diff options
| author |  A. Unique TensorFlower <gardener@tensorflow.org> | 2018-08-22 15:06:15 -0700 |
|---|---|---|
| committer | TensorFlower Gardener <gardener@tensorflow.org> | 2018-08-22 15:15:31 -0700 |
| commit | cd199a89dbffdd55aa2fc89acb874763382f196d (patch) | |
| tree | 831b17c85b5c7c8257cc05bbc7d8940db8be0b32 | |
| parent | d44030304e4c0a50a3169ea06b528bd780780acb (diff) | |
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: 209837298
65 files changed, 260 insertions, 260 deletions
diff --git a/tensorflow/compiler/tests/adadelta_test.py b/tensorflow/compiler/tests/adadelta_test.py index 3e3c09c66e..b7b7fda293 100644 --- a/tensorflow/compiler/tests/adadelta_test.py +++ b/tensorflow/compiler/tests/adadelta_test.py @@ -33,7 +33,7 @@ class AdadeltaOptimizerTest(xla_test.XLATestCase): def testBasic(self): num_updates = 4 # number of ADADELTA steps to perform for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): for grad in [0.2, 0.1, 0.01]: for lr in [1.0, 0.5, 0.1]: var0_init = [1.0, 2.0] diff --git a/tensorflow/compiler/tests/adagrad_da_test.py b/tensorflow/compiler/tests/adagrad_da_test.py index dc1625793a..69fb3ec296 100644 --- a/tensorflow/compiler/tests/adagrad_da_test.py +++ b/tensorflow/compiler/tests/adagrad_da_test.py @@ -33,7 +33,7 @@ class AdagradDAOptimizerTest(xla_test.XLATestCase): def testAdagradDAWithoutRegularizationBasic1(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): global_step = resource_variable_ops.ResourceVariable( 0, dtype=dtypes.int64) var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) @@ -69,7 +69,7 @@ class AdagradDAOptimizerTest(xla_test.XLATestCase): def testAdagradDAwithoutRegularizationBasic2(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): global_step = resource_variable_ops.ResourceVariable( 0, dtype=dtypes.int64) var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) @@ -100,7 +100,7 @@ class AdagradDAOptimizerTest(xla_test.XLATestCase): def testAdagradDAWithL1(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): global_step = resource_variable_ops.ResourceVariable( 0, dtype=dtypes.int64) var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) @@ -131,7 +131,7 @@ class AdagradDAOptimizerTest(xla_test.XLATestCase): def testAdagradDAWithL1_L2(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): global_step = resource_variable_ops.ResourceVariable( 0, dtype=dtypes.int64) var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) diff --git a/tensorflow/compiler/tests/adagrad_test.py b/tensorflow/compiler/tests/adagrad_test.py index d775850a80..ab69319c59 100644 --- a/tensorflow/compiler/tests/adagrad_test.py +++ b/tensorflow/compiler/tests/adagrad_test.py @@ -32,7 +32,7 @@ class AdagradOptimizerTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) @@ -57,7 +57,7 @@ class AdagradOptimizerTest(xla_test.XLATestCase): def testTensorLearningRate(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) @@ -83,7 +83,7 @@ class AdagradOptimizerTest(xla_test.XLATestCase): def testSharing(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) diff --git a/tensorflow/compiler/tests/adamax_test.py b/tensorflow/compiler/tests/adamax_test.py index c4fdbc5974..3ed1d41b71 100644 --- a/tensorflow/compiler/tests/adamax_test.py +++ b/tensorflow/compiler/tests/adamax_test.py @@ -49,7 +49,7 @@ class AdaMaxOptimizerTest(xla_test.XLATestCase): def testBasic(self): for i, dtype in enumerate(self.float_types): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): variable_scope.get_variable_scope().set_use_resource(True) # Initialize variables for numpy implementation. m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 @@ -100,7 +100,7 @@ class AdaMaxOptimizerTest(xla_test.XLATestCase): def testTensorLearningRate(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): variable_scope.get_variable_scope().set_use_resource(True) # Initialize variables for numpy implementation. m0, v0, m1, v1 = 0.0, 0.0, 0.0, 0.0 diff --git a/tensorflow/compiler/tests/addsign_test.py b/tensorflow/compiler/tests/addsign_test.py index 9ec5a964cb..1bc07ace23 100644 --- a/tensorflow/compiler/tests/addsign_test.py +++ b/tensorflow/compiler/tests/addsign_test.py @@ -63,7 +63,7 @@ class AddSignTest(xla_test.XLATestCase): alpha=1.0, beta=0.9): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): # Initialize variables for numpy implementation. m0, m1 = 0.0, 0.0 var0_np = np.array([1.0, 2.0], dtype=dtype) diff --git a/tensorflow/compiler/tests/argminmax_test.py b/tensorflow/compiler/tests/argminmax_test.py index 9d3a889b1f..4155342787 100644 --- a/tensorflow/compiler/tests/argminmax_test.py +++ b/tensorflow/compiler/tests/argminmax_test.py @@ -40,7 +40,7 @@ class ArgMinMaxTest(xla_test.XLATestCase): op_input: numpy input array to use as input to 'op'. expected: numpy array representing the expected output of 'op'. """ - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): pinp = array_ops.placeholder( dtypes.as_dtype(op_input.dtype), op_input.shape, name="a") diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 5b7001b5a4..4a281c37e4 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -36,7 +36,7 @@ class BinaryOpsTest(xla_test.XLATestCase): """Test cases for binary operators.""" def _testBinary(self, op, a, b, expected, equality_test=None): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a") pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b") diff --git a/tensorflow/compiler/tests/bucketize_op_test.py b/tensorflow/compiler/tests/bucketize_op_test.py index ef4d5f6322..5c24db539b 100644 --- a/tensorflow/compiler/tests/bucketize_op_test.py +++ b/tensorflow/compiler/tests/bucketize_op_test.py @@ -29,7 +29,7 @@ from tensorflow.python.platform import test class BucketizationOpTest(xla_test.XLATestCase): def testInt(self): - with self.test_session() as sess: + with self.cached_session() as sess: p = array_ops.placeholder(dtypes.int32) with self.test_scope(): op = math_ops._bucketize(p, boundaries=[0, 3, 8, 11]) @@ -38,7 +38,7 @@ class BucketizationOpTest(xla_test.XLATestCase): sess.run(op, {p: [-5, 0, 2, 3, 5, 8, 10, 11, 12]})) def testFloat(self): - with self.test_session() as sess: + with self.cached_session() as sess: p = array_ops.placeholder(dtypes.float32) with self.test_scope(): op = math_ops._bucketize(p, boundaries=[0., 3., 8., 11.]) @@ -48,7 +48,7 @@ class BucketizationOpTest(xla_test.XLATestCase): sess.run(op, {p: [-5., 0., 2., 3., 5., 8., 10., 11., 12.]})) def test2DInput(self): - with self.test_session() as sess: + with self.cached_session() as sess: p = array_ops.placeholder(dtypes.float32) with self.test_scope(): op = math_ops._bucketize(p, boundaries=[0, 3, 8, 11]) @@ -58,7 +58,7 @@ class BucketizationOpTest(xla_test.XLATestCase): {p: [[-5, 0, 2, 3, 5], [8, 10, 11, 12, 0]]})) def testInvalidBoundariesOrder(self): - with self.test_session() as sess: + with self.cached_session() as sess: p = array_ops.placeholder(dtypes.int32) with self.test_scope(): op = math_ops._bucketize(p, boundaries=[0, 8, 3, 11]) @@ -67,7 +67,7 @@ class BucketizationOpTest(xla_test.XLATestCase): sess.run(op, {p: [-5, 0]}) def testBoundariesNotList(self): - with self.test_session(): + with self.cached_session(): with self.assertRaisesRegexp(TypeError, "Expected list.