys will be used. Defaults to None.
less_keys (List[str], optional): Metric keys that will be
inferred by 'less' comparison rule. If ``None``, _default_less_keys
will be used. Defaults to None.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmengine.fileio.FileClient` for details.
Defaults to None. It will be deprecated in future. Please use
``backend_args`` instead.
filename_tmpl (str, optional): String template to indicate checkpoint
name. If specified, must contain one and only one "{}", which will
be replaced with ``epoch + 1`` if ``by_epoch=True`` else
``iteration + 1``.
Defaults to None, which means "epoch_{}.pth" or "iter_{}.pth"
accordingly.
backend_args (dict, optional): Arguments to instantiate the
preifx of uri corresponding backend. Defaults to None.
New in v0.2.0.
Examples:
>>> # Save best based on single metric
>>> CheckpointHook(interval=2, by_epoch=True, save_best='acc',
>>> rule='less')
>>> # Save best based on multi metrics with the same comparison rule
>>> CheckpointHook(interval=2, by_epoch=True,
>>> save_best=['acc', 'mIoU'], rule='greater')
>>> # Save best based on multi metrics with different comparison rule
>>> CheckpointHook(interval=2, by_epoch=True,
>>> save_best=['FID', 'IS'], rule=['less', 'greater'])
"""
out_dir: str
priority = 'VERY_LOW'
# logic to save best checkpoints
# Since the key for determining greater or less is related to the
# downstream tasks, downstream repositories may need to overwrite
# the following inner variables accordingly.
rule_map = {'greater': lambda x, y: x > y, 'less': lambda x, y: x < y}
init_value_map = {'greater': -inf, 'less': inf}
_default_greater_keys = [
'acc', 'top', 'AR@', 'auc', 'precision', 'mAP', 'mDice', 'mIoU',
'mAcc', 'aAcc'
]
_default_less_keys = ['loss']
def __init__(self,
interval: int = -1,
by_epoch: bool = True,
save_optimizer: bool = True,
save_param_scheduler: bool = True,
out_dir: Optional[Union[str, Path]] = None,
max_keep_ckpts: int = -1,
save_last: bool = True,
save_best: Union[str, List[str], None] = None,
rule: Union[str, List[str], None] = None,
greater_keys: Optional[Sequence[str]] = None,
less_keys: Optional[Sequence[str]] = None,
file_client_args: Optional[dict] = None,
filename_tmpl: Optional[str] = None,
backend_args: Optional[dict] = None,
**kwargs) -> None:
self.interval = interval
self.by_epoch = by_epoch
self.save_optimizer = save_optimizer
self.save_param_scheduler = save_param_scheduler
self.out_dir = out_dir # type: ignore
self.max_keep_ckpts = max_keep_ckpts
self.save_last = save_last
self.args = kwargs
if file_client_args is not None:
warnings.warn(
'"file_client_args" will be deprecated in future. '
'Please use "backend_args" instead', DeprecationWarning)
if backend_args is not None:
raise ValueError(
'"file_client_args" and "backend_args" cannot be set '
'at the same time.')
self.file_client_args = file_client_args
self.backend_args = backend_args
if filename_tmpl is None:
if self.by_epoch:
self.filename_tmpl = 'epoch_{}.pth'
else:
self.filename_tmpl = 'iter_{}.pth'
else:
self.filename_tmpl = filename_tmpl
# save best logic
assert (isinstance(save_best, str) or is_list_of(save_best, str)
or (save_best is None)), (
'"save_best" should be a str or list of str or None, '
f'but got {type(save_best)}')
if isinstance(save_best, list):
if 'auto' in save_best:
assert len(save_best) == 1, (
'Only support one "auto" in "save_best" list.')
assert len(save_best) == len(
set(save_best)), ('Find duplicate element in "save_best".')
