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""" Checkpoint Saver
Track top-n training checkpoints and maintain recovery checkpoints on specified intervals.
Hacked together by / Copyright 2020 Ross Wightman
"""
import glob
import logging
import operator
import os
import shutil
import torch
from .model import unwrap_model, get_state_dict
_logger = logging.getLogger(__name__)
class CheckpointSaver:
def __init__(
self,
model,
optimizer,
args=None,
model_ema=None,
amp_scaler=None,
checkpoint_prefix='checkpoint',
recovery_prefix='recovery',
checkpoint_dir='',
recovery_dir='',
decreasing=False,
max_history=10,
unwrap_fn=unwrap_model
):
# objects to save state_dicts of
self.model = model
self.optimizer = optimizer
self.args = args
self.model_ema = model_ema
self.amp_scaler = amp_scaler
# state
self.checkpoint_files = [] # (filename, metric) tuples in order of decreasing betterness
self.best_epoch = None
self.best_metric = None
self.curr_recovery_file = ''
self.prev_recovery_file = ''
self.can_hardlink = True
# config
self.checkpoint_dir = checkpoint_dir
self.recovery_dir = recovery_dir
self.save_prefix = checkpoint_prefix
self.recovery_prefix = recovery_prefix
self.extension = '.pth.tar'
self.decreasing = decreasing # a lower metric is better if True
self.cmp = operator.lt if decreasing else operator.gt # True if lhs better than rhs
self.max_history = max_history
self.unwrap_fn = unwrap_fn
assert self.max_history >= 1
def _replace(self, src, dst):
if self.can_hardlink:
try:
if os.path.exists(dst):
os.unlink(dst) # required for Windows support.
except (OSError, NotImplementedError) as e:
self.can_hardlink = False
os.replace(src, dst)
def _duplicate(self, src, dst):
if self.can_hardlink:
try:
if os.path.exists(dst):
# for Windows
os.unlink(dst)
os.link(src, dst)
return
except (OSError, NotImplementedError) as e:
self.can_hardlink = False
shutil.copy2(src, dst)
def _save(self, save_path, epoch, metric=None):
save_state = {
'epoch': epoch,
'arch': type(self.model).__name__.lower(),
'state_dict': get_state_dict(self.model, self.unwrap_fn),
'optimizer': self.optimizer.state_dict(),
'version': 2, # version < 2 increments epoch before save
}
if self.args is not None:
save_state['arch'] = self.args.model
save_state['args'] = self.args
if self.amp_scaler is not None:
save_state[self.amp_scaler.state_dict_key] = self.amp_scaler.state_dict()
if self.model_ema is not None:
save_state['state_dict_ema'] = get_state_dict(self.model_ema, self.unwrap_fn)
if metric is not None:
save_state['metric'] = metric
torch.save(save_state, save_path)
def _cleanup_checkpoints(self, trim=0):
trim = min(len(self.checkpoint_files), trim)
delete_index = self.max_history - trim
if delete_index < 0 or len(self.checkpoint_files) <= delete_index:
return
to_delete = self.checkpoint_files[delete_index:]
for d in to_delete:
try:
_logger.debug("Cleaning checkpoint: {}".format(d))
os.remove(d[0])
except Exception as e:
_logger.error("Exception '{}' while deleting checkpoint".format(e))
self.checkpoint_files = self.checkpoint_files[:delete_index]
def save_checkpoint(self, epoch, metric=None):
assert epoch >= 0
tmp_save_path = os.path.join(self.checkpoint_dir, 'tmp' + self.extension)
last_save_path = os.path.join(self.checkpoint_dir, 'last' + self.extension)
self._save(tmp_save_path, epoch, metric)
self._replace(tmp_save_path, last_save_path)
worst_file = self.checkpoint_files[-1] if self.checkpoint_files else None
if (
len(self.checkpoint_files) < self.max_history
or metric is None
or self.cmp(metric, worst_file[1])
):
if len(self.checkpoint_files) >= self.max_history:
self._cleanup_checkpoints(1)
filename = '-'.join([self.save_prefix, str(epoch)]) + self.extension
save_path = os.path.join(self.checkpoint_dir, filename)
self._duplicate(last_save_path, save_path)
self.checkpoint_files.append((save_path, metric))
self.checkpoint_files = sorted(
self.checkpoint_files,
key=lambda x: x[1],
reverse=not self.decreasing # sort in descending order if a lower metric is not better
)
checkpoints_str = "Current checkpoints:\n"
for c in self.checkpoint_files:
checkpoints_str += ' {}\n'.format(c)
_logger.info(checkpoints_str)
if metric is not None and (self.best_metric is None or self.cmp(metric, self.best_metric)):
self.best_epoch = epoch
self.best_metric = metric
best_save_path = os.path.join(self.checkpoint_dir, 'model_best' + self.extension)
self._duplicate(last_save_path, best_save_path)
return (None, None) if self.best_metric is None else (self.best_metric, self.best_epoch)
def save_recovery(self, epoch, batch_idx=0):
assert epoch >= 0
tmp_save_path = os.path.join(self.recovery_dir, 'recovery_tmp' + self.extension)
self._save(tmp_save_path, epoch)
filename = '-'.join([self.recovery_prefix, str(epoch), str(batch_idx)]) + self.extension
save_path = os.path.join(self.recovery_dir, filename)
self._replace(tmp_save_path, save_path)
if os.path.exists(self.prev_recovery_file):
try:
_logger.debug("Cleaning recovery: {}".format(self.prev_recovery_file))
os.remove(self.prev_recovery_file)
except Exception as e:
_logger.error("Exception '{}' while removing {}".format(e, self.prev_recovery_file))
self.prev_recovery_file = self.curr_recovery_file
self.curr_recovery_file = save_path
def find_recovery(self):
recovery_path = os.path.join(self.recovery_dir, self.recovery_prefix)
files = glob.glob(recovery_path + '*' + self.extension)
files = sorted(files)
return files[0] if len(files) else ''
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