*"): p = array_ops.placeholder(dtypes.int32) with self.test_scope(): diff --git a/tensorflow/compiler/tests/categorical_op_test.py b/tensorflow/compiler/tests/categorical_op_test.py index a4e7f75081..a57d1dc81e 100644 --- a/tensorflow/compiler/tests/categorical_op_test.py +++ b/tensorflow/compiler/tests/categorical_op_test.py @@ -56,7 +56,7 @@ class CategoricalTest(xla_test.XLATestCase): Returns: Frequencies from sampled classes; shape [batch_size, num_classes]. """ - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): random_seed.set_random_seed(1618) op = random_ops.multinomial(logits, num_samples, output_dtype=dtypes.int32) @@ -79,7 +79,7 @@ class CategoricalTest(xla_test.XLATestCase): def _testRngIsNotConstant(self, rng, dtype, output_dtype): # Tests that 'rng' does not always return the same value. - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = rng(dtype, output_dtype) @@ -107,7 +107,7 @@ class CategoricalTest(xla_test.XLATestCase): def testCategoricalIsInRange(self): for dtype in self.float_types: for output_dtype in self.output_dtypes(): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = random_ops.multinomial( array_ops.ones(shape=[1, 20], dtype=dtype), 1000, diff --git a/tensorflow/compiler/tests/cholesky_op_test.py b/tensorflow/compiler/tests/cholesky_op_test.py index ed532db0ee..d1896a50f7 100644 --- a/tensorflow/compiler/tests/cholesky_op_test.py +++ b/tensorflow/compiler/tests/cholesky_op_test.py @@ -54,7 +54,7 @@ class CholeskyOpTest(xla_test.XLATestCase): def _verifyCholesky(self, x, atol=1e-6): # Verify that LL^T == x. - with self.test_session() as sess: + with self.cached_session() as sess: placeholder = array_ops.placeholder( dtypes.as_dtype(x.dtype), shape=x.shape) with self.test_scope(): diff --git a/tensorflow/compiler/tests/clustering_test.py b/tensorflow/compiler/tests/clustering_test.py index e42ebf8f9e..88bd58b2da 100644 --- a/tensorflow/compiler/tests/clustering_test.py +++ b/tensorflow/compiler/tests/clustering_test.py @@ -38,7 +38,7 @@ class ClusteringTest(xla_test.XLATestCase): val1 = np.array([4, 3, 2, 1], dtype=np.float32) val2 = np.array([5, 6, 7, 8], dtype=np.float32) expected = val1 + val2 - with self.test_session(): + with self.cached_session(): with self.test_scope(): input1 = constant_op.constant(val1, name="const1") input2 = constant_op.constant(val2, name="const2") @@ -50,7 +50,7 @@ class ClusteringTest(xla_test.XLATestCase): val1 = np.array([4, 3, 2, 1]).astype(np.float32) val2 = np.array([5, 6, 7, 8]).astype(np.float32) expected = val1 + val2 - with self.test_session(): + with self.cached_session(): with ops.device(CPU_DEVICE): input1 = constant_op.constant(val1, name="const1") input2 = constant_op.constant(val2, name="const2") @@ -68,7 +68,7 @@ class ClusteringTest(xla_test.XLATestCase): # where x and z are placed on the CPU and y and w are placed on the XLA # device. If y and w are clustered for compilation, then the graph will # deadlock since the clustered graph will contain a self-loop. - with self.test_session() as sess: + with self.cached_session() as sess: with ops.device(CPU_DEVICE): x = array_ops.placeholder(dtypes.float32, [2]) with self.test_scope(): @@ -81,7 +81,7 @@ class ClusteringTest(xla_test.XLATestCase): self.assertAllClose(result, [12., 2.], rtol=1e-3) def testHostMemory(self): - with self.test_session() as sess: + with self.cached_session() as sess: x = array_ops.placeholder(dtypes.int32) with self.test_scope(): y = x + 1 diff --git a/tensorflow/compiler/tests/concat_ops_test.py b/tensorflow/compiler/tests/concat_ops_test.py index d9ad428147..37e5318bb5 100644 --- a/tensorflow/compiler/tests/concat_ops_test.py +++ b/tensorflow/compiler/tests/concat_ops_test.py @@ -33,7 +33,7 @@ from tensorflow.python.platform import googletest class ConcatTest(xla_test.XLATestCase): def testHStack(self): - with self.test_session(): + with self.cached_session(): p1 = array_ops.placeholder(dtypes.float32, shape=[4, 4]) p2 = array_ops.placeholder(dtypes.float32, shape=[4, 4]) with self.test_scope(): @@ -49,7 +49,7 @@ class ConcatTest(xla_test.XLATestCase): self.assertAllEqual(result[4:, :], params[p2]) def testVStack(self): - with self.test_session(): + with self.cached_session(): p1 = array_ops.placeholder(dtypes.float32, shape=[4, 4]) p2 = array_ops.placeholder(dtypes.float32, shape=[4, 4]) with self.test_scope(): @@ -65,7 +65,7 @@ class ConcatTest(xla_test.XLATestCase): self.assertAllEqual(result[:, 4:], params[p2]) def testInt32(self): - with self.test_session(): + with self.cached_session(): p1 = np.random.rand(2, 3).astype("i") p2 = np.random.rand(2, 3).astype("i") x1 = constant_op.constant(p1) @@ -88,7 +88,7 @@ class ConcatTest(xla_test.XLATestCase): dtype_feed = dtypes.float32 else: dtype_feed = dtype - with self.test_session(): + with self.cached_session(): p = [] for i in np.arange(num_tensors): input_shape = shape @@ -130,7 +130,7 @@ class ConcatTest(xla_test.XLATestCase): self._testRandom(dtypes.int32) def _testGradientsSimple(self): - with self.test_session(): + with self.cached_session(): inp = [] inp_tensors = [] with self.test_scope(): @@ -157,7 +157,7 @@ class ConcatTest(xla_test.XLATestCase): self._testGradientsSimple() def _testGradientsFirstDim(self): - with self.test_session(): + with self.cached_session(): inp = [] inp_tensors = [] with self.test_scope(): @@ -185,7 +185,7 @@ class ConcatTest(xla_test.XLATestCase): self._testGradientsFirstDim() def _testGradientsLastDim(self): - with self.test_session(): + with self.cached_session(): inp = [] inp_tensors = [] with self.test_scope(): @@ -220,7 +220,7 @@ class ConcatTest(xla_test.XLATestCase): # Random dim to concat on concat_dim = np.random.randint(5) concat_dim_sizes = np.random.randint(1, 5, size=num_tensors) - with self.test_session(): + with self.cached_session(): inp = [] inp_tensors = [] with self.test_scope(): @@ -254,7 +254,7 @@ class ConcatTest(xla_test.XLATestCase): def DISABLED_testZeroSize(self): # Verify that concat doesn't crash and burn for zero size inputs np.random.seed(7) - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): for shape0 in (), (2,): axis = len(shape0) @@ -276,14 +276,14 @@ class ConcatTest(xla_test.XLATestCase): def testConcatTuple(self): c1 = np.random.rand(4, 4).astype(np.float32) c2 = np.random.rand(4, 4).astype(np.float32) - with self.test_session(): + with self.cached_session(): with self.test_scope(): concat_list_t = array_ops.concat([c1, c2], 0) concat_tuple_t = array_ops.concat((c1, c2), 0) self.assertAllEqual(concat_list_t.eval(), concat_tuple_t.eval()) def testConcatNoScalars(self): - with self.test_session(): + with self.cached_session(): with self.test_scope(): scalar = constant_op.constant(7) dim = array_ops.placeholder(dtypes.int32) @@ -295,7 +295,7 @@ class ConcatTest(xla_test.XLATestCase): class ConcatOffsetTest(xla_test.XLATestCase): def testBasic(self): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): cdim = constant_op.constant(1, dtypes.int32) s0 = constant_op.constant([2, 3, 5], dtypes.int32) @@ -309,7 +309,7 @@ class ConcatOffsetTest(xla_test.XLATestCase): class PackTest(xla_test.XLATestCase): def testBasic(self): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): s0 = constant_op.constant([2, 3, 5], dtypes.int32) s1 = constant_op.constant([2, 7, 5], dtypes.int32) @@ -319,7 +319,7 @@ class PackTest(xla_test.XLATestCase): self.assertAllEqual(ans, [[2, 3, 5], [2, 7, 5], [2, 20, 5]]) def testScalars(self): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): s0 = constant_op.constant(2, dtypes.int32) s1 = constant_op.constant(3, dtypes.int32) @@ -329,7 +329,7 @@ class PackTest(xla_test.XLATestCase): self.assertAllEqual(ans, [2, 3, 5]) def testEmpty(self): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): s0 = constant_op.constant([[]], dtypes.int32) s1 = constant_op.constant([[]], dtypes.int32) diff --git a/tensorflow/compiler/tests/conv2d_test.py b/tensorflow/compiler/tests/conv2d_test.py index f9db103f6d..af00ff287d 100644 --- a/tensorflow/compiler/tests/conv2d_test.py +++ b/tensorflow/compiler/tests/conv2d_test.py @@ -87,7 +87,7 @@ class Conv2DTest(xla_test.XLATestCase, parameterized.TestCase): dilations = test_utils.PermuteDimsBetweenDataFormats( dilations, data_format_src, data_format_dst) - with self.test_session() as sess: + with self.cached_session() as sess: t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes) t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes) with self.test_scope(): @@ -288,7 +288,7 @@ class Conv2DBackpropInputTest(xla_test.XLATestCase, parameterized.TestCase): dilations = test_utils.PermuteDimsBetweenDataFormats( dilations, data_format_src, data_format_dst) - with self.test_session() as sess: + with self.cached_session() as sess: t1 = array_ops.placeholder(dtypes.float32, shape=filter_sizes) t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes) with self.test_scope(): @@ -586,7 +586,7 @@ class