else:
# convert str to list[str]
if save_best is not None:
save_best = [save_best] # type: ignore # noqa: F401
self.save_best = save_best
# rule logic
assert (isinstance(rule, str) or is_list_of(rule, str)
or (rule is None)), (
'"rule" should be a str or list of str or None, '
f'but got {type(rule)}')
if isinstance(rule, list):
# check the length of rule list
assert len(rule) in [
1,
len(self.save_best) # type: ignore
], ('Number of "rule" must be 1 or the same as number of '
f'"save_best", but got {len(rule)}.')
else:
# convert str/None to list
rule = [rule] # type: ignore # noqa: F401
if greater_keys is None:
self.greater_keys = self._default_greater_keys
else:
if not isinstance(greater_keys, (list, tuple)):
greater_keys = (greater_keys, ) # type: ignore
assert is_seq_of(greater_keys, str)
self.greater_keys = greater_keys # type: ignore
if less_keys is None:
self.less_keys = self._default_less_keys
else:
if not isinstance(less_keys, (list, tuple)):
less_keys = (less_keys, ) # type: ignore
assert is_seq_of(less_keys, str)
self.less_keys = less_keys # type: ignore
if self.save_best is not None:
self.is_better_than: Dict[str, Callable] = dict()
self._init_rule(rule, self.save_best)
if len(self.key_indicators) == 1:
self.best_ckpt_path: Optional[str] = None
else:
self.best_ckpt_path_dict: Dict = dict()
def before_train(self, runner) -> None:
"""Finish all operations, related to checkpoint.
This function will get the appropriate file client, and the directory
to save these checkpoints of the model.
Args:
runner (Runner): The runner of the training process.
"""
if self.out_dir is None:
self.out_dir = runner.work_dir
# If self.file_client_args is None, self.file_client will not
# used in CheckpointHook. To avoid breaking backward compatibility,
# it will not be removed util the release of MMEngine1.0
self.file_client = FileClient.infer_client(self.file_client_args,
self.out_dir)
if self.file_client_args is None:
self.file_backend = get_file_backend(
self.out_dir, backend_args=self.backend_args)
else:
self.file_backend = self.file_client
# if `self.out_dir` is not equal to `runner.work_dir`, it means that
# `self.out_dir` is set so the final `self.out_dir` is the
# concatenation of `self.out_dir` and the last level directory of
# `runner.work_dir`
if self.out_dir != runner.work_dir:
basename = osp.basename(runner.work_dir.rstrip(osp.sep))
self.out_dir = self.file_backend.join_path(
self.out_dir, basename) # type: ignore # noqa: E501
runner.logger.info(f'Checkpoints will be saved to {self.out_dir}.')
if self.save_best is not None:
if len(self.key_indicators) == 1:
if 'best_ckpt' not in runner.message_hub.runtime_info:
self.best_ckpt_path = None
else:
self.best_ckpt_path = runner.message_hub.get_info(
'best_ckpt')
else:
for key_indicator in self.key_indicators:
best_ckpt_name = f'best_ckpt_{key_indicator}'
if best_ckpt_name not in runner.message_hub.runtime_info:
self.best_ckpt_path_dict[key_indicator] = None
else:
self.best_ckpt_path_dict[
key_indicator] = runner.message_hub.get_info(
best_ckpt_name)
def after_train_epoch(self, runner) -> None:
"""Save the checkpoint and synchronize buffers after each epoch.
Args:
runner (Runner): The runner of the training process.
"""
if not self.by_epoch:
return
# save checkpoint for following cases:
# 1. every ``self.interval`` epochs
# 2. reach the last epoch of training
if self.every_n_epochs(runner, self.interval) or (
self.save_last and self.is_last_train_epoch(runner)):
runner.logger.info(
f'Saving checkpoint at {runner.epoch + 1} epochs')
self._save_checkpoint(runner)
def after_val_epoch(self, runner, metrics):
"""Save the checkpoint and synchronize buffers after each evaluation
epoch.
