Let ModelSaver follows the official Saver API.

tensorpack/callbacks/saver.py

@@ -9,6 +9,7 @@ import glob
 
 from .base import Callback
 from ..utils import logger
+from ..utils.develop import log_deprecated
 from ..tfutils.common import get_tf_version_number
 
 __all__ = ['ModelSaver', 'MinSaver', 'MaxSaver']
@@ -19,18 +20,26 @@ class ModelSaver(Callback):
     Save the model every epoch.
     """
 
-    def __init__(self, keep_recent=10, keep_freq=0.5,
+    def __init__(self, max_to_keep=10,
+                 keep_checkpoint_every_n_hours=0.5,
                  checkpoint_dir=None,
-                 var_collections=tf.GraphKeys.GLOBAL_VARIABLES):
+                 var_collections=tf.GraphKeys.GLOBAL_VARIABLES,
+                 keep_recent=None, keep_freq=None):
         """
         Args:
-            keep_recent(int): see ``tf.train.Saver`` documentation.
-            keep_freq(int): see ``tf.train.Saver`` documentation.
+            max_to_keep, keep_checkpoint_every_n_hours(int): the same as in ``tf.train.Saver``.
             checkpoint_dir (str): Defaults to ``logger.LOG_DIR``.
             var_collections (str or list of str): collection of the variables (or list of collections) to save.
         """
-        self.keep_recent = keep_recent
-        self.keep_freq = keep_freq
+        self._max_to_keep = max_to_keep
+        self._keep_every_n_hours = keep_checkpoint_every_n_hours
+        if keep_recent is not None or keep_freq is not None:
+            log_deprecated("ModelSaver(keep_recent=, keep_freq=)", "Use max_to_keep and keep_checkpoint_every_n_hours!")
+            if keep_recent is not None:
+                self._max_to_keep = keep_recent
+            if keep_freq is not None:
+                self._keep_every_n_hours = keep_freq
+
         if not isinstance(var_collections, list):
             var_collections = [var_collections]
         self.var_collections = var_collections
@@ -48,14 +57,14 @@ class ModelSaver(Callback):
         if get_tf_version_number() <= 1.1:
             self.saver = tf.train.Saver(
                 var_list=vars,
-                max_to_keep=self.keep_recent,
-                keep_checkpoint_every_n_hours=self.keep_freq,
+                max_to_keep=self._max_to_keep,
+                keep_checkpoint_every_n_hours=self._keep_every_n_hours,
                 write_version=tf.train.SaverDef.V2)
         else:
             self.saver = tf.train.Saver(
                 var_list=vars,
-                max_to_keep=self.keep_recent,
-                keep_checkpoint_every_n_hours=self.keep_freq,
+                max_to_keep=self._max_to_keep,
+                keep_checkpoint_every_n_hours=self._keep_every_n_hours,
                 write_version=tf.train.SaverDef.V2,
                 save_relative_paths=True)
         self.meta_graph_written = False