From ff6dd474a624960e89a0a62a7855595cece2edca Mon Sep 17 00:00:00 2001
From: "A. Unique TensorFlower" <gardener@tensorflow.org>
Date: Fri, 8 Sep 2017 14:33:01 -0700
Subject: [PATCH] Use self._in_graph_mode consistently in ResourceVariable
 instead of sometimes getting it from the context.

Also: fix formatting of a comment and use a more precise test to detect
if initial_value is set.
PiperOrigin-RevId: 168047258
---
 .../python/ops/resource_variable_ops.py       | 20 +++++++++++--------
 1 file changed, 12 insertions(+), 8 deletions(-)

diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py
index fdc8a5843fe..c735be06983 100644
--- a/tensorflow/python/ops/resource_variable_ops.py
+++ b/tensorflow/python/ops/resource_variable_ops.py
@@ -42,14 +42,14 @@ from tensorflow.python.ops.gen_resource_variable_ops import *
 from tensorflow.python.util import compat
 
 
-def _eager_safe_variable_handle(shape, dtype, shared_name, name,
+def _eager_safe_variable_handle(shape, dtype, shared_name, name, graph_mode,
                                 container=None):
   """Creates a variable handle with information to do shape inference."""
   handle = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
                                                    shared_name=shared_name,
                                                    name=name,
                                                    container=container)
-  if context.in_graph_mode():
+  if graph_mode:
     return handle
   with context.graph_mode(), ops.Graph().as_default():
     h = gen_resource_variable_ops.var_handle_op(shape=shape, dtype=dtype,
@@ -152,8 +152,8 @@ class ResourceVariable(variables.Variable):
         uniquified automatically.
       dtype: If set, initial_value will be converted to the given type.
         If None, either the datatype will be kept (if initial_value is
-       a Tensor) or float32 will be used (if it is a Python object convertible
-       to a Tensor).
+        a Tensor) or float32 will be used (if it is a Python object convertible
+        to a Tensor).
       variable_def: `VariableDef` protocol buffer. If not None, recreates the
         `ResourceVariable` object with its contents. `variable_def` and other
         arguments (except for import_scope) are mutually exclusive.
@@ -172,7 +172,7 @@ class ResourceVariable(variables.Variable):
         shape and `validate_shape` is `True`.
     """
     if variable_def:
-      if initial_value:
+      if initial_value is not None:
         raise ValueError("variable_def and initial_value are mutually "
                          "exclusive.")
       if not context.in_graph_mode():
@@ -277,7 +277,8 @@ class ResourceVariable(variables.Variable):
                   shape=initial_value.get_shape(),
                   dtype=initial_value.dtype.base_dtype,
                   shared_name=handle_name,
-                  name=name)
+                  name=name,
+                  graph_mode=self._in_graph_mode)
               self._handle_device = (
                   self._handle.device if self._in_graph_mode else
                   context.get_default_context().device_name)
@@ -291,6 +292,7 @@ class ResourceVariable(variables.Variable):
                 dtype=initial_value.dtype.base_dtype,
                 shared_name=handle_name,
                 name=name,
+                graph_mode=False,
                 container="")
             self._handle_device = (
                 self._handle.device if self._in_graph_mode else
@@ -316,6 +318,7 @@ class ResourceVariable(variables.Variable):
               dtype=initial_value.dtype.base_dtype,
               shared_name=handle_name,
               name=name,
+              graph_mode=self._in_graph_mode,
               container="")
           self._handle_device = (self._handle.device if self._in_graph_mode else
                                  context.get_default_context().device_name)
@@ -372,6 +375,7 @@ class ResourceVariable(variables.Variable):
     """Initializes from `VariableDef` proto."""
     # Note that init_from_proto is currently not supported in Eager mode.
     assert context.in_graph_mode()
+    self._in_graph_mode = True
     assert isinstance(variable_def, variable_pb2.VariableDef)
     if not variable_def.is_resource:
       raise ValueError("Trying to restore Variable as ResourceVariable.")
@@ -434,7 +438,7 @@ class ResourceVariable(variables.Variable):
   @property
   def create(self):
     """The op responsible for initializing this variable."""
-    if not context.in_graph_mode():
+    if not self._in_graph_mode:
       raise RuntimeError("Calling create in EAGER mode not supported.")
     return self._initializer_op
 
@@ -520,7 +524,7 @@ class ResourceVariable(variables.Variable):
       # In graph mode, ensure we read the variable in the same device as the
       # handle. In eager mode, however, this sometimes tries to read a GPU
       # variable in the CPU because the handle is host memory. For now, then, we
-      # need to skip the device block in eager. TODO(apassos) eager should have
+      # need to skip the device block in eager. TODO(apassos): eager should have
       # separate notions of device and memory, so handle.device can be GPU while
       # handle.memory_space is always CPU.
       if context.in_graph_mode():
-- 
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