always reuse when not training. otherwise reuse can be set back to False in TF<1.1 (fix #277)

tensorpack/callbacks/inference_runner.py

@@ -109,10 +109,9 @@ class InferenceRunnerBase(Callback):
         in_tensors = self._find_input_tensors()
         assert isinstance(in_tensors, list), in_tensors
 
-        with tf.variable_scope(tf.get_variable_scope(), reuse=True):
-            def fn(_):
-                self.trainer.model.build_graph(in_tensors)
-            PredictorTowerBuilder(fn, self._prefix).build(self._predict_tower_id)
+        def fn(_):
+            self.trainer.model.build_graph(in_tensors)
+        PredictorTowerBuilder(fn, self._prefix).build(self._predict_tower_id)
 
         self._feed_tensors = self._find_feed_tensors()
         self._hooks = [self._build_hook(inf) for inf in self.infs]

tensorpack/tfutils/tower.py

@@ -107,8 +107,9 @@ class TowerContext(object):
                 self._ctxs.append(tf.variable_scope(self._name))
             else:
                 # use existing variable scope
+                reuse = self.index > 0 or (not self.is_training)
                 self._ctxs.append(tf.variable_scope(
-                    tf.get_variable_scope(), reuse=self.index > 0))
+                    tf.get_variable_scope(), reuse=reuse))
                 self._ctxs.append(tf.name_scope(self._name))
         self._ctxs.append(tf.device(self._device))
         for c in self._ctxs: