Move from deprecated self.test_session() to self.session() when a graph is set.

self.test_session() has been deprecated in cl/208545396 as its behavior confuses readers of the test. Moving to self.session() instead.

PiperOrigin-RevId: 209696110

4 files changed, 27 insertions, 27 deletions

diff --git a/tensorflow/contrib/layers/python/layers/initializers_test.py b/tensorflow/contrib/layers/python/layers/initializers_test.py
index b7fe878893..bd3692b258 100644
--- a/tensorflow/contrib/layers/python/layers/initializers_test.py
+++ b/tensorflow/contrib/layers/python/layers/initializers_test.py
@@ -85,7 +85,7 @@ class VarianceScalingInitializerTest(test.TestCase):
 
   def _test_variance(self, initializer, shape, variance, factor, mode, uniform):
     with ops.Graph().as_default() as g:
-      with self.test_session(graph=g) as sess:
+      with self.session(graph=g) as sess:
         var = variable_scope.get_variable(
             name='test',
             shape=shape,
diff --git a/tensorflow/contrib/layers/python/layers/layers_test.py b/tensorflow/contrib/layers/python/layers/layers_test.py
index 51c7abb105..eee90864b4 100644
--- a/tensorflow/contrib/layers/python/layers/layers_test.py
+++ b/tensorflow/contrib/layers/python/layers/layers_test.py
@@ -1067,7 +1067,7 @@ class Convolution2dTransposeTests(test.TestCase):
         conv = layers_lib.conv2d(
             transpose, num_filters, filter_size, stride=stride, padding='VALID')
 
-        with self.test_session(graph=graph) as sess:
+        with self.session(graph=graph) as sess:
           sess.run(variables_lib.global_variables_initializer())
           self.assertListEqual(list(conv.eval().shape), input_size)
 
@@ -1460,14 +1460,14 @@ class DropoutTest(test.TestCase):
 class FlattenTest(test.TestCase):
 
   def testInvalidRank(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       inputs.set_shape(tensor_shape.TensorShape((5,)))
       with self.assertRaisesRegexp(ValueError, 'incompatible with the layer'):
         _layers.flatten(inputs)
 
   def testUnknownLastDim(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       inputs.set_shape(tensor_shape.TensorShape((5, None)))
       output = _layers.flatten(inputs)
@@ -1629,7 +1629,7 @@ class FCTest(test.TestCase):
   def testCreateFC(self):
     height, width = 3, 3
     for layer_fn in (_layers.fully_connected, layers_lib.relu):
-      with ops.Graph().as_default() as g, self.test_session(g):
+      with ops.Graph().as_default() as g, self.session(g):
         inputs = np.random.uniform(size=(5, height * width * 3))
         output = layer_fn(inputs, 32)
         self.assertEqual(output.op.name, 'fully_connected/Relu')
@@ -1814,27 +1814,27 @@ class BatchNormTest(test.TestCase):
         a, center=False, data_format='NCHW', zero_debias_moving_mean=True)
 
   def testUnknownShape(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       with self.assertRaisesRegexp(ValueError, 'undefined rank'):
         _layers.batch_norm(inputs)
 
   def testInvalidDataFormat(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       with self.assertRaisesRegexp(
           ValueError, 'data_format has to be either NCHW or NHWC.'):
         _layers.batch_norm(inputs, data_format='CHWN')
 
   def testUnknownChannelsDimNHWC(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       inputs.set_shape(tensor_shape.TensorShape((5, 3, 3, None)))
       with self.assertRaisesRegexp(ValueError, 'undefined'):
         _layers.batch_norm(inputs, data_format='NHWC')
 
   def testUnknownChannelsDimNCHW(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       inputs.set_shape(tensor_shape.TensorShape((5, None, 3, 3)))
       with self.assertRaisesRegexp(ValueError, 'undefined'):
@@ -2810,13 +2810,13 @@ class BatchNormTest(test.TestCase):
 class LayerNormTest(test.TestCase):
 
   def testUnknownShape(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       with self.assertRaisesRegexp(ValueError, 'undefined rank'):
         _layers.layer_norm(inputs)
 
   def testParamsDimsNotFullyDefined(self):
-    with ops.Graph().as_default() as g, self.test_session(g):
+    with ops.Graph().as_default() as g, self.session(g):
       inputs = array_ops.placeholder(dtype=dtypes.float32)
       inputs.set_shape(tensor_shape.TensorShape((5, 3, 3, None)))
       with self.assertRaisesRegexp(ValueError, 'is not fully defined'):
@@ -2876,7 +2876,7 @@ class LayerNormTest(test.TestCase):
       for sigma in [1.0, 0.1]:
         input_values = np.random.randn(*input_shape) * sigma + mu
         with ops.Graph().as_default() as g:
-          with self.test_session(graph=g) as sess:
+          with self.session(graph=g) as sess:
             inputs = constant_op.constant(
                 input_values, shape=input_shape, dtype=dtype)
             output_t = _layers.layer_norm(
diff --git a/tensorflow/contrib/layers/python/layers/optimizers_test.py b/tensorflow/contrib/layers/python/layers/optimizers_test.py
index a4461a20e5..0f037e24ad 100644
--- a/tensorflow/contrib/layers/python/layers/optimizers_test.py
+++ b/tensorflow/contrib/layers/python/layers/optimizers_test.py
@@ -66,7 +66,7 @@ class OptimizersTest(test.TestCase):
     ]
     for optimizer in optimizers:
       with ops.Graph().as_default() as g:
-        with self.test_session(graph=g) as session:
+        with self.session(graph=g) as session:
           x, var, loss, global_step = _setup_model()
           train = optimizers_lib.optimize_loss(
               loss, global_step, learning_rate=0.1, optimizer=optimizer)
@@ -82,7 +82,7 @@ class OptimizersTest(test.TestCase):
       return gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
 
