Move from deprecated self.test_session() to self.cached_session().

self.test_session() has been deprecated in 9962eb5e84b15e309410071b06c2ed2d6148ed44 as its name confuses readers of the test. Moving to cached_session() instead which is more explicit about:
* the fact that the session may be reused.
* the session is not closed even when doing a "with self.test_session()" statement.

PiperOrigin-RevId: 209700671

8 files changed, 65 insertions, 65 deletions

diff --git a/tensorflow/contrib/slim/python/slim/nets/alexnet_test.py b/tensorflow/contrib/slim/python/slim/nets/alexnet_test.py
index eb93f753ae..b6d1afd27d 100644
--- a/tensorflow/contrib/slim/python/slim/nets/alexnet_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/alexnet_test.py
@@ -33,7 +33,7 @@ class AlexnetV2Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = alexnet.alexnet_v2(inputs, num_classes)
       self.assertEquals(logits.op.name, 'alexnet_v2/fc8/squeezed')
@@ -44,7 +44,7 @@ class AlexnetV2Test(test.TestCase):
     batch_size = 1
     height, width = 300, 400
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = alexnet.alexnet_v2(inputs, num_classes, spatial_squeeze=False)
       self.assertEquals(logits.op.name, 'alexnet_v2/fc8/BiasAdd')
@@ -55,7 +55,7 @@ class AlexnetV2Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       _, end_points = alexnet.alexnet_v2(inputs, num_classes)
       expected_names = [
@@ -70,7 +70,7 @@ class AlexnetV2Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       alexnet.alexnet_v2(inputs, num_classes)
       expected_names = [
@@ -98,7 +98,7 @@ class AlexnetV2Test(test.TestCase):
     batch_size = 2
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = alexnet.alexnet_v2(eval_inputs, is_training=False)
       self.assertListEqual(logits.get_shape().as_list(),
@@ -112,7 +112,7 @@ class AlexnetV2Test(test.TestCase):
     train_height, train_width = 224, 224
     eval_height, eval_width = 300, 400
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       train_inputs = random_ops.random_uniform(
           (train_batch_size, train_height, train_width, 3))
       logits, _ = alexnet.alexnet_v2(train_inputs)
@@ -132,7 +132,7 @@ class AlexnetV2Test(test.TestCase):
   def testForward(self):
     batch_size = 1
     height, width = 224, 224
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = alexnet.alexnet_v2(inputs)
       sess.run(variables.global_variables_initializer())
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py b/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py
index 7a3d1c9703..34f12d7591 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v1_test.py
@@ -143,7 +143,7 @@ class InceptionV1Test(test.TestCase):
     height, width = 224, 224
     num_classes = 1000
     input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = array_ops.placeholder(
           dtypes.float32, shape=(batch_size, None, None, 3))
       logits, end_points = inception_v1.inception_v1(inputs, num_classes)
@@ -167,7 +167,7 @@ class InceptionV1Test(test.TestCase):
     self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
     images = random_ops.random_uniform((batch_size, height, width, 3))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(logits, {inputs: images.eval()})
       self.assertEquals(output.shape, (batch_size, num_classes))
@@ -182,7 +182,7 @@ class InceptionV1Test(test.TestCase):
         eval_inputs, num_classes, is_training=False)
     predictions = math_ops.argmax(logits, 1)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(predictions)
       self.assertEquals(output.shape, (batch_size,))
@@ -200,7 +200,7 @@ class InceptionV1Test(test.TestCase):
     logits, _ = inception_v1.inception_v1(eval_inputs, num_classes, reuse=True)
     predictions = math_ops.argmax(logits, 1)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(predictions)
       self.assertEquals(output.shape, (eval_batch_size,))
@@ -211,7 +211,7 @@ class InceptionV1Test(test.TestCase):
     logits, _ = inception_v1.inception_v1(
         images, num_classes=num_classes, spatial_squeeze=False)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       variables.global_variables_initializer().run()
       logits_out = sess.run(logits)
       self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py b/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py
index 5fbc9e5aa3..66effba944 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v2_test.py
@@ -196,7 +196,7 @@ class InceptionV2Test(test.TestCase):
     height, width = 224, 224
     num_classes = 1000
     input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = array_ops.placeholder(
           dtypes.float32, shape=(batch_size, None, None, 3))
       logits, end_points = inception_v2.inception_v2(inputs, num_classes)
@@ -220,7 +220,7 @@ class InceptionV2Test(test.TestCase):
     self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
     images = random_ops.random_uniform((batch_size, height, width, 3))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(logits, {inputs: images.eval()})
       self.assertEquals(output.shape, (batch_size, num_classes))
@@ -235,7 +235,7 @@ class InceptionV2Test(test.TestCase):
         eval_inputs, num_classes, is_training=False)
     predictions = math_ops.argmax(logits, 1)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(predictions)
       self.assertEquals(output.shape, (batch_size,))