Conv2DBackpropFilterTest(xla_test.XLATestCase, parameterized.TestCase): dilations = test_utils.PermuteDimsBetweenDataFormats( dilations, data_format_src, data_format_dst) - with self.test_session() as sess: + with self.cached_session() as sess: t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes) t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes) with self.test_scope(): diff --git a/tensorflow/compiler/tests/conv3d_test.py b/tensorflow/compiler/tests/conv3d_test.py index 31ee41f04f..33fd983b54 100644 --- a/tensorflow/compiler/tests/conv3d_test.py +++ b/tensorflow/compiler/tests/conv3d_test.py @@ -36,7 +36,7 @@ from tensorflow.python.platform import googletest class Conv3DBackpropFilterV2GradTest(xla_test.XLATestCase): def testGradient(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): for padding in ["SAME", "VALID"]: for stride in [1, 2]: np.random.seed(1) @@ -69,7 +69,7 @@ class Conv3DBackpropFilterV2GradTest(xla_test.XLATestCase): class Conv3DTransposeTest(xla_test.XLATestCase): def testConv3DTransposeSingleStride(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): strides = [1, 1, 1, 1, 1] # Input, output: [batch, depth, height, width, channel] @@ -119,7 +119,7 @@ class Conv3DTransposeTest(xla_test.XLATestCase): self.assertAllClose(target, value[n, d, h, w, k]) def testConv3DTransposeSame(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): strides = [1, 2, 2, 2, 1] # Input, output: [batch, depth, height, width, depth] @@ -157,7 +157,7 @@ class Conv3DTransposeTest(xla_test.XLATestCase): self.assertAllClose(target, value[n, d, h, w, k]) def testConv3DTransposeValid(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): strides = [1, 2, 2, 2, 1] # Input, output: [batch, depth, height, width, depth] @@ -217,7 +217,7 @@ class Conv3DTransposeTest(xla_test.XLATestCase): np.random.seed(1) # Make it reproducible. x_val = np.random.random_sample(x_shape).astype(np.float64) f_val = np.random.random_sample(f_shape).astype(np.float64) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): x = constant_op.constant(x_val, name="x", dtype=dtypes.float32) f = constant_op.constant(f_val, name="f", dtype=dtypes.float32) output = nn_ops.conv3d_transpose( diff --git a/tensorflow/compiler/tests/dense_layer_test.py b/tensorflow/compiler/tests/dense_layer_test.py index 865f60ccab..04f3b3ef49 100644 --- a/tensorflow/compiler/tests/dense_layer_test.py +++ b/tensorflow/compiler/tests/dense_layer_test.py @@ -86,7 +86,7 @@ class DenseLayerTest(test.TestCase): XlaLaunch op by XLA. """ - with self.test_session() as sess: + with self.cached_session() as sess: x = array_ops.placeholder(shape=[2, 2, 3], dtype=np.float32) with jit_scope(): y = layers.dense(x, 3) @@ -113,7 +113,7 @@ class DenseLayerTest(test.TestCase): cluster, causing dense layer to be split into TWO XlaLaunch ops. """ - with self.test_session() as sess: + with self.cached_session() as sess: x = array_ops.placeholder(shape=[None, None, 3], dtype=np.float32) with jit_scope(): y = layers.dense(x, 3) diff --git a/tensorflow/compiler/tests/depthwise_conv_op_test.py b/tensorflow/compiler/tests/depthwise_conv_op_test.py index 98dc73e189..6ef8a68ca5 100644 --- a/tensorflow/compiler/tests/depthwise_conv_op_test.py +++ b/tensorflow/compiler/tests/depthwise_conv_op_test.py @@ -151,7 +151,7 @@ class DepthwiseConv2DTest(xla_test.XLATestCase): dtype=data_type).reshape(tensor_in_sizes) x2 = np.array([f * 1.0 for f in range(1, total_size_2 + 1)], dtype=data_type).reshape(filter_in_sizes) - with self.test_session() as sess: + with self.cached_session() as sess: if data_type == np.float32: tolerance = 1e-4 else: @@ -247,7 +247,7 @@ class DepthwiseConv2DTest(xla_test.XLATestCase): dtype=np.float32).reshape(tensor_in_sizes) x2 = np.array([f * 1.0 for f in range(1, total_size_2 + 1)], dtype=np.float32).reshape(filter_in_sizes) - with self.test_session() as sess: + with self.cached_session() as sess: t1 = array_ops.placeholder(shape=tensor_in_sizes, dtype=np.float32) t2 = array_ops.placeholder(shape=filter_in_sizes, dtype=np.float32) with self.test_scope(): @@ -321,7 +321,7 @@ class DepthwiseConv2DTest(xla_test.XLATestCase): x2 = np.random.rand(*output_sizes).astype(np.float32) def _GetVal(use_xla): - with self.test_session(): + with self.cached_session(): t0 = constant_op.constant(input_sizes, shape=[len(input_sizes)]) t1 = array_ops.placeholder(np.float32, shape=filter_sizes) t2 = array_ops.placeholder(np.float32, shape=output_sizes) @@ -356,7 +356,7 @@ class DepthwiseConv2DTest(xla_test.XLATestCase): x2 = np.random.rand(*output_sizes).astype(np.float32) def _GetVal(use_xla): - with self.test_session(): + with self.cached_session(): t0 = array_ops.placeholder(np.float32, shape=input_sizes) t1 = constant_op.constant(filter_sizes, shape=[len(filter_sizes)]) t2 = array_ops.placeholder(np.float32, shape=output_sizes) diff --git a/tensorflow/compiler/tests/dynamic_slice_ops_test.py b/tensorflow/compiler/tests/dynamic_slice_ops_test.py index 154e36b10e..5f01e128f0 100644 --- a/tensorflow/compiler/tests/dynamic_slice_ops_test.py +++ b/tensorflow/compiler/tests/dynamic_slice_ops_test.py @@ -30,7 +30,7 @@ from tensorflow.python.platform import test class DynamicUpdateSliceOpsTest(xla_test.XLATestCase): def _assertOpOutputMatchesExpected(self, op, args, expected): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): placeholders = [ array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape) diff --git a/tensorflow/compiler/tests/dynamic_stitch_test.py b/tensorflow/compiler/tests/dynamic_stitch_test.py index edd78153b5..50b04daa6b 100644 --- a/tensorflow/compiler/tests/dynamic_stitch_test.py +++ b/tensorflow/compiler/tests/dynamic_stitch_test.py @@ -30,7 +30,7 @@ from tensorflow.python.platform import googletest class DynamicStitchTest(xla_test.XLATestCase): def _AssertDynamicStitchResultIs(self, indices, data, expected): - with self.test_session() as session: + with self.cached_session() as session: index_placeholders = [ array_ops.placeholder(dtypes.as_dtype(arg.dtype)) for arg in indices ] diff --git a/tensorflow/compiler/tests/eager_test.py b/tensorflow/compiler/tests/eager_test.py index 3d21fb5864..e32f3d4b7f 100644 --- a/tensorflow/compiler/tests/eager_test.py +++ b/tensorflow/compiler/tests/eager_test.py @@ -101,7 +101,7 @@ class EagerTest(xla_test.XLATestCase): self.assertAllEqual(15, product) # Run some ops graphly - with context.graph_mode(), self.test_session() as sess: + with context.graph_mode(), self.cached_session() as sess: with self.test_scope(): three = constant_op.constant(3) five = constant_op.constant(5) diff --git a/tensorflow/compiler/tests/extract_image_patches_op_test.py b/tensorflow/compiler/tests/extract_image_patches_op_test.py index 5529fdbb09..37061e91d1 100644 --- a/tensorflow/compiler/tests/extract_image_patches_op_test.py +++ b/tensorflow/compiler/tests/extract_image_patches_op_test.py @@ -44,7 +44,7 @@ class ExtractImagePatches(xla_test.XLATestCase): strides = [1] + strides + [1] rates = [1] + rates + [1] - with self.test_session(): + with self.cached_session(): image_placeholder = array_ops.placeholder(dtypes.float32) with self.test_scope(): out_tensor = array_ops.extract_image_patches( diff --git a/tensorflow/compiler/tests/fake_quant_ops_test.py b/tensorflow/compiler/tests/fake_quant_ops_test.py index c48ab178bf..2178c44556 100644 --- a/tensorflow/compiler/tests/fake_quant_ops_test.py +++ b/tensorflow/compiler/tests/fake_quant_ops_test.py @@ -107,7 +107,7 @@ class FakeQuantWithMinMaxArgsTest(xla_test.XLATestCase): ], dtype=np.float32) - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): input_placeholder = array_ops.placeholder( dtypes.float32, inputs.shape, name="inputs") @@ -198,7 +198,7 @@ class FakeQuantWithMinMaxArgsGradientTest(xla_test.XLATestCase): [0.0, 0.