Args:
runner (Runner): The runner of the training process.
metrics (dict): Evaluation results of all metrics
"""
self._save_best_checkpoint(runner, metrics)
def _get_metric_score(self, metrics, key_indicator):
eval_res = OrderedDict()
if metrics is not None:
eval_res.update(metrics)
if len(eval_res) == 0:
warnings.warn(
'Since `eval_res` is an empty dict, the behavior to save '
'the best checkpoint will be skipped in this evaluation.')
return None
return eval_res[key_indicator]
def _save_checkpoint(self, runner) -> None:
"""Save the current checkpoint and delete outdated checkpoint.
Args:
runner (Runner): The runner of the training process.
"""
if self.by_epoch:
ckpt_filename = self.filename_tmpl.format(runner.epoch + 1)
else:
ckpt_filename = self.filename_tmpl.format(runner.iter + 1)
runner.save_checkpoint(
self.out_dir,
ckpt_filename,
self.file_client_args,
save_optimizer=self.save_optimizer,
save_param_scheduler=self.save_param_scheduler,
by_epoch=self.by_epoch,
backend_args=self.backend_args,
**self.args)
# Model parallel-like training should involve pulling sharded states
# from all ranks, but skip the following procedure.
if not is_main_process():
return
runner.message_hub.update_info(
'last_ckpt',
self.file_backend.join_path(self.out_dir, ckpt_filename))
# remove other checkpoints
if self.max_keep_ckpts > 0:
if self.by_epoch:
current_ckpt = runner.epoch + 1
else:
current_ckpt = runner.iter + 1
redundant_ckpts = range(
current_ckpt - self.max_keep_ckpts * self.interval, 0,
-self.interval)
for _step in redundant_ckpts:
ckpt_path = self.file_backend.join_path(
self.out_dir, self.filename_tmpl.format(_step))
if self.file_backend.isfile(ckpt_path):
self.file_backend.remove(ckpt_path)
else:
break
save_file = osp.join(runner.work_dir, 'last_checkpoint')
filepath = self.file_backend.join_path(self.out_dir, ckpt_filename)
with open(save_file, 'w') as f:
f.write(filepath)
def _save_best_checkpoint(self, runner, metrics) -> None:
"""Save the current checkpoint and delete outdated checkpoint.
Args:
runner (Runner): The runner of the training process.
metrics (dict): Evaluation results of all metrics.
"""
if not self.save_best:
return
if self.by_epoch:
ckpt_filename = self.filename_tmpl.format(runner.epoch)
cur_type, cur_time = 'epoch', runner.epoch
else:
ckpt_filename = self.filename_tmpl.format(runner.iter)
cur_type, cur_time = 'iter', runner.iter
# handle auto in self.key_indicators and self.rules before the loop
if 'auto' in self.key_indicators:
self._init_rule(self.rules, [list(metrics.keys())[0]])
# save best logic
# get score from messagehub
for key_indicator, rule in zip(self.key_indicators, self.rules):
key_score = self._get_metric_score(metrics, key_indicator)
if len(self.key_indicators) == 1:
best_score_key = 'best_score'
runtime_best_ckpt_key = 'best_ckpt'
best_ckpt_path = self.best_ckpt_path
else:
best_score_key = f'best_score_{key_indicator}'
runtime_best_ckpt_key = f'best_ckpt_{key_indicator}'
best_ckpt_path = self.best_ckpt_path_dict[key_indicator]
if best_score_key not in runner.message_hub.runtime_info:
best_score = self.init_value_map[rule]
else:
best_score = runner.message_hub.get_info(best_score_key)
if key_score is None or not self.is_better_than[key_indicator](
key_score, best_score):
continue
best_score = key_score
runner.message_hub.update_info(best_score_key, best_score)
if best_ckpt_path and \
self.file_client.isfile(best_ckpt_path) and \
is_main_process():
self.file_client.remove(best_ckpt_path)
runner.logger.info(
f'The previous best checkpoint {best_ckpt_path} '
'is removed')
best_ckpt_name = f'best_{key_indicator}_{ckpt_filename}'
if len(self.key_indicators) == 1:
self.best_ckpt_path = self.file_client.join_path( # type: ignore # noqa: E501
self.out_dir, best_ckpt_name)
runner.message_hub.update_info(runtime_best_ckpt_key,
self.best_ckpt_path)
else:
self.best_ckpt_path_dict[
key_indicator] = self.file_client.join_path( # type: ignore # noqa: E501
self.out_dir, best_ckpt_name)
runner.message_hub.update_info(
runtime_best_ckpt_key,
self.best_ckpt_path_dict[key_indicator])
runner.save_checkpoint(
self.out_dir,
filename=best_ckpt_name,
file_client_args=self.file_client_args,
save_optimizer=False,
save_param_scheduler=False,
by_epoch=False,
backend_args=self.backend_args)
runner.logger.info(
f'The best checkpoint with {best_score:0.4f} {key_indicator} '
f'at {cur_time} {cur_type} is saved to {best_ckpt_name}.')
