get_collection in TowerTensorHandle

README.md

@@ -36,7 +36,6 @@ We refuse toy examples.
 Instead of showing you 10 arbitrary networks trained on toy datasets,
 [tensorpack examples](examples) faithfully replicate papers and care about reproducing numbers,
 demonstrating its flexibility for actual research.
-Some highlights:
 
 ### Vision:
 + [Train ResNet](examples/ResNet) and [other models](examples/ImageNetModels) on ImageNet.
@@ -57,7 +56,6 @@ Some highlights:
 + [char-rnn for fun](examples/Char-RNN)
 + [LSTM language model on PennTreebank](examples/PennTreebank)
 
-
 ## Install:
 
 Dependencies:

tensorpack/callbacks/saver.py

@@ -98,6 +98,7 @@ class MinSaver(Callback):
             reverse (bool): if True, will save the maximum.
             filename (str): the name for the saved model.
                 Defaults to ``min-{monitor_stat}.tfmodel``.
+            checkpoint_dir (str): the directory containing checkpoints.
 
         Example:
             Save the model with minimum validation error to
@@ -108,9 +109,8 @@ class MinSaver(Callback):
                 MinSaver('val-error')
 
         Notes:
-            It assumes that :class:`ModelSaver` is used with
-            the same ``checkpoint_dir``. And it will save
-            the model to that directory as well.
+            It assumes that :class:`ModelSaver` is used with the same ``checkpoint_dir``
+            and appears earlier in the callback list.
             The default for both :class:`ModelSaver` and :class:`MinSaver`
             is ``checkpoint_dir=logger.get_logger_dir()``
         """

tensorpack/tfutils/tower.py

@@ -333,6 +333,15 @@ class TowerTensorHandle(object):
             name_with_vs = name
         return get_op_or_tensor_by_name(name_with_vs)
 
+    def get_collection(self, name):
+        """
+        Get items from a collection that are added in this tower.
+
+        Args:
+            name (str): the name of the collection
+        """
+        return self._ctx.get_collection_in_tower(name)
+
     @property
     def input(self):
         """