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