annotation optimized and code style optimized

python/paddle/fluid/io.py

@@ -478,9 +478,10 @@ def save_checkpoint(executor,
 
     :param executor
     :param checkpoint_dir
+    :param trainer_id
+    :param is_chief
     :param main_program
     :param max_num_checkpoints
-    :param is_chief
     """
     if checkpoint_dir is None:
         raise ValueError("The values of 'checkpoint_dir' should not be None")
@@ -502,6 +503,11 @@ def save_checkpoint(executor,
 
 
 def need_load_checkpoint(checkpoint_dir):
+    """
+    If the directory have checkpoint files, it will return lastest checkpoint directory serial number
+
+    :param checkpoint_dir
+    """
     serial = _get_lastest_checkpoint_dir(checkpoint_dir)
     if serial < 0:
         return None
@@ -515,6 +521,7 @@ def load_checkpoint(executor, checkpoint_dir, serial, main_program):
 
     :param executor
     :param checkpoint_dir
+    :param serial
     :param main_program
     """
 
@@ -536,7 +543,11 @@ def clean_checkpoint(checkpoint_dir, delete_dir=False):
     """
     clean the checkpoint dir, when the train exits normally, the trainer will call clean_checkpoint to delete checkpoint directory saved before.
     delete_dir only works when the directory is empty, otherwise, OSError is raised.  
+
+    :param checkpoint_dir
+    :param delete_dir
     """
+
     if checkpoint_dir is None:
         raise ValueError("The values of 'checkpoint_dir' should not be None")
     _lru_delete(checkpoint_dir, max_num_checkpoints=0)
@@ -549,6 +560,11 @@ def load_persist_vars_without_grad(executor, dirname, program, nest=True):
     """
     load_persist_vars_without_grad will load variables from a directory by an executor,
     the variable named end with "@GRAD" will not be loaded.
+
+    :param executor
+    :param dirname
+    :param program
+    :param nest
     """
 
     if nest:
@@ -566,6 +582,10 @@ def save_persist_vars_without_grad(executor, dirname, program):
     """
     save_persist_vars_without_grad  will save variables to a directory by an executor,
     the variable named end with "@GRAD" will not be saved.
+
+    :param executor
+    :param dirname
+    :param program
     """
     cur_dir = _get_model_dir(dirname)
     save_vars(

python/paddle/fluid/trainer.py

@@ -79,8 +79,8 @@ class CheckpointConfig(object):
         else:
             self.step_interval = step_interval
 
-        self._epoch_id = 0
-        self._step_id = 0
+        self.epoch_id = 0
+        self.step_id = 0
         self._load_serial = None
 
 
@@ -185,8 +185,8 @@ class Trainer(object):
             epoch_id, step_id = io.load_trainer_args(
                 self.checkpoint.checkpoint_dir, self.checkpoint._load_serial,
                 self.trainer_id, ["epoch_id", "step_id"])
-            self.checkpoint._epoch_id = int(epoch_id)
-            self.checkpoint._step_id = int(step_id)
+            self.checkpoint.epoch_id = int(epoch_id)
+            self.checkpoint.step_id = int(step_id)
 
         if param_path and os.path.isdir(param_path):
             # load params from param_path into scope
@@ -353,7 +353,7 @@ class Trainer(object):
     def _train_by_any_executor(self, event_handler, exe, num_epochs, reader):
         epochs = [
             epoch_id for epoch_id in range(num_epochs)
-            if epoch_id >= self.checkpoint._epoch_id
+            if epoch_id >= self.checkpoint.epoch_id
         ]
         for epoch_id in epochs:
             event_handler(BeginEpochEvent(epoch_id))
@@ -363,7 +363,7 @@ class Trainer(object):
                     return
 
                 if self.checkpoint and self.checkpoint._load_serial \
-                    and self.checkpoint._step_id >= step_id and self.checkpoint._epoch_id == epoch_id:
+                    and self.checkpoint.step_id >= step_id and self.checkpoint.epoch_id == epoch_id:
                     continue
 
                 begin_event = BeginStepEvent(epoch_id, step_id)