     with ops.Graph().as_default() as g:
-      with self.test_session(graph=g) as session:
+      with self.session(graph=g) as session:
         x, var, loss, global_step = _setup_model()
         train = optimizers_lib.optimize_loss(
             loss, global_step, learning_rate=None, optimizer=optimizer_fn)
@@ -96,14 +96,14 @@ class OptimizersTest(test.TestCase):
     optimizers = ["blah", variables.Variable, object(), lambda x: None]
     for optimizer in optimizers:
       with ops.Graph().as_default() as g:
-        with self.test_session(graph=g):
+        with self.session(graph=g):
           _, _, loss, global_step = _setup_model()
           with self.assertRaises(ValueError):
             optimizers_lib.optimize_loss(
                 loss, global_step, learning_rate=0.1, optimizer=optimizer)
 
   def testBadSummaries(self):
-    with ops.Graph().as_default() as g, self.test_session(graph=g):
+    with ops.Graph().as_default() as g, self.session(graph=g):
       _, _, loss, global_step = _setup_model()
       with self.assertRaises(ValueError):
         optimizers_lib.optimize_loss(
@@ -111,7 +111,7 @@ class OptimizersTest(test.TestCase):
             summaries=["loss", "bad_summary"])
 
   def testInvalidLoss(self):
-    with ops.Graph().as_default() as g, self.test_session(graph=g):
+    with ops.Graph().as_default() as g, self.session(graph=g):
       _, _, _, global_step = _setup_model()
       with self.assertRaises(ValueError):
         optimizers_lib.optimize_loss(
@@ -121,7 +121,7 @@ class OptimizersTest(test.TestCase):
             [[1.0]], global_step, learning_rate=0.1, optimizer="SGD")
 
   def testInvalidGlobalStep(self):
-    with ops.Graph().as_default() as g, self.test_session(graph=g):
+    with ops.Graph().as_default() as g, self.session(graph=g):
       x = array_ops.placeholder(dtypes.float32, [])
       var = variable_scope.get_variable(
           "test", [], initializer=init_ops.constant_initializer(10))
@@ -157,7 +157,7 @@ class OptimizersTest(test.TestCase):
             optimizer="SGD")
 
   def testInvalidLearningRate(self):
-    with ops.Graph().as_default() as g, self.test_session(graph=g):
+    with ops.Graph().as_default() as g, self.session(graph=g):
       _, _, loss, global_step = _setup_model()
       with self.assertRaises(ValueError):
         optimizers_lib.optimize_loss(
@@ -270,7 +270,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      with ops.Graph().as_default() as g, self.session(graph=g) as session:
         x = array_ops.placeholder(dtypes.float32, [])
         var = variable_scope.get_variable(
             "test", [], initializer=init_ops.constant_initializer(10))
@@ -295,7 +295,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g):
+      with ops.Graph().as_default() as g, self.session(graph=g):
         x = array_ops.placeholder(dtypes.float32, [])
         var = variable_scope.get_variable(
             "test", [], initializer=init_ops.constant_initializer(10))
@@ -319,7 +319,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      with ops.Graph().as_default() as g, self.session(graph=g) as session:
         x, var, loss, global_step = _setup_model()
         update_var = variable_scope.get_variable(
             "update", [], initializer=init_ops.constant_initializer(10))
@@ -342,7 +342,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      with ops.Graph().as_default() as g, self.session(graph=g) as session:
         x, var, loss, global_step = _setup_model()
         update_var = variable_scope.get_variable(
             "update", [], initializer=init_ops.constant_initializer(10))
@@ -365,7 +365,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      with ops.Graph().as_default() as g, self.session(graph=g) as session:
         x, var, loss, global_step = _setup_model()
         update_var = variable_scope.get_variable(
             "update", [], initializer=init_ops.constant_initializer(10))
@@ -389,7 +389,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      with ops.Graph().as_default() as g, self.session(graph=g) as session:
         x, var, loss, global_step = _setup_model()
         update_var = variable_scope.get_variable(
             "update", [], initializer=init_ops.constant_initializer(10))
@@ -413,7 +413,7 @@ class OptimizersTest(test.TestCase):
         gradient_descent.GradientDescentOptimizer(learning_rate=0.1)
     ]
     for optimizer in optimizers:
-      with ops.Graph().as_default() as g, self.test_session(graph=g) as session:
+      with ops.Graph().as_default() as g, self.session(graph=g) as session:
         x, var, loss, global_step = _setup_model()
         update_var = variable_scope.get_variable(
             "update", [], initializer=init_ops.constant_initializer(10))
diff --git a/tensorflow/contrib/layers/python/layers/utils_test.py b/tensorflow/contrib/layers/python/layers/utils_test.py
index 645dc1291e..a9bd89532a 100644
--- a/tensorflow/contrib/layers/python/layers/utils_test.py
+++ b/tensorflow/contrib/layers/python/layers/utils_test.py
@@ -47,7 +47,7 @@ class ConstantValueTest(test.TestCase):
 
   def test_variable(self):
     for v in [True, False, 1, 0, 1.0]:
-      with ops.Graph().as_default() as g, self.test_session(g) as sess:
+      with ops.Graph().as_default() as g, self.session(g) as sess:
         x = variables.Variable(v)
         value = utils.constant_value(x)
         self.assertEqual(value, None)