@@ -253,7 +253,7 @@ class InceptionV2Test(test.TestCase):
     logits, _ = inception_v2.inception_v2(eval_inputs, num_classes, reuse=True)
     predictions = math_ops.argmax(logits, 1)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(predictions)
       self.assertEquals(output.shape, (eval_batch_size,))
@@ -264,7 +264,7 @@ class InceptionV2Test(test.TestCase):
     logits, _ = inception_v2.inception_v2(
         images, num_classes=num_classes, spatial_squeeze=False)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       variables.global_variables_initializer().run()
       logits_out = sess.run(logits)
       self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
diff --git a/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py b/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
index 6ba02318ed..0f9cca7bbd 100644
--- a/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/inception_v3_test.py
@@ -226,7 +226,7 @@ class InceptionV3Test(test.TestCase):
     height, width = 299, 299
     num_classes = 1000
     input_np = np.random.uniform(0, 1, (batch_size, height, width, 3))
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = array_ops.placeholder(
           dtypes.float32, shape=(batch_size, None, None, 3))
       logits, end_points = inception_v3.inception_v3(inputs, num_classes)
@@ -249,7 +249,7 @@ class InceptionV3Test(test.TestCase):
     self.assertListEqual(logits.get_shape().as_list(), [None, num_classes])
     images = random_ops.random_uniform((batch_size, height, width, 3))
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(logits, {inputs: images.eval()})
       self.assertEquals(output.shape, (batch_size, num_classes))
@@ -264,7 +264,7 @@ class InceptionV3Test(test.TestCase):
         eval_inputs, num_classes, is_training=False)
     predictions = math_ops.argmax(logits, 1)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(predictions)
       self.assertEquals(output.shape, (batch_size,))
@@ -283,7 +283,7 @@ class InceptionV3Test(test.TestCase):
         eval_inputs, num_classes, is_training=False, reuse=True)
     predictions = math_ops.argmax(logits, 1)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(predictions)
       self.assertEquals(output.shape, (eval_batch_size,))
@@ -294,7 +294,7 @@ class InceptionV3Test(test.TestCase):
     logits, _ = inception_v3.inception_v3(
         images, num_classes=num_classes, spatial_squeeze=False)
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       variables.global_variables_initializer().run()
       logits_out = sess.run(logits)
       self.assertListEqual(list(logits_out.shape), [1, 1, 1, num_classes])
diff --git a/tensorflow/contrib/slim/python/slim/nets/overfeat_test.py b/tensorflow/contrib/slim/python/slim/nets/overfeat_test.py
index 317af3cb29..44fa35ad14 100644
--- a/tensorflow/contrib/slim/python/slim/nets/overfeat_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/overfeat_test.py
@@ -33,7 +33,7 @@ class OverFeatTest(test.TestCase):
     batch_size = 5
     height, width = 231, 231
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = overfeat.overfeat(inputs, num_classes)
       self.assertEquals(logits.op.name, 'overfeat/fc8/squeezed')
@@ -44,7 +44,7 @@ class OverFeatTest(test.TestCase):
     batch_size = 1
     height, width = 281, 281
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = overfeat.overfeat(inputs, num_classes, spatial_squeeze=False)
       self.assertEquals(logits.op.name, 'overfeat/fc8/BiasAdd')
@@ -55,7 +55,7 @@ class OverFeatTest(test.TestCase):
     batch_size = 5
     height, width = 231, 231
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       _, end_points = overfeat.overfeat(inputs, num_classes)
       expected_names = [
@@ -70,7 +70,7 @@ class OverFeatTest(test.TestCase):
     batch_size = 5
     height, width = 231, 231
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       overfeat.overfeat(inputs, num_classes)
       expected_names = [
@@ -98,7 +98,7 @@ class OverFeatTest(test.TestCase):
     batch_size = 2
     height, width = 231, 231
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = overfeat.overfeat(eval_inputs, is_training=False)
       self.assertListEqual(logits.get_shape().as_list(),
@@ -112,7 +112,7 @@ class OverFeatTest(test.TestCase):
     train_height, train_width = 231, 231
     eval_height, eval_width = 281, 281
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       train_inputs = random_ops.random_uniform(
           (train_batch_size, train_height, train_width, 3))
       logits, _ = overfeat.overfeat(train_inputs)
@@ -132,7 +132,7 @@ class OverFeatTest(test.TestCase):
   def testForward(self):
     batch_size = 1
     height, width = 231, 231
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = overfeat.overfeat(inputs)
       sess.run(variables.global_variables_initializer())
diff --git a/tensorflow/contrib/slim/python/slim/nets/resnet_v1_test.py b/tensorflow/contrib/slim/python/slim/nets/resnet_v1_test.py
index 576444214d..8ff44fe4b5 100644
--- a/tensorflow/contrib/slim/python/slim/nets/resnet_v1_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/resnet_v1_test.py
@@ -69,7 +69,7 @@ class ResnetUtilsTest(test.TestCase):
     x = resnet_utils.subsample(x, 2)
     expected = array_ops.reshape(
         constant_op.constant([0, 2, 6, 8]), [1, 2, 2, 1])
-    with self.test_session():
+    with self.cached_session():
       self.assertAllClose(x.eval(), expected.eval())
 
