msb-public
/
PaddleSpeech
mirror of https://github.com/PaddlePaddle/PaddleSpeech
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optimize the function

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pull/680/head
Haoxin Ma 3 years ago
parent 16210c0587
commit 6d92417edd
2 changed files with 32 additions and 82 deletions
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5
deepspeech/training/trainer.py
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@ -151,11 +151,12 @@ class Trainer():
resume training.
"""
scratch = None
infos = self.checkpoint.load_last_parameters(
infos = self.checkpoint._load_parameters(
self.model,
self.optimizer,
checkpoint_dir=self.checkpoint_dir,
checkpoint_path=self.args.checkpoint_path)
checkpoint_path=self.args.checkpoint_path,
checkpoint_file='checkpoint_latest')
if infos:
# restore from ckpt
self.iteration = infos["step"]

109
deepspeech/utils/checkpoint.py
Unescape View File

@ -39,8 +39,8 @@ class Checkpoint(object):
self.latest_n = latest_n
self._save_all = (kbest_n == -1)
def should_save_best(self, metric: float) -> bool:
if not self.best_full():
def _should_save_best(self, metric: float) -> bool:
if not self._best_full():
return True
# already full
@ -49,10 +49,10 @@ class Checkpoint(object):
worst_metric = self.best_records[worst_record_path]
return metric < worst_metric
def best_full(self):
def _best_full(self):
return (not self._save_all) and len(self.best_records) == self.kbest_n
def latest_full(self):
def _latest_full(self):
return len(self.latest_records) == self.latest_n
def add_checkpoint(self,
@ -63,62 +63,62 @@ class Checkpoint(object):
infos,
metric_type="val_loss"):
if (metric_type not in infos.keys()):
self.save_parameters(checkpoint_dir, tag_or_iteration, model,
self._save_parameters(checkpoint_dir, tag_or_iteration, model,
optimizer, infos)
return
#save best
if self.should_save_best(infos[metric_type]):
self.save_best_checkpoint_and_update(
if self._should_save_best(infos[metric_type]):
self._save_best_checkpoint_and_update(
infos[metric_type], checkpoint_dir, tag_or_iteration, model,
optimizer, infos)
#save latest
self.save_latest_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
self._save_latest_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
model, optimizer, infos)
if isinstance(tag_or_iteration, int):
self.save_checkpoint_record(checkpoint_dir, tag_or_iteration)
self._save_checkpoint_record(checkpoint_dir, tag_or_iteration)
def save_best_checkpoint_and_update(self, metric, checkpoint_dir,
def _save_best_checkpoint_and_update(self, metric, checkpoint_dir,
tag_or_iteration, model, optimizer,
infos):
# remove the worst
if self.best_full():
if self._best_full():
worst_record_path = max(self.best_records,
key=self.best_records.get)
self.best_records.pop(worst_record_path)
if (worst_record_path not in self.latest_records):
logger.info(
"remove the worst checkpoint: {}".format(worst_record_path))
self.del_checkpoint(checkpoint_dir, worst_record_path)
self._del_checkpoint(checkpoint_dir, worst_record_path)
# add the new one
self.save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
self._save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
infos)
self.best_records[tag_or_iteration] = metric
def save_latest_checkpoint_and_update(
def _save_latest_checkpoint_and_update(
self, checkpoint_dir, tag_or_iteration, model, optimizer, infos):
# remove the old
if self.latest_full():
if self._latest_full():
to_del_fn = self.latest_records.pop(0)
if (to_del_fn not in self.best_records.keys()):
logger.info(
"remove the latest checkpoint: {}".format(to_del_fn))
self.del_checkpoint(checkpoint_dir, to_del_fn)
self._del_checkpoint(checkpoint_dir, to_del_fn)
self.latest_records.append(tag_or_iteration)
self.save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
self._save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
infos)
def del_checkpoint(self, checkpoint_dir, tag_or_iteration):
def _del_checkpoint(self, checkpoint_dir, tag_or_iteration):
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(tag_or_iteration))
for filename in glob.glob(checkpoint_path + ".*"):