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 0.0, 0.0], dtype=np.float32) - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): gradient_placeholder = array_ops.placeholder( dtypes.float32, gradients.shape, name="gradients") @@ -306,7 +306,7 @@ class FakeQuantWithMinMaxVarsTest(xla_test.XLATestCase): ], dtype=np.float32) - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): input_placeholder = array_ops.placeholder( dtypes.float32, inputs.shape, name="inputs") @@ -406,7 +406,7 @@ class FakeQuantWithMinMaxVarsGradientTest(xla_test.XLATestCase): expected_backprops_wrt_min = 1.0 + 2.0 expected_backprops_wrt_max = 10.0 + 11.0 - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): gradient_placeholder = array_ops.placeholder( dtypes.float32, gradients.shape, name="gradients") diff --git a/tensorflow/compiler/tests/fft_test.py b/tensorflow/compiler/tests/fft_test.py index c64ea249ec..b3e13fbaa6 100644 --- a/tensorflow/compiler/tests/fft_test.py +++ b/tensorflow/compiler/tests/fft_test.py @@ -71,7 +71,7 @@ class FFTTest(xla_test.XLATestCase): data = np.reshape(data.astype(np.float32).view(np.complex64), shape) data = to_32bit(complex_to_input(data)) expected = to_32bit(input_to_expected(data)) - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): ph = array_ops.placeholder( dtypes.as_dtype(data.dtype), shape=data.shape) @@ -93,7 +93,7 @@ class FFTTest(xla_test.XLATestCase): data, nperseg=ws, noverlap=ws - hs, boundary=None, window=window)[2] expected = np.swapaxes(expected, -1, -2) expected *= window.sum() # scipy divides by window sum - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): ph = array_ops.placeholder( dtypes.as_dtype(data.dtype), shape=data.shape) diff --git a/tensorflow/compiler/tests/fifo_queue_test.py b/tensorflow/compiler/tests/fifo_queue_test.py index 0f64cc87cd..8c7edfd277 100644 --- a/tensorflow/compiler/tests/fifo_queue_test.py +++ b/tensorflow/compiler/tests/fifo_queue_test.py @@ -31,13 +31,13 @@ from tensorflow.python.platform import test class FIFOQueueTest(xla_test.XLATestCase): def testEnqueue(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) enqueue_op = q.enqueue((10.0,)) enqueue_op.run() def testEnqueueWithShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32, shapes=(3, 2)) enqueue_correct_op = q.enqueue(([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],)) enqueue_correct_op.run() @@ -46,7 +46,7 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertEqual(1, q.size().eval()) def testMultipleDequeues(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) self.evaluate(q.enqueue([1])) self.evaluate(q.enqueue([2])) @@ -55,7 +55,7 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertAllEqual(set([1, 2, 3]), set([a, b, c])) def testQueuesDontShare(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) self.evaluate(q.enqueue(1)) q2 = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) @@ -64,13 +64,13 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertAllEqual(self.evaluate(q.dequeue()), 1) def testEnqueueDictWithoutNames(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) with self.assertRaisesRegexp(ValueError, "must have names"): q.enqueue({"a": 12.0}) def testParallelEnqueue(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -95,7 +95,7 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertItemsEqual(elems, results) def testParallelDequeue(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -119,7 +119,7 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertItemsEqual(elems, results) def testDequeue(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) elems = [10.0, 20.0, 30.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -133,7 +133,7 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertEqual([elems[i]], vals) def testEnqueueAndBlockingDequeue(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): q = data_flow_ops.FIFOQueue(3, dtypes_lib.float32) elems = [10.0, 20.0, 30.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -163,7 +163,7 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertEqual([elem], result) def testMultiEnqueueAndDequeue(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): q = data_flow_ops.FIFOQueue(10, (dtypes_lib.int32, dtypes_lib.float32)) elems = [(5, 10.0), (10, 20.0), (15, 30.0)] enqueue_ops = [q.enqueue((x, y)) for x, y in elems] @@ -179,12 +179,12 @@ class FIFOQueueTest(xla_test.XLATestCase): self.assertEqual([y], y_val) def testQueueSizeEmpty(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) self.assertEqual([0], q.size().eval()) def testQueueSizeAfterEnqueueAndDequeue(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): q = data_flow_ops.FIFOQueue(10, dtypes_lib.float32) enqueue_op = q.enqueue((10.0,)) dequeued_t = q.dequeue() diff --git a/tensorflow/compiler/tests/ftrl_test.py b/tensorflow/compiler/tests/ftrl_test.py index 1da97fd512..7ca50b02d9 100644 --- a/tensorflow/compiler/tests/ftrl_test.py +++ b/tensorflow/compiler/tests/ftrl_test.py @@ -112,7 +112,7 @@ class FtrlOptimizerTest(xla_test.XLATestCase): def testFtrlwithoutRegularization(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -146,7 +146,7 @@ class FtrlOptimizerTest(xla_test.XLATestCase): def testFtrlwithoutRegularization2(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -174,7 +174,7 @@ class FtrlOptimizerTest(xla_test.XLATestCase): def testFtrlWithL1(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -202,7 +202,7 @@ class FtrlOptimizerTest(xla_test.XLATestCase): def testFtrlWithL1_L2(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -236,7 +236,7 @@ class FtrlOptimizerTest(xla_test.XLATestCase): weights will tend to have smaller magnitudes with this parameter set. """ for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.2], dtype=dtype) @@ -273,9 +273,9 @@ class FtrlOptimizerTest(xla_test.XLATestCase): def testEquivAdagradwithoutRegularization(self): steps = 5 for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val0, val1 = self.equivAdagradTest_FtrlPart(steps, dtype) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val2, val3 = self.equivAdagradTest_AdagradPart(steps, dtype) self.assertAllCloseAccordingToType(val0, val2, rtol=1e-4, half_rtol=1e-2) @@ -284,9 +284,9 @@ class FtrlOptimizerTest(xla_test.XLATestCase): def testEquivGradientDescentwithoutRegularization(self): steps = 5 for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val0, val1 = self.equivGradientDescentTest_FtrlPart(steps, dtype) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val2, val3 = self.equivGradientDescentTest_GradientDescentPart( steps, dtype) diff --git a/tensorflow/compiler/tests/function_test.py b/tensorflow/compiler/tests/function_test.py index 04fba44446..b1891b918c 100644 --- a/tensorflow/compiler/tests/function_test.py +++ b/tensorflow/compiler/tests/function_test.py @@ -40,7 +40,7 @@ class FunctionTest(xla_test.XLATestCase): bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32) expected = APlus2B(aval, bval) - with self.test_session() as sess: + with self.cached_session() as sess: @function.Defun(dtypes.float32, dtypes.float32) def Foo(a, b): @@ -66,7 +66,7 @@ class FunctionTest(xla_test.XLATestCase): bval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32) expected = APlus2B(aval, bval) - with self.test_session() as sess: + with self.cached_session() as sess: @function.Defun(dtypes.float32, dtypes.float32) def Foo(a, b): @@ -90,7 +90,7 @@ class FunctionTest(xla_test.XLATestCase): bval = np.array([5, 6, 7, 8]).reshape([2, 2]).astype(np.float32) expected = Func(aval, bval) - with self.test_session() as sess: + with self.cached_session() as sess: @function.Defun(dtypes.float32, dtypes.float32) def Foo(a, b): @@ -105,7 +105,7 @@ class FunctionTest(xla_test.XLATestCase): def testCompileTimeConstantsInDefun(self): """Tests that XLA handles compile-time constants in defuns.""" - with self.test_session() as sess: + with self.cached_session() as sess: @function.Defun(dtypes.float32, dtypes.int32, dtypes.int32) def Foo(a, c, d): @@ -140,7 +140,7 @@ class FunctionTest(xla_test.XLATestCase): bval = np.array([4, 3, 2, 1]).reshape([2, 2]).astype(np.float32) expected = aval + bval * 2 - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): a = array_ops.placeholder(dtypes.float32, name="a") b = array_ops.placeholder(dtypes.float32, name="b") diff --git a/tensorflow/compiler/tests/fused_batchnorm_test.py