   def testSubsampleFourByFour(self):
@@ -77,7 +77,7 @@ class ResnetUtilsTest(test.TestCase):
     x = resnet_utils.subsample(x, 2)
     expected = array_ops.reshape(
         constant_op.constant([0, 2, 8, 10]), [1, 2, 2, 1])
-    with self.test_session():
+    with self.cached_session():
       self.assertAllClose(x.eval(), expected.eval())
 
   def testConv2DSameEven(self):
@@ -110,7 +110,7 @@ class ResnetUtilsTest(test.TestCase):
     y4_expected = math_ops.to_float([[48, 37], [37, 22]])
     y4_expected = array_ops.reshape(y4_expected, [1, n2, n2, 1])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       self.assertAllClose(y1.eval(), y1_expected.eval())
       self.assertAllClose(y2.eval(), y2_expected.eval())
@@ -148,7 +148,7 @@ class ResnetUtilsTest(test.TestCase):
     y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
     y4_expected = y2_expected
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       self.assertAllClose(y1.eval(), y1_expected.eval())
       self.assertAllClose(y2.eval(), y2_expected.eval())
@@ -223,7 +223,7 @@ class ResnetUtilsTest(test.TestCase):
       with arg_scope([layers.batch_norm], is_training=False):
         for output_stride in [1, 2, 4, 8, None]:
           with ops.Graph().as_default():
-            with self.test_session() as sess:
+            with self.cached_session() as sess:
               random_seed.set_random_seed(0)
               inputs = create_test_input(1, height, width, 3)
               # Dense feature extraction followed by subsampling.
@@ -364,7 +364,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
     for output_stride in [4, 8, 16, 32, None]:
       with arg_scope(resnet_utils.resnet_arg_scope()):
         with ops.Graph().as_default():
-          with self.test_session() as sess:
+          with self.cached_session() as sess:
             random_seed.set_random_seed(0)
             inputs = create_test_input(2, 81, 81, 3)
             # Dense feature extraction followed by subsampling.
@@ -401,7 +401,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
     self.assertListEqual(logits.get_shape().as_list(),
                          [None, 1, 1, num_classes])
     images = create_test_input(batch, height, width, 3)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(logits, {inputs: images.eval()})
       self.assertEqual(output.shape, (batch, 1, 1, num_classes))
@@ -415,7 +415,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
       output, _ = self._resnet_small(inputs, None, global_pool=global_pool)
     self.assertListEqual(output.get_shape().as_list(), [batch, None, None, 32])
     images = create_test_input(batch, height, width, 3)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(output, {inputs: images.eval()})
       self.assertEqual(output.shape, (batch, 3, 3, 32))
@@ -431,7 +431,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
           inputs, None, global_pool=global_pool, output_stride=output_stride)
     self.assertListEqual(output.get_shape().as_list(), [batch, None, None, 32])
     images = create_test_input(batch, height, width, 3)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(output, {inputs: images.eval()})
       self.assertEqual(output.shape, (batch, 9, 9, 32))
diff --git a/tensorflow/contrib/slim/python/slim/nets/resnet_v2_test.py b/tensorflow/contrib/slim/python/slim/nets/resnet_v2_test.py
index 6bdda18c5b..055ecff1c3 100644
--- a/tensorflow/contrib/slim/python/slim/nets/resnet_v2_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/resnet_v2_test.py
@@ -69,7 +69,7 @@ class ResnetUtilsTest(test.TestCase):
     x = resnet_utils.subsample(x, 2)
     expected = array_ops.reshape(
         constant_op.constant([0, 2, 6, 8]), [1, 2, 2, 1])
-    with self.test_session():
+    with self.cached_session():
       self.assertAllClose(x.eval(), expected.eval())
 