os.remove(filename)
logger.info("delete file: {}".format(filename))
def load_checkpoint_idx(self, checkpoint_record: str) -> int:
def _load_checkpoint_idx(self, checkpoint_record: str) -> int:
"""Get the iteration number corresponding to the latest saved checkpoint.
Args:
checkpoint_path (str): the saved path of checkpoint.
@ -134,7 +134,7 @@ class Checkpoint(object):
iteration = int(latest_checkpoint.split(":")[-1])
return iteration
def save_checkpoint_record(self, checkpoint_dir: str, iteration: int):
def _save_checkpoint_record(self, checkpoint_dir: str, iteration: int):
"""Save the iteration number of the latest model to be checkpoint record.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
@ -153,65 +153,13 @@ class Checkpoint(object):
for i in self.latest_records:
handle.write("model_checkpoint_path:{}\n".format(i))
def load_last_parameters(self,
model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
"""Load a last model checkpoint from disk.
Args:
model (Layer): model to load parameters.
optimizer (Optimizer, optional): optimizer to load states if needed.
Defaults to None.
checkpoint_dir (str, optional): the directory where checkpoint is saved.
checkpoint_path (str, optional): if specified, load the checkpoint
stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
Returns:
configs (dict): epoch or step, lr and other meta info should be saved.
"""
configs = {}
if checkpoint_path is not None:
tag = os.path.basename(checkpoint_path).split(":")[-1]
elif checkpoint_dir is not None:
checkpoint_record = os.path.join(checkpoint_dir,
"checkpoint_latest")
iteration = self.load_checkpoint_idx(checkpoint_record)
if iteration == -1:
return configs
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
)
rank = dist.get_rank()
params_path = checkpoint_path + ".pdparams"
model_dict = paddle.load(params_path)
model.set_state_dict(model_dict)
logger.info("Rank {}: loaded model from {}".format(rank, params_path))
optimizer_path = checkpoint_path + ".pdopt"
if optimizer and os.path.isfile(optimizer_path):
optimizer_dict = paddle.load(optimizer_path)
optimizer.set_state_dict(optimizer_dict)
logger.info("Rank {}: loaded optimizer state from {}".format(
rank, optimizer_path))
info_path = re.sub('.pdparams$', '.json', params_path)
if os.path.exists(info_path):
with open(info_path, 'r') as fin:
configs = json.load(fin)
return configs
def load_best_parameters(self,
def _load_parameters(self,
model,
optimizer=None,
checkpoint_dir=None,
checkpoint_path=None):
checkpoint_path=None,
checkpoint_file=None):
"""Load a last model checkpoint from disk.
Args:
model (Layer): model to load parameters.
@ -221,6 +169,7 @@ class Checkpoint(object):
checkpoint_path (str, optional): if specified, load the checkpoint
stored in the checkpoint_path(prefix) and the argument 'checkpoint_dir' will
be ignored. Defaults to None.
checkpoint_file "checkpoint_latest" or "checkpoint_best"
Returns:
configs (dict): epoch or step, lr and other meta info should be saved.
"""
@ -228,16 +177,16 @@ class Checkpoint(object):
if checkpoint_path is not None:
tag = os.path.basename(checkpoint_path).split(":")[-1]
elif checkpoint_dir is not None:
checkpoint_record = os.path.join(checkpoint_dir, "checkpoint_best")
iteration = self.load_checkpoint_idx(checkpoint_record)
elif checkpoint_dir is not None and checkpoint_file is not None:
checkpoint_record = os.path.join(checkpoint_dir, checkpoint_file)
iteration = self._load_checkpoint_idx(checkpoint_record)
if iteration == -1:
return configs
checkpoint_path = os.path.join(checkpoint_dir,
"{}".format(iteration))
else:
raise ValueError(
"At least one of 'checkpoint_dir' and 'checkpoint_path' should be specified!"
"At least one of 'checkpoint_dir' and 'checkpoint_file' and 'checkpoint_path' should be specified!"
)
rank = dist.get_rank()
@ -261,7 +210,7 @@ class Checkpoint(object):
return configs
@mp_tools.rank_zero_only
def save_parameters(self,
def _save_parameters(self,
checkpoint_dir: str,
tag_or_iteration: Union[int, str],
model: paddle.nn.Layer,

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