/
checkpoint.py
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/
checkpoint.py
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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import warnings
from typing import Optional
from mmcv.fileio import FileClient
from ..dist_utils import allreduce_params, master_only
from .hook import HOOKS, Hook
@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): 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``.
`Changed in version 1.3.16.`
max_keep_ckpts (int, optional): 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, optional): Whether to force the last checkpoint to be
saved regardless of interval. Default: True.
sync_buffer (bool, optional): 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.
`New in version 1.3.16.`
.. warning::
Before v1.3.16, the ``out_dir`` argument indicates the path where the
checkpoint is stored. However, since v1.3.16, ``out_dir`` indicates the
root directory and the final path to save checkpoint is the
concatenation of ``out_dir`` and the last level directory of
``runner.work_dir``. Suppose the value of ``out_dir`` is "/path/of/A"
and the value of ``runner.work_dir`` is "/path/of/B", then the final
path will be "/path/of/A/B".
"""
def __init__(self,
interval: int = -1,
by_epoch: bool = True,
save_optimizer: bool = True,
out_dir: Optional[str] = None,
max_keep_ckpts: int = -1,
save_last: bool = True,
sync_buffer: bool = False,
file_client_args: Optional[dict] = None,
**kwargs):
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):
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)
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):
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_epoch(runner)):
runner.logger.info(
f'Saving checkpoint at {runner.epoch + 1} epochs')
if self.sync_buffer:
allreduce_params(runner.model.buffers())
self._save_checkpoint(runner)
@master_only
def _save_checkpoint(self, runner):
"""Save the current checkpoint and delete unwanted checkpoint."""
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)
# 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))
if self.file_client.isfile(ckpt_path):
self.file_client.remove(ckpt_path)
else:
break
def after_train_iter(self, runner):
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)):
runner.logger.info(
f'Saving checkpoint at {runner.iter + 1} iterations')
if self.sync_buffer:
allreduce_params(runner.model.buffers())
self._save_checkpoint(runner)