Merge changes from github.

PiperOrigin-RevId: 203037623

2 files changed, 10 insertions, 10 deletions

diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms.py b/tensorflow/contrib/quantize/python/fold_batch_norms.py
index 804cd8d72d..e3c4899830 100644
--- a/tensorflow/contrib/quantize/python/fold_batch_norms.py
+++ b/tensorflow/contrib/quantize/python/fold_batch_norms.py
@@ -506,7 +506,7 @@ def _FoldUnfusedBatchNorms(graph, is_training, freeze_batch_norm_delay):
 def _IsValidUnfusedBatchNorm(graph, context):
   """Checks that the output of the unfused batch norm has consumers."""
   add_shift = graph.get_operation_by_name(
-      context + '/BatchNorm/batchnorm/add_1')
+      context + '/BatchNorm/batchnorm_1/add_1')
   # Ensure that the output tensor of batch norm has consumers, otherwise this
   # is a dangling node and not a match.
   return bool(add_shift.outputs[0].consumers())
@@ -599,7 +599,7 @@ def _GetBatchNormParams(graph, context, has_scaling):
 
   op_suffix_mean = '/BatchNorm/moments/Squeeze'
   op_suffix_variance = '/BatchNorm/moments/Squeeze_1'
-  op_suffix_epsilon = '/BatchNorm/batchnorm/add/y'
+  op_suffix_epsilon = '/BatchNorm/batchnorm_1/add/y'
   op_suffix_bn_decay_mean = '/BatchNorm/AssignMovingAvg/decay'
   op_suffix_bn_decay_var = '/BatchNorm/AssignMovingAvg_1/decay'
 
@@ -675,12 +675,12 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
 
   Returns:
     A pair of Operations, the first is the original consumer node of the batch
-      norm (../BatchNorm/batchnorm/add_1), the second is the consumer node of
+      norm (../BatchNorm/batchnorm_1/add_1), the second is the consumer node of
       the folded graph (add_fold).
   """
   mul_scale_name = 'mul_1' if has_scaling else 'mul'
   mul_scale = graph.get_operation_by_name(context +
-                                          '/BatchNorm/batchnorm/' +
+                                          '/BatchNorm/batchnorm_1/' +
                                           mul_scale_name)
   op_below = mul_scale.inputs[0].op
   # Skip over the BatchToSpace operation in the case of atrous convolutions.
@@ -707,7 +707,7 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
     ]
     scale_name = 'mul' if has_scaling else 'Rsqrt'
     scale = graph.get_operation_by_name(
-        context + '/BatchNorm/batchnorm/' + scale_name)
+        context + '/BatchNorm/batchnorm_1/' + scale_name)
     scale = array_ops.reshape(scale.outputs[0], new_shape,
                               context + '/scale_reshape')
 
@@ -735,7 +735,7 @@ def _CreateFoldedOp(graph, context, has_scaling, freeze_batch_norm_delay,
                                [(1, mul_fold.outputs[0])])
 
   add_shift = graph.get_operation_by_name(
-      context + '/BatchNorm/batchnorm/add_1')
+      context + '/BatchNorm/batchnorm_1/add_1')
 
   corrected_output = conv_or_fc_folded.outputs[0]
   # Copy the batch to space operation if we have a atrous convolution.
@@ -930,7 +930,7 @@ def _HasScaling(graph, input_to_ops_map, bn):
   Returns:
     A boolean indicating whether this batch norm layer has scaling enabled.
   """
-  rsqrt_op = graph.get_operation_by_name(bn + '/BatchNorm/batchnorm/Rsqrt')
+  rsqrt_op = graph.get_operation_by_name(bn + '/BatchNorm/batchnorm_1/Rsqrt')
   rsqrt_consumers = input_to_ops_map.ConsumerOperations(rsqrt_op)
 
   return sum(1 for op in rsqrt_consumers if op.type == 'Mul') == 1
diff --git a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py
index 272afcdf07..7c907ffd92 100644
--- a/tensorflow/contrib/quantize/python/fold_batch_norms_test.py
+++ b/tensorflow/contrib/quantize/python/fold_batch_norms_test.py
@@ -600,13 +600,13 @@ class FoldBatchNormsTest(test_util.TensorFlowTestCase):
     if has_scaling:
       if fused:
         return scope + '/BatchNorm_Fold/mul'
-      return scope + '/BatchNorm/batchnorm/mul'
-    return scope + '/BatchNorm/batchnorm/Rsqrt'
+      return scope + '/BatchNorm/batchnorm_1/mul'
+    return scope + '/BatchNorm/batchnorm_1/Rsqrt'
 
   def _BathNormBiasName(self, scope, fused):
     if fused:
       return scope + '/BatchNorm_Fold/bias'
-    return scope + '/BatchNorm/batchnorm/sub'
+    return scope + '/BatchNorm/batchnorm_1/sub'
 
   def _WeightInit(self, stddev):
     """Returns a truncated normal variable initializer.