MinSaver takes into account checkpoint_dir. (#520)

tensorpack/callbacks/saver.py

@@ -89,31 +89,33 @@ class MinSaver(Callback):
     """
     Separately save the model with minimum value of some statistics.
     """
-    def __init__(self, monitor_stat, reverse=False, filename=None):
+    def __init__(self, monitor_stat, reverse=False, filename=None, checkpoint_dir=None):
         """
         Args:
             monitor_stat(str): the name of the statistics.
             reverse (bool): if True, will save the maximum.
             filename (str): the name for the saved model.
                 Defaults to ``min-{monitor_stat}.tfmodel``.
-
         Example:
             Save the model with minimum validation error to
             "min-val-error.tfmodel":
-
             .. code-block:: python
-
                 MinSaver('val-error')
-
         Note:
             It assumes that :class:`ModelSaver` is used with
-            ``checkpoint_dir=logger.get_logger_dir()`` (the default). And it will save
+            the same ``checkpoint_dir``. And it will save
             the model to that directory as well.
+            The default for both :class:`ModelSaver` and :class:`MinSaver`
+            is ``checkpoint_dir=logger.get_logger_dir()``
+
         """
         self.monitor_stat = monitor_stat
         self.reverse = reverse
         self.filename = filename
         self.min = None
+        self.checkpoint_dir = checkpoint_dir
+        if self.checkpoint_dir is None:
+            self.checkpoint_dir = logger.get_logger_dir()
 
     def _get_stat(self):
         try:
@@ -135,13 +137,13 @@ class MinSaver(Callback):
                 self._save()
 
     def _save(self):
-        ckpt = tf.train.get_checkpoint_state(logger.get_logger_dir())
+        ckpt = tf.train.get_checkpoint_state(self.checkpoint_dir)
         if ckpt is None:
             raise RuntimeError(
                 "Cannot find a checkpoint state. Do you forget to use ModelSaver?")
         path = ckpt.model_checkpoint_path
 
-        newname = os.path.join(logger.get_logger_dir(),
+        newname = os.path.join(self.checkpoint_dir,
                                self.filename or
                                ('max-' + self.monitor_stat if self.reverse else 'min-' + self.monitor_stat))
         files_to_copy = tf.gfile.Glob(path + '*')
@@ -155,11 +157,11 @@ class MaxSaver(MinSaver):
     """
     Separately save the model with maximum value of some statistics.
     """
-    def __init__(self, monitor_stat, filename=None):
+    def __init__(self, monitor_stat, filename=None, checkpoint_dir=None):
         """
         Args:
             monitor_stat(str): the name of the statistics.
             filename (str): the name for the saved model.
                 Defaults to ``max-{monitor_stat}.tfmodel``.
         """
-        super(MaxSaver, self).__init__(monitor_stat, True, filename=filename)
+        super(MaxSaver, self).__init__(monitor_stat, True, filename=filename, checkpoint_dir=checkpoint_dir)