backward-compat with tf1.0

tensorpack/tfutils/common.py

@@ -59,7 +59,13 @@ def get_global_step_var():
         "The global_step variable should be created under the root variable scope!"
     assert not scope.reuse, \
         "The global_step variable shouldn't be called under a reuse variable scope!"
-    var = training_util.get_or_create_global_step()
+    if get_tf_version_number() <= 1.0:
+        var = tf.get_variable('global_step',
+                              initializer=tf.constant(0, dtype=tf.int64),
+                              trainable=False, dtype=tf.int64)
+        tf.add_to_collection(tf.GraphKeys.GLOBAL_STEP, var)
+    else:
+        var = training_util.get_or_create_global_step()
     return var
 
 

tensorpack/train/feedfree.py

@@ -64,12 +64,14 @@ class SingleCostFeedfreeTrainer(FeedfreeTrainerBase):
         """ get the cost and gradient"""
         self.build_train_tower()
         cost = self.model.get_cost()    # assume single cost
+        # opt may be created under first-tower variable scope (which is '')
         opt = self.model.get_optimizer()
         # GATE_NONE faster?
         varlist = tf.trainable_variables()
         ctx = get_current_tower_context()
         if ctx is not None and ctx.has_own_variables and ctx.vs_name:
             # only optimize w.r.t vars in this tower
+            # TODO assumption on the first-tower empty variable scope
             varlist = [v for v in varlist if v.op.name.startswith(ctx.vs_name + '/')]
         grads = opt.compute_gradients(
             cost,