# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from pathlib import Path
from typing import Any, Optional, Sequence, Tuple, Union
from mmengine.data import BaseDataSample
from mmengine.fileio import FileClient
from mmengine.registry import HOOKS
from .hook import Hook
DATA_BATCH = Optional[Sequence[Tuple[Any, BaseDataSample]]]
@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.
Default: -1, which means "never".
by_epoch (bool): Saving checkpoints by epoch or by iteration.
Default: True.
save_optimizer (bool): Whether to save optimizer state_dict in the
checkpoint. It is usually used for resuming experiments.
Default: True.
out_dir (str, optional | Path): 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``. Deafule 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.
Default: -1, which means unlimited.
save_last (bool): Whether to force the last checkpoint to be
saved regardless of interval. Default: True.
sync_buffer (bool): Whether to synchronize buffers in
different gpus. Default: False.
file_client_args (dict, optional): Arguments to instantiate a
FileClient. See :class:`mmcv.fileio.FileClient` for details.
Default: None.
"""
priority = 'VERY_LOW'
def __init__(self,
interval: int = -1,
by_epoch: bool = True,
save_optimizer: bool = True,
out_dir: Union[str, Path] = None,
max_keep_ckpts: int = -1,
save_last: bool = True,
sync_buffer: bool = False,
file_client_args: Optional[dict] = None,
**kwargs) -> None:
self.interval = interval
self.by_epoch = by_epoch
self.save_optimizer = save_optimizer
self.out_dir = out_dir
self.max_keep_ckpts = max_keep_ckpts
self.save_last = save_last
self.args = kwargs
self.sync_buffer = sync_buffer
self.file_client_args = file_client_args
def before_run(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 not self.out_dir:
self.out_dir = runner.work_dir
self.file_client = FileClient.infer_client(self.file_client_args,
self.out_dir)
# 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_client.join_path(
self.out_dir, basename) # type: ignore # noqa: E501
runner.logger.info((f'Checkpoints will be saved to {self.out_dir} by '
f'{self.file_client.name}.'))
# disable the create_symlink option because some file backends do not
# allow to create a symlink
if 'create_symlink' in self.args:
if self.args[
'create_symlink'] and not self.file_client.allow_symlink:
self.args['create_symlink'] = False
warnings.warn(
('create_symlink is set as True by the user but is changed'
'to be False because creating symbolic link is not '
f'allowed in {self.file_client.name}'))
else:
self.args['create_symlink'] = self.file_client.allow_symlink
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')
if self.sync_buffer:
pass
# TODO
self._save_checkpoint(runner)
# TODO Add master_only decorator
def _save_checkpoint(self, runner) -> None:
"""Save the current checkpoint and delete outdated checkpoint.
Args:
runner (Runner): The runner of the training process.
"""
runner.save_checkpoint(
self.out_dir, save_optimizer=self.save_optimizer, **self.args)
if runner.meta is not None:
if self.by_epoch:
cur_ckpt_filename = self.args.get(
'filename_tmpl', 'epoch_{}.pth').format(runner.epoch + 1)
else:
cur_ckpt_filename = self.args.get(
'filename_tmpl', 'iter_{}.pth').format(runner.iter + 1)
runner.meta.setdefault('hook_msgs', dict())
runner.meta['hook_msgs']['last_ckpt'] = self.file_client.join_path(
self.out_dir, cur_ckpt_filename) # type: ignore
# remove other checkpoints
if self.max_keep_ckpts > 0:
if self.by_epoch:
name = 'epoch_{}.pth'
current_ckpt = runner.epoch + 1
else:
name = 'iter_{}.pth'
current_ckpt = runner.iter + 1
redundant_ckpts = range(
current_ckpt - self.max_keep_ckpts * self.interval, 0,
-self.interval)
filename_tmpl = self.args.get('filename_tmpl', name)
for _step in redundant_ckpts:
ckpt_path = self.file_client.join_path(
self.out_dir, filename_tmpl.format(_step)) # type: ignore
if self.file_client.isfile(ckpt_path):
self.file_client.remove(ckpt_path)
else:
break
def after_train_iter(self,
runner,
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.
data_batch (Sequence[Tuple[Any, BaseDataSample]], optional): Data
from dataloader. Defaults to None.
outputs (dict, optional): Outputs from model.
Defaults to None.
"""
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_iters(runner, self.interval) or \
(self.save_last and self.is_last_iter(runner, mode='train')):
runner.logger.info(f'Saving checkpoint at \
{runner.iter + 1} iterations')
if self.sync_buffer:
pass
# TODO
self._save_checkpoint(runner)