b/tensorflow/compiler/tests/fused_batchnorm_test.py index 132e42ac7a..8c018cccb8 100644 --- a/tensorflow/compiler/tests/fused_batchnorm_test.py +++ b/tensorflow/compiler/tests/fused_batchnorm_test.py @@ -83,7 +83,7 @@ class FusedBatchNormTest(xla_test.XLATestCase, parameterized.TestCase): y_ref, mean_ref, var_ref = self._reference_training( x_val, scale_val, offset_val, epsilon, data_format_src) - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): # To avoid constant folding x_val_converted = test_utils.ConvertBetweenDataFormats( x_val, data_format_src, data_format) @@ -126,7 +126,7 @@ class FusedBatchNormTest(xla_test.XLATestCase, parameterized.TestCase): y_ref, mean_ref, var_ref = self._reference_training( x_val, scale_val, offset_val, epsilon, data_format_src) - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): # To avoid constant folding x_val_converted = test_utils.ConvertBetweenDataFormats( x_val, data_format_src, data_format) @@ -210,7 +210,7 @@ class FusedBatchNormTest(xla_test.XLATestCase, parameterized.TestCase): grad_x_ref, grad_scale_ref, grad_offset_ref = self._reference_grad( x_val, grad_val, scale_val, mean_val, var_val, epsilon, data_format_src) - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): grad_val_converted = test_utils.ConvertBetweenDataFormats( grad_val, data_format_src, data_format) x_val_converted = test_utils.ConvertBetweenDataFormats( @@ -260,7 +260,7 @@ class FusedBatchNormTest(xla_test.XLATestCase, parameterized.TestCase): var_val = np.random.random_sample(scale_shape).astype(np.float32) data_format_src = "NHWC" - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): grad_val_converted = test_utils.ConvertBetweenDataFormats( grad_val, data_format_src, data_format) x_val_converted = test_utils.ConvertBetweenDataFormats( diff --git a/tensorflow/compiler/tests/gather_nd_op_test.py b/tensorflow/compiler/tests/gather_nd_op_test.py index 23b0aed34f..7161f4ab33 100644 --- a/tensorflow/compiler/tests/gather_nd_op_test.py +++ b/tensorflow/compiler/tests/gather_nd_op_test.py @@ -29,7 +29,7 @@ from tensorflow.python.platform import test class GatherNdTest(xla_test.XLATestCase): def _runGather(self, params, indices): - with self.test_session(): + with self.cached_session(): paramsp = array_ops.placeholder(params.dtype) indicesp = array_ops.placeholder(indices.dtype) with self.test_scope(): @@ -46,7 +46,7 @@ class GatherNdTest(xla_test.XLATestCase): np.array([[4], [4], [0]], np.int32))) def testEmptyIndicesAndParamsOKButJustEmptyParamsFails(self): - with self.test_session(): + with self.cached_session(): params = np.ones((3, 3), dtype=np.float32) indices_empty = np.empty((0, 2), dtype=np.int32) diff --git a/tensorflow/compiler/tests/gather_test.py b/tensorflow/compiler/tests/gather_test.py index e9c8ef7c91..089d95daab 100644 --- a/tensorflow/compiler/tests/gather_test.py +++ b/tensorflow/compiler/tests/gather_test.py @@ -42,7 +42,7 @@ class GatherTest(xla_test.XLATestCase): return data def testScalar1D(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): data = np.array([0, 1, 2, 3, 7, 5]) for dtype in self.all_tf_types: for indices in 4, [4], [1, 2, 2, 4, 5]: @@ -55,7 +55,7 @@ class GatherTest(xla_test.XLATestCase): self.assertAllEqual(np_val, gather_val) def testScalar2D(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11], [12, 13, 14]]) for dtype in self.all_tf_types: @@ -69,7 +69,7 @@ class GatherTest(xla_test.XLATestCase): self.assertAllEqual(expected, gather_val) def testSimpleTwoD32(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11], [12, 13, 14]]) for dtype in self.all_tf_types: @@ -87,7 +87,7 @@ class GatherTest(xla_test.XLATestCase): if np.int64 not in self.int_types: return - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): data = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8], [9, 10, 11], [12, 13, 14]]) # The indices must be in bounds for any axis. @@ -114,7 +114,7 @@ class GatherTest(xla_test.XLATestCase): for axis in 0, 1, 2, 3, -1, -2: params = self._buildParams(np.random.randn(*shape), dtype) indices = np.random.randint(shape[axis], size=indices_shape) - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): tf_params = array_ops.placeholder(dtype=dtype) tf_indices = constant_op.constant(indices, dtype=dtypes.int32) gather = array_ops.gather(tf_params, tf_indices, axis=axis) @@ -123,7 +123,7 @@ class GatherTest(xla_test.XLATestCase): self.assertAllEqual(gather_np, gather_value) def testIndicesWithDifferentDimensions(self): - with self.test_session(): + with self.cached_session(): for dtype in self.numeric_tf_types: params = array_ops.placeholder(dtype=dtype) indices = array_ops.placeholder(dtype=np.int32) @@ -137,7 +137,7 @@ class GatherTest(xla_test.XLATestCase): [[7]], gather.eval(feed_dict={params: [4, 7, 2], indices: [[1]]})) def testGatherPrecision(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): data = np.array([[0, 0, 0, 0], [0, 2 * (1 + np.exp2(-8)), 0, 0], [0, 0, 0, 0], [0.015789, 0.0985, 0.55789, 0.3842]]) indices = np.array([1, 2, 3, 1]) diff --git a/tensorflow/compiler/tests/image_ops_test.py b/tensorflow/compiler/tests/image_ops_test.py index bf986ade06..1a82fcbb2a 100644 --- a/tensorflow/compiler/tests/image_ops_test.py +++ b/tensorflow/compiler/tests/image_ops_test.py @@ -54,7 +54,7 @@ class RGBToHSVTest(xla_test.XLATestCase): inp = GenerateNumpyRandomRGB(shape).astype(nptype) # Convert to HSV and back, as a batch and individually - with self.test_session() as sess: + with self.cached_session() as sess: batch0 = array_ops.placeholder(nptype, shape=shape) with self.test_scope(): batch1 = image_ops.rgb_to_hsv(batch0) @@ -78,7 +78,7 @@ class RGBToHSVTest(xla_test.XLATestCase): data = [0, 5, 13, 54, 135, 226, 37, 8, 234, 90, 255, 1] for nptype in self.float_types: rgb_np = np.array(data, dtype=nptype).reshape([2, 2, 3]) / 255. - with self.test_session(): + with self.cached_session(): placeholder = array_ops.placeholder(nptype) with self.test_scope(): hsv = image_ops.rgb_to_hsv(placeholder) @@ -97,7 +97,7 @@ class RGBToHSVTest(xla_test.XLATestCase): for r, g, b in rgb_flat ]) hsv_np = hsv_np.reshape(4, 4, 4, 3) - with self.test_session(): + with self.cached_session(): placeholder = array_ops.placeholder(nptype) with self.test_scope(): hsv_op = image_ops.rgb_to_hsv(placeholder) @@ -108,7 +108,7 @@ class RGBToHSVTest(xla_test.XLATestCase): class AdjustContrastTest(xla_test.XLATestCase): def _testContrast(self, x_np, y_np, contrast_factor): - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(x_np.dtype, shape=x_np.shape) flt_x = image_ops.convert_image_dtype(x, dtypes.float32) with self.test_scope(): @@ -146,7 +146,7 @@ class AdjustContrastTest(xla_test.XLATestCase): return y_np def _adjustContrastTf(self, x_np, contrast_factor): - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(np.float32) with self.test_scope(): y = image_ops.adjust_contrast(x, contrast_factor) @@ -180,7 +180,7 @@ class AdjustHueTest(xla_test.XLATestCase): y_data = [0, 13, 1, 54, 226, 59, 8, 234, 150, 255, 39, 1] y_np = np.array(y_data, dtype=np.uint8).reshape(x_shape) - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(x_np.dtype, shape=x_shape) flt_x = image_ops.convert_image_dtype(x, dtypes.float32) with self.test_scope(): @@ -198,7 +198,7 @@ class AdjustHueTest(xla_test.XLATestCase): y_data = [13, 0, 11, 226, 54, 221, 234, 8, 92, 1, 217, 255] y_np = np.array(y_data, dtype=np.uint8).reshape(x_shape) - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(x_np.dtype, shape=x_shape) flt_x = image_ops.convert_image_dtype(x, dtypes.float32) with self.test_scope(): @@ -216,7 +216,7 @@ class AdjustHueTest(xla_test.XLATestCase): y_data = [13, 0, 11, 226, 54, 221, 234, 8, 92, 1, 217, 255] y_np = np.array(y_data, dtype=np.uint8).reshape(x_shape) - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(x_np.dtype, shape=x_shape) flt_x = image_ops.convert_image_dtype(x, dtypes.float32) with self.test_scope(): @@ -244,7 +244,7 @@ class AdjustHueTest(xla_test.XLATestCase): return y_v.reshape(x_np.shape) def _adjustHueTf(self, x_np, delta_h): - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(dtypes.float32) with self.test_scope(): y = gen_image_ops.adjust_hue(x, delta_h) @@ -324,7 +324,7 @@ class AdjustSaturationTest(xla_test.XLATestCase): y_rgb_data = [6, 9, 13, 140, 180, 226, 135, 121, 234, 172, 255, 128] y_np = np.array(y_rgb_data, dtype=np.uint8).reshape(x_shape) - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(x_np.dtype, shape=x_shape) y = self._adjust_saturation(x, saturation_factor) y_tf = y.eval({x: x_np}) @@ -339,7 +339,7 @@ class AdjustSaturationTest(xla_test.XLATestCase): y_data = [0, 5, 13, 0, 106, 226, 30, 0, 234, 89, 255, 0] y_np = np.array(y_data, dtype=np.uint8).reshape(x_shape) - with self.test_session(): + with self.cached_session(): x = array_ops.placeholder(x_np.dtype, shape=x_shape) y = self._adjust_saturation(x, saturation_factor) y_tf = y.eval({x: x_np}) @@ -378,7 +378,7 @@ class AdjustSaturationTest(xla_test.XLATestCase): "gb_same", "rgb_same", ] - with self.test_session(): + with self.cached_session(): for x_shape in x_shapes: for test_style in test_styles: x_np = np.random.rand(*x_shape) * 255. @@ -413,7 +413,7 @@ class ResizeBilinearTest(xla_test.XLATestCase): large_tolerance=False): if expected is None: self.fail("expected must be specified") - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): image = array_ops.placeholder(image_np.dtype) resized = gen_image_ops.resize_bilinear( image, target_shape, align_corners=True) @@ -433,7 +433,7 @@ class ResizeBilinearTest(xla_test.XLATestCase): self.fail("input_shape must be specified") if expected is None: self.fail("expected must be specified") - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): dtype = dtype or np.float32 grads = array_ops.placeholder(np.float32) resized = gen_image_ops.resize_bilinear_grad( @@ -596,7 +596,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase): iou_threshold_np = np.array(0.5, dtype=np.float32) score_threshold_np = np.array(0.0, dtype=np.float32) - with self.test_session() as sess: + with self.cached_session() as sess: boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape) scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape) iou_threshold = array_ops.placeholder(iou_threshold_np.dtype, @@ -639,7 +639,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase): iou_threshold_np = np.array(0.5, dtype=np.float32) score_threshold_np = np.array(0.0, dtype=np.float32) - with self.test_session() as sess: + with self.cached_session() as sess: boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape) scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape) iou_threshold = array_ops.placeholder(iou_threshold_np.dtype, @@ -686,7 +686,7 @@ class NonMaxSuppressionTest(xla_test.XLATestCase): iou_threshold_np = np.array(0.5, dtype=np.float32) score_threshold_np = np.array(0.4, dtype=np.float32) - with self.test_session() as sess: + with self.cached_session() as sess: boxes = array_ops.placeholder(boxes_np.dtype, shape=boxes_np.shape) scores = array_ops.placeholder(scores_np.dtype, shape=scores_np.shape) iou_threshold = array_ops.placeholder(iou_threshold_np.dtype, diff --git a/tensorflow/compiler/tests/listdiff_op_test.py b/tensorflow/compiler/tests/listdiff_op_test.py index 45a04f0cf5..58622114e4 100644 --- a/tensorflow/compiler/tests/listdiff_op_test.py +++ b/tensorflow/compiler/tests/listdiff_op_test.py @@ -33,7 +33,7 @@ class ListDiffTest(xla_test.XLATestCase): def _testListDiff(self, x, y, out, idx): for dtype in [dtypes.int32, dtypes.int64]: for index_dtype in [dtypes.int32, dtypes.int64]: - with self.test_session() as sess: + with self.cached_session() as sess: x_tensor = ops.convert_to_tensor(x, dtype=dtype) y_tensor = ops.convert_to_tensor(y, dtype=dtype) with self.test_scope(): diff --git a/tensorflow/compiler/tests/lrn_ops_test.py b/tensorflow/compiler/tests/lrn_ops_test.py index 253b45902f..c6ad67993e 100644 --- a/tensorflow/compiler/tests/lrn_ops_test.py +++ b/tensorflow/compiler/tests/lrn_ops_test.py @@ -58,7 +58,7 @@ class LRNTest(xla_test.XLATestCase): return output def _RunAndVerify(self, dtype): - with self.test_session(): + with self.cached_session(): # random shape shape = np.random.randint(1, 16, size=4) # Make depth at least 2 to make it meaningful @@ -110,7 +110,7 @@ class LRNTest(xla_test.XLATestCase): alpha = 1.0 * np.random.rand() beta = 1.0 * np.random.rand() - with self.test_session(): + with self.cached_session(): in_image = constant_op.constant(in_image_vals, shape=shape) out_image = constant_op.constant(out_image_vals, shape=shape) out_grads = constant_op.constant(out_grads_vals, shape=shape) diff --git a/tensorflow/compiler/tests/lstm_test.py b/tensorflow/compiler/tests/lstm_test.py index 31093c6571..265c0b6d14 100644 --- a/tensorflow/compiler/tests/lstm_test.py +++ b/tensorflow/compiler/tests/lstm_test.py @@ -73,7 +73,7 @@ class LSTMTest(test.TestCase): def _RunLSTMCell(self, basename, init_weights, m_prev_scalar, c_prev_scalar, pad_scalar): - with self.test_session() as sess: + with self.cached_session() as sess: num_inputs = 1 num_nodes = 1 @@ -156,7 +156,7 @@ class LSTMTest(test.TestCase): def _RunLSTMLayer(self, basename, init_weights, m_init_scalar, c_init_scalar, pad_scalar): - with self.test_session() as sess: + with self.cached_session() as sess: num_inputs = 1 num_nodes = 1 seq_length = 3 diff --git a/tensorflow/compiler/tests/matrix_band_part_test.py b/tensorflow/compiler/tests/matrix_band_part_test.py index 0d9f99f8a6..9222db4b7e 100644 --- a/tensorflow/compiler/tests/matrix_band_part_test.py +++ b/tensorflow/compiler/tests/matrix_band_part_test.py @@ -29,7 +29,7 @@ from tensorflow.python.platform import test class MatrixBandPartTest(xla_test.XLATestCase): def _testMatrixBandPart(self, dtype, shape): - with self.test_session(): + with self.cached_session(): batch_shape = shape[:-2] mat = np.ones(shape).astype(dtype) batch_mat = np.tile(mat, batch_shape + [1, 1]) diff --git a/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py b/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py index 2bb8a97bda..94cd3eeb31 100644 --- a/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py +++ b/tensorflow/compiler/tests/matrix_triangular_solve_op_test.py @@ -54,7 +54,7 @@ class MatrixTriangularSolveOpTest(xla_test.XLATestCase): def _VerifyTriangularSolve(self, a, b, lower, adjoint, atol): clean_a = np.tril(a) if lower else np.triu(a) - with self.test_session() as sess: + with self.cached_session() as sess: placeholder_a = MakePlaceholder(a) placeholder_ca = MakePlaceholder(clean_a) placeholder_b = MakePlaceholder(b) diff --git a/tensorflow/compiler/tests/momentum_test.py b/tensorflow/compiler/tests/momentum_test.py index c2592c54cf..f77521a7c4 100644 --- a/tensorflow/compiler/tests/momentum_test.py +++ b/tensorflow/compiler/tests/momentum_test.py @@ -41,7 +41,7 @@ class MomentumOptimizerTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) @@ -95,7 +95,7 @@ class MomentumOptimizerTest(xla_test.XLATestCase): def testNesterovMomentum(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([0.1, 0.2], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([0.3, 0.4], dtype=dtype) var0_np = np.array([0.1, 0.2], dtype=dtype) @@ -120,7 +120,7 @@ class MomentumOptimizerTest(xla_test.XLATestCase): def testTensorLearningRateAndMomentum(self): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) diff --git a/tensorflow/compiler/tests/nary_ops_test.py b/tensorflow/compiler/tests/nary_ops_test.py index da08225e9f..a1c07fce73 100644 --- a/tensorflow/compiler/tests/nary_ops_test.py +++ b/tensorflow/compiler/tests/nary_ops_test.py @@ -32,7 +32,7 @@ from tensorflow.python.platform import googletest class NAryOpsTest(xla_test.XLATestCase): def _testNAry(self, op, args, expected, equality_fn=None): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): placeholders = [ array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape) @@ -126,7 +126,7 @@ class NAryOpsTest(xla_test.XLATestCase): [[1, 2, 3, 7, 8, 9], [4, 5, 6, 10, 11, 12]], dtype=np.float32)) def testOneHot(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): indices = array_ops.constant(np.array([[2, 3], [0, 1]], dtype=np.int32)) op = array_ops.one_hot(indices, np.int32(4), @@ -148,7 +148,7 @@ class NAryOpsTest(xla_test.XLATestCase): self.assertAllEqual(output, expected) def testSplitV(self): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): output = session.run( array_ops.split(np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 0, 1, 2]], diff --git a/tensorflow/compiler/tests/nullary_ops_test.py b/tensorflow/compiler/tests/nullary_ops_test.py index 2f9122645d..f985c5d2d9 100644 --- a/tensorflow/compiler/tests/nullary_ops_test.py +++ b/tensorflow/compiler/tests/nullary_ops_test.py @@ -29,14 +29,14 @@ from tensorflow.python.platform import googletest class NullaryOpsTest(xla_test.XLATestCase): def _testNullary(self, op, expected): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): output = op() result = session.run(output) self.assertAllClose(result, expected, rtol=1e-3) def testNoOp(self): - with self.test_session(): + with self.cached_session(): with self.test_scope(): output = control_flow_ops.no_op() # This should not crash. diff --git a/tensorflow/compiler/tests/oom_test.py b/tensorflow/compiler/tests/oom_test.py index d68d32057a..7635f89249 100644 --- a/tensorflow/compiler/tests/oom_test.py +++ b/tensorflow/compiler/tests/oom_test.py @@ -46,7 +46,7 @@ class OutOfMemoryTest(xla_test.XLATestCase): def test_loop(): size = int(2e8) while True: - with self.test_session(): + with self.cached_session(): # Force the compiled code to not be constant by feeding in a # parameter. p = array_ops.placeholder(dtypes.float32, shape=[2, 1, 1]) diff --git a/tensorflow/compiler/tests/placeholder_test.py b/tensorflow/compiler/tests/placeholder_test.py index a75d99189b..77bb839409 100644 --- a/tensorflow/compiler/tests/placeholder_test.py +++ b/tensorflow/compiler/tests/placeholder_test.py @@ -28,7 +28,7 @@ from tensorflow.python.platform import googletest class PlaceholderTest(xla_test.XLATestCase): def test_placeholder_with_default_default(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): v = resource_variable_ops.ResourceVariable(4.0) ph = array_ops.placeholder_with_default(v, shape=[]) out = ph * 2 @@ -36,7 +36,7 @@ class PlaceholderTest(xla_test.XLATestCase): self.assertEqual(8.0, sess.run(out)) def test_placeholder_with_default_fed(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): v = resource_variable_ops.ResourceVariable(4.0) ph = array_ops.placeholder_with_default(v, shape=[]) out = ph * 2 diff --git a/tensorflow/compiler/tests/pooling_ops_3d_test.py b/tensorflow/compiler/tests/pooling_ops_3d_test.py index 17f860db61..b6cdd38345 100644 --- a/tensorflow/compiler/tests/pooling_ops_3d_test.py +++ b/tensorflow/compiler/tests/pooling_ops_3d_test.py @@ -62,7 +62,7 @@ class Pooling3DTest(xla_test.XLATestCase): # numbers from 1. x = np.arange(1.0, total_size + 1, dtype=np.float32) x = x.reshape(input_sizes) - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): inputs = array_ops.placeholder(dtypes.float32) t = pool_func( inputs, @@ -210,7 +210,7 @@ class Pooling3DTest(xla_test.XLATestCase): strides = [1] + strides + [1] total_size = np.prod(input_sizes) x = np.arange(1, total_size + 1, dtype=np.float32).reshape(input_sizes) - with self.test_session() as sess: + with self.cached_session() as sess: # Use the forward pool function to compute some corresponding outputs # (needed for the CPU device, and we need the shape in both cases). with ops.device("CPU"): diff --git a/tensorflow/compiler/tests/pooling_ops_test.py b/tensorflow/compiler/tests/pooling_ops_test.py index 9fc94752ea..d03bd4fdbb 100644 --- a/tensorflow/compiler/tests/pooling_ops_test.py +++ b/tensorflow/compiler/tests/pooling_ops_test.py @@ -89,7 +89,7 @@ class PoolingTest(xla_test.XLATestCase): # numbers from 1. x = np.array([f * 1.0 for f in range(1, total_size + 1)], dtype=np.float32) x = x.reshape(input_sizes) - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): inputs = array_ops.placeholder(dtypes.float32) t = inputs @@ -324,7 +324,7 @@ class PoolGradTest(xla_test.XLATestCase): # TODO(b/74222344): Fix nan handling for max pool grad. # x[np.random.choice(total_size)] = np.nan x = x.reshape(input_sizes) - with self.test_session() as sess: + with self.cached_session() as sess: # Use the forward pool function to compute some corresponding outputs # (needed for the CPU device, and we need the shape in both cases). with ops.device(self.CPU_DEVICE): diff --git a/tensorflow/compiler/tests/powersign_test.py b/tensorflow/compiler/tests/powersign_test.py index 5fa7706d72..86536da7fe 100644 --- a/tensorflow/compiler/tests/powersign_test.py +++ b/tensorflow/compiler/tests/powersign_test.py @@ -64,7 +64,7 @@ class PowerSignTest(xla_test.XLATestCase): base=math.e, beta=0.9): for dtype in self.float_types: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): # Initialize variables for numpy implementation. m0, m1 = 0.0, 0.0 var0_np = np.array([1.0, 2.0], dtype=dtype) diff --git a/tensorflow/compiler/tests/proximal_adagrad_test.py b/tensorflow/compiler/tests/proximal_adagrad_test.py index cde87db63d..c41b4171e2 100644 --- a/tensorflow/compiler/tests/proximal_adagrad_test.py +++ b/tensorflow/compiler/tests/proximal_adagrad_test.py @@ -32,7 +32,7 @@ from tensorflow.python.training import proximal_adagrad class ProximalAdagradOptimizerTest(xla_test.XLATestCase): def testResourceProximalAdagradwithoutRegularization(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([0.0, 0.0]) var1 = resource_variable_ops.ResourceVariable([0.0, 0.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -60,7 +60,7 @@ class ProximalAdagradOptimizerTest(xla_test.XLATestCase): self.assertEqual(2, len(opt_vars)) def testProximalAdagradwithoutRegularization2(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -84,7 +84,7 @@ class ProximalAdagradOptimizerTest(xla_test.XLATestCase): self.assertAllClose(np.array([3.715679, 2.433051]), var1.eval()) def testProximalAdagradWithL1(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -108,7 +108,7 @@ class ProximalAdagradOptimizerTest(xla_test.XLATestCase): self.assertAllClose(np.array([2.959304, 1.029232]), var1.eval()) def testProximalAdagradWithL1_L2(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -151,7 +151,7 @@ class ProximalAdagradOptimizerTest(xla_test.XLATestCase): return var0.eval(), var1.eval() def testEquivAdagradwithoutRegularization(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val0, val1 = self.applyOptimizer( proximal_adagrad.ProximalAdagradOptimizer( 3.0, @@ -159,7 +159,7 @@ class ProximalAdagradOptimizerTest(xla_test.XLATestCase): l1_regularization_strength=0.0, l2_regularization_strength=0.0)) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val2, val3 = self.applyOptimizer( adagrad.AdagradOptimizer( 3.0, initial_accumulator_value=0.1)) diff --git a/tensorflow/compiler/tests/proximal_gradient_descent_test.py b/tensorflow/compiler/tests/proximal_gradient_descent_test.py index 11eb768711..3d808e6b8a 100644 --- a/tensorflow/compiler/tests/proximal_gradient_descent_test.py +++ b/tensorflow/compiler/tests/proximal_gradient_descent_test.py @@ -32,7 +32,7 @@ from tensorflow.python.training import proximal_gradient_descent class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase): def testResourceProximalGradientDescentwithoutRegularization(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([0.0, 0.0]) var1 = resource_variable_ops.ResourceVariable([0.0, 0.