update docs about tensor access

docs/tutorial/extend/callback.md

@@ -87,7 +87,16 @@ to let this method run every k steps or every k epochs.
 ### What you can do in the callback
 
 * Access tensors / ops in either training / inference mode (need to create them in `_setup_graph`).
-	`self.trainer.get_predictor` is a helper function to create a callable under inference mode.
+	* Use TF methods such as `self.graph.get_tensor_by_name`, to access tensors.
+
+	If you're using a `TowerTrainer` instance, more tools are available:
+	* Use `self.trainer.tower_func.towers` to access the
+		[tower handles](http://tensorpack.readthedocs.io/en/latest/modules/tfutils.html#tensorpack.tfutils.tower.TowerTensorHandles),
+		and therefore the tensors in each tower.
+	* [self.get_tensors_maybe_in_tower()](http://tensorpack.readthedocs.io/en/latest/modules/callbacks.html#tensorpack.callbacks.Callback.get_tensors_maybe_in_tower)
+		is a helper function to access tensors in the first training tower.
+	* [self.trainer.get_predictor()](http://tensorpack.readthedocs.io/en/latest/modules/train.html#tensorpack.train.TowerTrainer.get_predictor)
+		is a helper function to create a callable under inference mode.
 * Write stuff to the monitor backend, by `self.trainer.monitors.put_xxx`.
 	The monitors might direct your events to TensorFlow events file, JSON file, stdout, etc.
 	You can get history monitor data as well. See the docs for [Monitors](../../modules/callbacks.html#tensorpack.callbacks.Monitors)

tensorpack/callbacks/base.py

@@ -207,9 +207,12 @@ class Callback(object):
 
     def get_tensors_maybe_in_tower(self, names):
         """
-        Get tensors in the graph.
-        Will automatically check for the __first training tower__
-        if no tensor with the given name exists.
+        Get tensors in the graph with the given names.
+        Will automatically check for the *first training tower*
+        if no existing tensor is found with the name.
+
+        Returns:
+            [tf.Tensor]
         """
         from ..train.tower import TowerTrainer  # noqa
 

tensorpack/tfutils/tower.py

@@ -80,24 +80,6 @@ class TowerContext(object):
     def ns_name(self):
         return self._name
 
-    # TODO another method to filter by ns_name
-    def filter_vars_by_vs_name(self, varlist):
-        """
-        Filter the list and only keep those under the current variable scope.
-        If this tower doesn't contain its own variable scope, return the list as-is.
-
-        Args:
-            varlist (list[tf.Variable] or list[tf.Tensor]):
-        """
-        if not self.has_own_variables:
-            return varlist
-        if len(self._vs_name) == 0:
-            # main_training_tower with no name. assume no other towers has
-            # been built yet, then varlist contains vars only in the first tower.
-            return varlist
-        prefix = self._vs_name + '/'
-        return [v for v in varlist if v.op.name.startswith(prefix)]
-
     def get_collection_in_tower(self, key):
         """
         Get items from this collection that are added in the current tower.