Change trigger_epoch to trigger in CallbackFactory

tensorpack/callbacks/base.py

@@ -5,6 +5,7 @@
 import tensorflow as tf
 from abc import ABCMeta
 import six
+from ..utils.develop import log_deprecated
 from ..tfutils.common import get_op_or_tensor_by_name
 
 __all__ = ['Callback', 'ProxyCallback', 'CallbackFactory', 'Triggerable']
@@ -255,17 +256,23 @@ class CallbackFactory(Callback):
     """
     Create a callback with some lambdas.
     """
-    def __init__(self, setup_graph=None, before_train=None,
-                 trigger_epoch=None, after_train=None):
+    def __init__(self, setup_graph=None, before_train=None, trigger=None,
+                 after_train=None, trigger_epoch=None):
         """
         Each lambda takes ``self`` as the only argument.
+
+        trigger_epoch was deprecated.
         """
 
         self._cb_setup_graph = setup_graph
         self._cb_before_train = before_train
-        self._cb_trigger_epoch = trigger_epoch
+        self._cb_trigger = trigger
         self._cb_after_train = after_train
 
+        if trigger_epoch:
+            self._cb_trigger = trigger_epoch
+            log_deprecated("CallbackFactory(trigger_epoch=)", "Use trigger instead.", "2017-11-15")
+
     def _setup_graph(self):
         if self._cb_setup_graph:
             self._cb_setup_graph(self)
@@ -274,9 +281,9 @@ class CallbackFactory(Callback):
         if self._cb_before_train:
             self._cb_before_train(self)
 
-    def _trigger_epoch(self):
-        if self._cb_trigger_epoch:
-            self._cb_trigger_epoch(self)
+    def _trigger(self):
+        if self._cb_trigger:
+            self._cb_trigger(self)
 
     def _after_train(self):
         if self._cb_after_train:

tensorpack/callbacks/group.py

@@ -98,7 +98,3 @@ class Callbacks(Callback):
     def _after_epoch(self):
         for cb in self.cbs:
             cb.after_epoch()
-
-    def append(self, cb):
-        assert isinstance(cb, Callback)
-        self.cbs.append(cb)

tensorpack/tfutils/summary.py

@@ -163,13 +163,14 @@ def add_moving_summary(v, *args, **kwargs):
     for x in v:
         assert isinstance(x, tf.Tensor), x
         assert x.get_shape().ndims == 0, x.get_shape()
+    gs = get_global_step_var()
     # TODO will produce variable tower0/xxx?
     # TODO not saved under distributed
     # TODO use zero_debias
     # TODO create EMA for each variable separately, so that the maintain ops
     # have a decent name (rather than EMA)
-    gs = get_global_step_var()
-    with tf.device(gs.device):
+    # clear namescope, otherwise the variable names will have duplicated name scope
+    with tf.name_scope(None), tf.device(gs.device):
         averager = tf.train.ExponentialMovingAverage(
             decay, num_updates=gs, name='EMA')
         avg_maintain_op = averager.apply(v)