   def testSubsampleFourByFour(self):
@@ -77,7 +77,7 @@ class ResnetUtilsTest(test.TestCase):
     x = resnet_utils.subsample(x, 2)
     expected = array_ops.reshape(
         constant_op.constant([0, 2, 8, 10]), [1, 2, 2, 1])
-    with self.test_session():
+    with self.cached_session():
       self.assertAllClose(x.eval(), expected.eval())
 
   def testConv2DSameEven(self):
@@ -110,7 +110,7 @@ class ResnetUtilsTest(test.TestCase):
     y4_expected = math_ops.to_float([[48, 37], [37, 22]])
     y4_expected = array_ops.reshape(y4_expected, [1, n2, n2, 1])
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       self.assertAllClose(y1.eval(), y1_expected.eval())
       self.assertAllClose(y2.eval(), y2_expected.eval())
@@ -151,7 +151,7 @@ class ResnetUtilsTest(test.TestCase):
     y4 = layers.conv2d(x, 1, [3, 3], stride=2, scope='Conv')
     y4_expected = y2_expected
 
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       self.assertAllClose(y1.eval(), y1_expected.eval())
       self.assertAllClose(y2.eval(), y2_expected.eval())
@@ -227,7 +227,7 @@ class ResnetUtilsTest(test.TestCase):
       with arg_scope([layers.batch_norm], is_training=False):
         for output_stride in [1, 2, 4, 8, None]:
           with ops.Graph().as_default():
-            with self.test_session() as sess:
+            with self.cached_session() as sess:
               random_seed.set_random_seed(0)
               inputs = create_test_input(1, height, width, 3)
               # Dense feature extraction followed by subsampling.
@@ -368,7 +368,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
     for output_stride in [4, 8, 16, 32, None]:
       with arg_scope(resnet_utils.resnet_arg_scope()):
         with ops.Graph().as_default():
-          with self.test_session() as sess:
+          with self.cached_session() as sess:
             random_seed.set_random_seed(0)
             inputs = create_test_input(2, 81, 81, 3)
             # Dense feature extraction followed by subsampling.
@@ -405,7 +405,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
     self.assertListEqual(logits.get_shape().as_list(),
                          [None, 1, 1, num_classes])
     images = create_test_input(batch, height, width, 3)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(logits, {inputs: images.eval()})
       self.assertEqual(output.shape, (batch, 1, 1, num_classes))
@@ -419,7 +419,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
       output, _ = self._resnet_small(inputs, None, global_pool=global_pool)
     self.assertListEqual(output.get_shape().as_list(), [batch, None, None, 32])
     images = create_test_input(batch, height, width, 3)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(output, {inputs: images.eval()})
       self.assertEqual(output.shape, (batch, 3, 3, 32))
@@ -435,7 +435,7 @@ class ResnetCompleteNetworkTest(test.TestCase):
           inputs, None, global_pool=global_pool, output_stride=output_stride)
     self.assertListEqual(output.get_shape().as_list(), [batch, None, None, 32])
     images = create_test_input(batch, height, width, 3)
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       sess.run(variables.global_variables_initializer())
       output = sess.run(output, {inputs: images.eval()})
       self.assertEqual(output.shape, (batch, 9, 9, 32))
diff --git a/tensorflow/contrib/slim/python/slim/nets/vgg_test.py b/tensorflow/contrib/slim/python/slim/nets/vgg_test.py
index 36628b32d1..71ce4b89cd 100644
--- a/tensorflow/contrib/slim/python/slim/nets/vgg_test.py
+++ b/tensorflow/contrib/slim/python/slim/nets/vgg_test.py
@@ -34,7 +34,7 @@ class VGGATest(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_a(inputs, num_classes)
       self.assertEquals(logits.op.name, 'vgg_a/fc8/squeezed')
@@ -45,7 +45,7 @@ class VGGATest(test.TestCase):
     batch_size = 1
     height, width = 256, 256
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_a(inputs, num_classes, spatial_squeeze=False)
       self.assertEquals(logits.op.name, 'vgg_a/fc8/BiasAdd')
@@ -73,7 +73,7 @@ class VGGATest(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       vgg.vgg_a(inputs, num_classes)
       expected_names = [
@@ -107,7 +107,7 @@ class VGGATest(test.TestCase):
     batch_size = 2
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_a(eval_inputs, is_training=False)
       self.assertListEqual(logits.get_shape().as_list(),
@@ -121,7 +121,7 @@ class VGGATest(test.TestCase):
     train_height, train_width = 224, 224
     eval_height, eval_width = 256, 256
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       train_inputs = random_ops.random_uniform(
           (train_batch_size, train_height, train_width, 3))
       logits, _ = vgg.vgg_a(train_inputs)
@@ -141,7 +141,7 @@ class VGGATest(test.TestCase):
   def testForward(self):
     batch_size = 1
     height, width = 224, 224
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_a(inputs)
       sess.run(variables.global_variables_initializer())
@@ -155,7 +155,7 @@ class VGG16Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_16(inputs, num_classes)
       self.assertEquals(logits.op.name, 'vgg_16/fc8/squeezed')
@@ -166,7 +166,7 @@ class VGG16Test(test.TestCase):
     batch_size = 1
     height, width = 256, 256
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_16(inputs, num_classes, spatial_squeeze=False)
       self.assertEquals(logits.op.name, 'vgg_16/fc8/BiasAdd')
@@ -197,7 +197,7 @@ class VGG16Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       vgg.vgg_16(inputs, num_classes)
       expected_names = [
@@ -241,7 +241,7 @@ class VGG16Test(test.TestCase):
     batch_size = 2
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_16(eval_inputs, is_training=False)
       self.assertListEqual(logits.get_shape().as_list(),
@@ -255,7 +255,7 @@ class VGG16Test(test.TestCase):
     train_height, train_width = 224, 224
     eval_height, eval_width = 256, 256
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       train_inputs = random_ops.random_uniform(
           (train_batch_size, train_height, train_width, 3))
       logits, _ = vgg.vgg_16(train_inputs)
@@ -275,7 +275,7 @@ class VGG16Test(test.TestCase):
   def testForward(self):
     batch_size = 1
     height, width = 224, 224
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_16(inputs)
       sess.run(variables.global_variables_initializer())
@@ -289,7 +289,7 @@ class VGG19Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_19(inputs, num_classes)
       self.assertEquals(logits.op.name, 'vgg_19/fc8/squeezed')
@@ -300,7 +300,7 @@ class VGG19Test(test.TestCase):
     batch_size = 1
     height, width = 256, 256
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_19(inputs, num_classes, spatial_squeeze=False)
       self.assertEquals(logits.op.name, 'vgg_19/fc8/BiasAdd')
@@ -332,7 +332,7 @@ class VGG19Test(test.TestCase):
     batch_size = 5
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       vgg.vgg_19(inputs, num_classes)
       expected_names = [
@@ -382,7 +382,7 @@ class VGG19Test(test.TestCase):
     batch_size = 2
     height, width = 224, 224
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       eval_inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_19(eval_inputs, is_training=False)
       self.assertListEqual(logits.get_shape().as_list(),
@@ -396,7 +396,7 @@ class VGG19Test(test.TestCase):
     train_height, train_width = 224, 224
     eval_height, eval_width = 256, 256
     num_classes = 1000
-    with self.test_session():
+    with self.cached_session():
       train_inputs = random_ops.random_uniform(
           (train_batch_size, train_height, train_width, 3))
       logits, _ = vgg.vgg_19(train_inputs)
@@ -416,7 +416,7 @@ class VGG19Test(test.TestCase):
   def testForward(self):
     batch_size = 1
     height, width = 224, 224
-    with self.test_session() as sess:
+    with self.cached_session() as sess:
       inputs = random_ops.random_uniform((batch_size, height, width, 3))
       logits, _ = vgg.vgg_19(inputs)
       sess.run(variables.global_variables_initializer())