Makes some ref-related dtype methods private.

Change: 139484060

tensorflow/python/debug/stepper.py

@@ -382,7 +382,7 @@ class NodeStepper(object):
         # Determine whether the input is feedable. Reference-type tensors,
         # e.g., Variables, should not be fed, because they can change.
         if isinstance(inp, ops.Tensor):
-          is_inp_ref = inp.dtype.is_ref_dtype
+          is_inp_ref = inp.dtype._is_ref_dtype   # pylint: disable=protected-access
           can_feed = self._sess.graph.is_feedable(inp) and not is_inp_ref
         else:
           is_inp_ref = False

tensorflow/python/framework/dtypes.py

@@ -59,8 +59,6 @@ class DType(object):
   @@name
   @@base_dtype
   @@real_dtype
-  @@is_ref_dtype
-  @@as_ref
   @@is_floating
   @@is_complex
   @@is_integer
@@ -97,14 +95,14 @@ class DType(object):
     self._type_enum = type_enum
 
   @property
-  def is_ref_dtype(self):
+  def _is_ref_dtype(self):
     """Returns `True` if this `DType` represents a reference type."""
     return self._type_enum > 100
 
   @property
-  def as_ref(self):
+  def _as_ref(self):
     """Returns a reference `DType` based on this `DType`."""
-    if self.is_ref_dtype:
+    if self._is_ref_dtype:
       return self
     else:
       return _INTERN_TABLE[self._type_enum + 100]
@@ -112,7 +110,7 @@ class DType(object):
   @property
   def base_dtype(self):
     """Returns a non-reference `DType` based on this `DType`."""
-    if self.is_ref_dtype:
+    if self._is_ref_dtype:
       return _INTERN_TABLE[self._type_enum - 100]
     else:
       return self
@@ -269,7 +267,7 @@ class DType(object):
       return False
     try:
       dtype = as_dtype(other).as_datatype_enum
-      return self._type_enum == dtype
+      return self._type_enum == dtype  # pylint: disable=protected-access
     except TypeError:
       return False
 

tensorflow/python/framework/importer.py

@@ -60,7 +60,7 @@ def _ArgToTypesNoRef(node_def, arg_def):
 def _SingleArgToTypes(node_def, arg_def):
   types = _ArgToTypesNoRef(node_def, arg_def)
   if arg_def.is_ref:
-    return [dtypes.as_dtype(dt).as_ref.as_datatype_enum for dt in types]
+    return [dtypes.as_dtype(dt)._as_ref.as_datatype_enum for dt in types]  # pylint: disable=protected-access
   return types
 
 

tensorflow/python/framework/importer_test.py

@@ -718,7 +718,7 @@ class ImportGraphDefTest(tf.test.TestCase):
     # We'll use the following device function to observe ops with two inputs.
     ops_with_two_inputs = []
     def input_counter(op):
-      if any(in_t.dtype.is_ref_dtype for in_t in op.inputs):
+      if any(in_t.dtype._is_ref_dtype for in_t in op.inputs):  # pylint: disable=protected-access
         ops_with_two_inputs.append(op)
       return ""
 

tensorflow/python/framework/op_def_library.py

@@ -607,7 +607,7 @@ class OpDefLibrary(object):
             assert False, "Unreachable"
 
         if input_arg.is_ref:
-          if not all(x.is_ref_dtype for x in types):
+          if not all(x._is_ref_dtype for x in types):  # pylint: disable=protected-access
             raise TypeError(
                 "Input '%s' of '%s' Op requires l-value input" %
                 (input_name, op_type_name))
@@ -741,7 +741,7 @@ class OpDefLibrary(object):
           types = [arg.type]
           output_structure.append(None)
         if arg.is_ref:
-          types = [dtypes.as_dtype(x).as_ref for x in types]
+          types = [dtypes.as_dtype(x)._as_ref for x in types]  # pylint: disable=protected-access
         output_types.extend(types)
 
       if keywords:

tensorflow/python/ops/control_flow_ops.py

@@ -160,7 +160,7 @@ def _Identity(data, name=None):
   """
   data = ops.convert_to_tensor_or_indexed_slices(data, as_ref=True)
   if isinstance(data, ops.Tensor):
-    if data.dtype.is_ref_dtype:
+    if data.dtype._is_ref_dtype:  # pylint: disable=protected-access
       return gen_array_ops._ref_identity(data, name=name)
     else:
       return array_ops.identity(data, name=name)
@@ -182,7 +182,7 @@ def _Identity(data, name=None):
 def _NextIteration(data, name=None):
   data = ops.convert_to_tensor_or_indexed_slices(data, as_ref=True)
   if isinstance(data, ops.Tensor):
-    if data.dtype.is_ref_dtype:
+    if data.dtype._is_ref_dtype:   # pylint: disable=protected-access
       return ref_next_iteration(data, name=name)
     else:
       return next_iteration(data, name=name)
@@ -223,7 +223,7 @@ def _Enter(data, frame_name, is_constant=False, parallel_iterations=10,
   """
   data = ops.convert_to_tensor_or_indexed_slices(data, as_ref=True)
   if isinstance(data, ops.Tensor):
-    if data.dtype.is_ref_dtype and use_ref:
+    if data.dtype._is_ref_dtype and use_ref:  # pylint: disable=protected-access
       result = ref_enter(data, frame_name, is_constant, parallel_iterations,
                          name=name)
     else:
@@ -272,7 +272,7 @@ def exit(data, name=None):
   """
   data = ops.convert_to_tensor_or_indexed_slices(data, as_ref=True)
   if isinstance(data, ops.Tensor):
-    if data.dtype.is_ref_dtype:
+    if data.dtype._is_ref_dtype:  # pylint: disable=protected-access
       return gen_control_flow_ops._ref_exit(data, name)
     else:
       return gen_control_flow_ops._exit(data, name)
@@ -378,7 +378,7 @@ def _SwitchRefOrTensor(data, pred, name="Switch"):
   # created within ops.colocate_with(data) to ignore the existing stack.
   with ops.colocate_with(data, ignore_existing=True):
     if isinstance(data, ops.Tensor):
-      if data.dtype.is_ref_dtype:
+      if data.dtype._is_ref_dtype:  # pylint: disable=protected-access
         return ref_switch(data, pred, name=name)
     return switch(data, pred, name=name)
 
@@ -414,7 +414,7 @@ def merge(inputs, name=None):
     inputs = [ops.convert_to_tensor_or_indexed_slices(inp, as_ref=True)
               for inp in inputs]
     if all([isinstance(v, ops.Tensor) for v in inputs]):
-      if all([v.dtype.is_ref_dtype for v in inputs]):
+      if all([v.dtype._is_ref_dtype for v in inputs]):  # pylint: disable=protected-access
         return gen_control_flow_ops._ref_merge(inputs, name)
       else:
         return gen_control_flow_ops._merge(inputs, name)