optimize the function

deepspeech/training/trainer.py

@@ -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"]

deepspeech/utils/checkpoint.py

@@ -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,