fix private function

deepspeech/training/trainer.py

@@ -151,12 +151,11 @@ class Trainer():
         resume training.
         """
         scratch = None
-        infos = self.checkpoint._load_parameters(
+        infos = self.checkpoint.load_latest_parameters(
             self.model,
             self.optimizer,
             checkpoint_dir=self.checkpoint_dir,
-            checkpoint_path=self.args.checkpoint_path,
-            checkpoint_file='checkpoint_latest')
+            checkpoint_path=self.args.checkpoint_path)
         if infos:
             # restore from ckpt
             self.iteration = infos["step"]

deepspeech/utils/checkpoint.py

@@ -38,23 +38,7 @@ class Checkpoint(object):
         self.kbest_n = kbest_n
         self.latest_n = latest_n
         self._save_all = (kbest_n == -1)
-
-    def _should_save_best(self, metric: float) -> bool:
-        if not self._best_full():
-            return True
-
-        # already full
-        worst_record_path = max(self.best_records, key=self.best_records.get)
-        # worst_record_path = max(self.best_records.iteritems(), key=operator.itemgetter(1))[0]
-        worst_metric = self.best_records[worst_record_path]
-        return metric < worst_metric
-
-    def _best_full(self):
-        return (not self._save_all) and len(self.best_records) == self.kbest_n
-
-    def _latest_full(self):
-        return len(self.latest_records) == self.latest_n
-
+    
     def add_checkpoint(self,
                        checkpoint_dir,
                        tag_or_iteration,
@@ -64,7 +48,7 @@ class Checkpoint(object):
                        metric_type="val_loss"):
         if (metric_type not in infos.keys()):
             self._save_parameters(checkpoint_dir, tag_or_iteration, model,
-                                 optimizer, infos)
+                                  optimizer, infos)
             return
 
         #save best
@@ -73,15 +57,71 @@ class Checkpoint(object):
                 infos[metric_type], checkpoint_dir, tag_or_iteration, model,
                 optimizer, infos)
         #save latest
-        self._save_latest_checkpoint_and_update(checkpoint_dir, tag_or_iteration,
-                                               model, optimizer, infos)
+        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)
 
+    def load_latest_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.
+        """
+        return self._load_parameters(model, optimizer, checkpoint_dir, checkpoint_path, 
+                                "checkpoint_latest")
+
+    def load_best_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.
+        """
+        return self._load_parameters(model, optimizer, checkpoint_dir, checkpoint_path, 
+                                "checkpoint_best")
+
+    def _should_save_best(self, metric: float) -> bool:
+        if not self._best_full():
+            return True
+
+        # already full
+        worst_record_path = max(self.best_records, key=self.best_records.get)
+        # worst_record_path = max(self.best_records.iteritems(), key=operator.itemgetter(1))[0]
+        worst_metric = self.best_records[worst_record_path]
+        return metric < worst_metric
+
+    def _best_full(self):
+        return (not self._save_all) and len(self.best_records) == self.kbest_n
+
+    def _latest_full(self):
+        return len(self.latest_records) == self.latest_n
+
     def _save_best_checkpoint_and_update(self, metric, checkpoint_dir,
-                                        tag_or_iteration, model, optimizer,
-                                        infos):
+                                         tag_or_iteration, model, optimizer,
+                                         infos):
         # remove the worst
         if self._best_full():
             worst_record_path = max(self.best_records,
@@ -93,8 +133,8 @@ class Checkpoint(object):
                 self._del_checkpoint(checkpoint_dir, worst_record_path)
 
         # add the new one
-        self._save_parameters(checkpoint_dir, tag_or_iteration, model, optimizer,
-                             infos)
+        self._save_parameters(checkpoint_dir, tag_or_iteration, model,
+                              optimizer, infos)
         self.best_records[tag_or_iteration] = metric
 
     def _save_latest_checkpoint_and_update(
@@ -108,8 +148,8 @@ class Checkpoint(object):
                 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,
-                             infos)
+        self._save_parameters(checkpoint_dir, tag_or_iteration, model,
+                              optimizer, infos)
 
     def _del_checkpoint(self, checkpoint_dir, tag_or_iteration):
         checkpoint_path = os.path.join(checkpoint_dir,
@@ -153,13 +193,12 @@ class Checkpoint(object):
             for i in self.latest_records:
                 handle.write("model_checkpoint_path:{}\n".format(i))
 
-
     def _load_parameters(self,
-                             model,
-                             optimizer=None,
-                             checkpoint_dir=None,
-                             checkpoint_path=None,
-                             checkpoint_file=None):
+                         model,
+                         optimizer=None,
+                         checkpoint_dir=None,
+                         checkpoint_path=None,
+                         checkpoint_file=None):
         """Load a last model checkpoint from disk. 
         Args:
             model (Layer): model to load parameters.
@@ -209,13 +248,14 @@ class Checkpoint(object):
                 configs = json.load(fin)
         return configs
 
+    
     @mp_tools.rank_zero_only
     def _save_parameters(self,
-                        checkpoint_dir: str,
-                        tag_or_iteration: Union[int, str],
-                        model: paddle.nn.Layer,
-                        optimizer: Optimizer=None,
-                        infos: dict=None):
+                         checkpoint_dir: str,
+                         tag_or_iteration: Union[int, str],
+                         model: paddle.nn.Layer,
+                         optimizer: Optimizer=None,
+                         infos: dict=None):
         """Checkpoint the latest trained model parameters.
         Args:
             checkpoint_dir (str): the directory where checkpoint is saved.