def _init_rule(self, rules, key_indicators) -> None:
"""Initialize rule, key_indicator, comparison_func, and best score. If
key_indicator is a list of string and rule is a string, all metric in
the key_indicator will share the same rule.
Here is the rule to determine which rule is used for key indicator when
the rule is not specific (note that the key indicator matching is case-
insensitive):
1. If the key indicator is in ``self.greater_keys``, the rule
will be specified as 'greater'.
2. Or if the key indicator is in ``self.less_keys``, the rule
will be specified as 'less'.
3. Or if any one item in ``self.greater_keys`` is a substring of
key_indicator, the rule will be specified as 'greater'.
4. Or if any one item in ``self.less_keys`` is a substring of
key_indicator, the rule will be specified as 'less'.
Args:
rule (List[Optional[str]]): Comparison rule for best score.
key_indicator (List[str]): Key indicator to determine
the comparison rule.
"""
if len(rules) == 1:
rules = rules * len(key_indicators)
self.rules = []
for rule, key_indicator in zip(rules, key_indicators):
if rule not in self.rule_map and rule is not None:
raise KeyError('rule must be greater, less or None, '
f'but got {rule}.')
if rule is None and key_indicator != 'auto':
# `_lc` here means we use the lower case of keys for
# case-insensitive matching
key_indicator_lc = key_indicator.lower()
greater_keys = {key.lower() for key in self.greater_keys}
less_keys = {key.lower() for key in self.less_keys}
if key_indicator_lc in greater_keys:
rule = 'greater'
elif key_indicator_lc in less_keys:
rule = 'less'
elif any(key in key_indicator_lc for key in greater_keys):
rule = 'greater'
elif any(key in key_indicator_lc for key in less_keys):
rule = 'less'
else:
raise ValueError('Cannot infer the rule for key '
f'{key_indicator}, thus a specific rule '
'must be specified.')
if rule is not None:
self.is_better_than[key_indicator] = self.rule_map[rule]
self.rules.append(rule)
self.key_indicators = key_indicators
def after_train_iter(self,
runner,
batch_idx: int,
data_batch: DATA_BATCH = None,
outputs=Optional[dict]) -> None:
"""Save the checkpoint and synchronize buffers after each iteration.
Args:
runner (Runner): The runner of the training process.
batch_idx (int): The index of the current batch in the train loop.
data_batch (dict or tuple or list, optional): Data from dataloader.
outputs (dict, optional): Outputs from model.
"""
if self.by_epoch:
return
# save checkpoint for following cases:
# 1. every ``self.interval`` iterations
# 2. reach the last iteration of training
if self.every_n_train_iters(runner, self.interval) or \
(self.save_last and
self.is_last_train_iter(runner)):
runner.logger.info(
f'Saving checkpoint at {runner.iter + 1} iterations')
self._save_checkpoint(runner)
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