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -53,7 +53,7 @@ class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase): self.assertAllClose(np.array([-0.09, -0.18]), var1.eval()) def testProximalGradientDescentwithoutRegularization2(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -75,7 +75,7 @@ class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase): self.assertAllClose(np.array([3.91, 2.82]), var1.eval()) def testProximalGradientDescentWithL1(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -97,7 +97,7 @@ class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase): self.assertAllClose(np.array([3.67, 2.37]), var1.eval()) def testProximalGradientDescentWithL1_L2(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): var0 = resource_variable_ops.ResourceVariable([1.0, 2.0]) var1 = resource_variable_ops.ResourceVariable([4.0, 3.0]) grads0 = constant_op.constant([0.1, 0.2]) @@ -137,14 +137,14 @@ class ProximalGradientDescentOptimizerTest(xla_test.XLATestCase): return var0.eval(), var1.eval() def testEquivGradientDescentwithoutRegularization(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val0, val1 = self.applyOptimizer( proximal_gradient_descent.ProximalGradientDescentOptimizer( 3.0, l1_regularization_strength=0.0, l2_regularization_strength=0.0)) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): val2, val3 = self.applyOptimizer( gradient_descent.GradientDescentOptimizer(3.0)) diff --git a/tensorflow/compiler/tests/qr_op_test.py b/tensorflow/compiler/tests/qr_op_test.py index 1b969ee2b3..3a268978bf 100644 --- a/tensorflow/compiler/tests/qr_op_test.py +++ b/tensorflow/compiler/tests/qr_op_test.py @@ -71,7 +71,7 @@ class QrOpTest(xla_test.XLATestCase, parameterized.TestCase): x_np = np.random.uniform( low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype) - with self.test_session() as sess: + with self.cached_session() as sess: x_tf = array_ops.placeholder(dtype) with self.test_scope(): q_tf, r_tf = linalg_ops.qr(x_tf, full_matrices=full_matrices) diff --git a/tensorflow/compiler/tests/random_ops_test.py b/tensorflow/compiler/tests/random_ops_test.py index 8c4e16e4e0..6e18344117 100644 --- a/tensorflow/compiler/tests/random_ops_test.py +++ b/tensorflow/compiler/tests/random_ops_test.py @@ -39,7 +39,7 @@ class RandomOpsTest(xla_test.XLATestCase): def _testRngIsNotConstant(self, rng, dtype): # Tests that 'rng' does not always return the same value. - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = rng(dtype) @@ -79,7 +79,7 @@ class RandomOpsTest(xla_test.XLATestCase): if (self.device in ["XLA_GPU", "XLA_CPU" ]) and (dtype in [dtypes.bfloat16, dtypes.half]): continue - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = random_ops.random_uniform( shape=[1000], dtype=dtype, minval=-2, maxval=33) @@ -99,7 +99,7 @@ class RandomOpsTest(xla_test.XLATestCase): count = 10000000 # TODO(b/34339814): implement inverse erf support for non-F32 types. for dtype in [dtypes.float32]: - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = random_ops.truncated_normal(shape=[count], dtype=dtype) y = sess.run(x) @@ -147,7 +147,7 @@ class RandomOpsTest(xla_test.XLATestCase): # TODO(b/26783907): this test requires the CPU backend to implement sort. if self.device in ["XLA_CPU"]: return - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = math_ops.range(1 << 16) shuffle = random_ops.random_shuffle(x) @@ -158,7 +158,7 @@ class RandomOpsTest(xla_test.XLATestCase): self.assertAllEqual(set(result), set(expected)) def testShuffle2d(self): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = array_ops.diag(math_ops.range(20)) shuffle = random_ops.random_shuffle(x) diff --git a/tensorflow/compiler/tests/reduce_ops_test.py b/tensorflow/compiler/tests/reduce_ops_test.py index cea2ec816f..6db8559b0b 100644 --- a/tensorflow/compiler/tests/reduce_ops_test.py +++ b/tensorflow/compiler/tests/reduce_ops_test.py @@ -42,7 +42,7 @@ class ReduceOpsTest(xla_test.XLATestCase): """Tests that the output of 'tf_reduce_fn' matches numpy's output.""" for test_input in test_inputs: - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): a = array_ops.placeholder(dtype) index = array_ops.placeholder(dtypes.int32) @@ -178,7 +178,7 @@ class ReduceOpPrecisionTest(xla_test.XLATestCase): """ for test_input in test_inputs: - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): a = array_ops.placeholder(dtype) index = array_ops.placeholder(dtypes.int32) diff --git a/tensorflow/compiler/tests/reduce_window_test.py b/tensorflow/compiler/tests/reduce_window_test.py index c69b6837b0..ff20ea3f42 100644 --- a/tensorflow/compiler/tests/reduce_window_test.py +++ b/tensorflow/compiler/tests/reduce_window_test.py @@ -32,7 +32,7 @@ class ReduceWindowTest(xla_test.XLATestCase): """Test cases for xla.reduce_window.""" def _reduce_window(self, operand, init, reducer, **kwargs): - with self.test_session(): + with self.cached_session(): placeholder = array_ops.placeholder(operand.dtype) with self.test_scope(): output = xla.reduce_window(placeholder, init, reducer, **kwargs) diff --git a/tensorflow/compiler/tests/reverse_ops_test.py b/tensorflow/compiler/tests/reverse_ops_test.py index 32ab5d08f0..392290fd92 100644 --- a/tensorflow/compiler/tests/reverse_ops_test.py +++ b/tensorflow/compiler/tests/reverse_ops_test.py @@ -51,7 +51,7 @@ class ReverseOpsTest(xla_test.XLATestCase): def _AssertReverseEqual(self, revdims, shape): np.random.seed(120) pval = np.random.randint(0, 100, size=shape).astype(float) - with self.test_session(): + with self.cached_session(): with self.test_scope(): p = array_ops.placeholder(dtypes.int32, shape=shape) axis = constant_op.constant( diff --git a/tensorflow/compiler/tests/reverse_sequence_op_test.py b/tensorflow/compiler/tests/reverse_sequence_op_test.py index ccfa630016..60c2337743 100644 --- a/tensorflow/compiler/tests/reverse_sequence_op_test.py +++ b/tensorflow/compiler/tests/reverse_sequence_op_test.py @@ -35,7 +35,7 @@ class ReverseSequenceTest(xla_test.XLATestCase): seq_lengths, truth, expected_err_re=None): - with self.test_session(): + with self.cached_session(): p = array_ops.placeholder(dtypes.as_dtype(x.dtype)) lengths = array_ops.placeholder(dtypes.as_dtype(seq_lengths.dtype)) with self.test_scope(): diff --git a/tensorflow/compiler/tests/rmsprop_test.py b/tensorflow/compiler/tests/rmsprop_test.py index ff8bbac911..8840a1329a 100644 --- a/tensorflow/compiler/tests/rmsprop_test.py +++ b/tensorflow/compiler/tests/rmsprop_test.py @@ -55,7 +55,7 @@ class RmspropTest(xla_test.XLATestCase): def testBasic(self): for dtype in self.float_types: for centered in [False, True]: - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): # Initialize variables for numpy implementation. var0_np = np.array([1.0, 2.0], dtype=dtype) grads0_np = np.array([0.1, 0.1], dtype=dtype) diff --git a/tensorflow/compiler/tests/scan_ops_test.py b/tensorflow/compiler/tests/scan_ops_test.py index 4292352e76..897db384b7 100644 --- a/tensorflow/compiler/tests/scan_ops_test.py +++ b/tensorflow/compiler/tests/scan_ops_test.py @@ -78,7 +78,7 @@ class CumsumTest(xla_test.XLATestCase): def _compare(self, x, axis, exclusive, reverse): np_out = handle_options(np.cumsum, x, axis, exclusive, reverse) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): p = array_ops.placeholder(x.dtype) tf_out = math_ops.cumsum(p, axis, exclusive, reverse).eval( feed_dict={p: x}) @@ -100,7 +100,7 @@ class CumsumTest(xla_test.XLATestCase): for dtype in self.valid_dtypes: x = np.arange(1, 6).reshape([5]).astype(dtype) for axis_dtype in self.axis_dtypes(): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): p = array_ops.placeholder(x.dtype) axis = constant_op.constant(0, axis_dtype) math_ops.cumsum(p, axis).eval(feed_dict={p: x}) @@ -131,7 +131,7 @@ class CumsumTest(xla_test.XLATestCase): def testInvalidAxis(self): x = np.arange(0, 10).reshape([2, 5]).astype(np.float32) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): input_tensor = ops.convert_to_tensor(x) with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, @@ -156,7 +156,7 @@ class CumprodTest(xla_test.XLATestCase): def _compare(self, x, axis, exclusive, reverse): np_out = handle_options(np.cumprod, x, axis, exclusive, reverse) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): p = array_ops.placeholder(x.dtype) prod = math_ops.cumprod(p, axis, exclusive, reverse) tf_out = prod.eval(feed_dict={p: x}) @@ -178,7 +178,7 @@ class CumprodTest(xla_test.XLATestCase): for dtype in self.valid_dtypes: x = np.arange(1, 6).reshape([5]).astype(dtype) for axis_dtype in self.axis_dtypes(): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): p = array_ops.placeholder(x.dtype) axis = constant_op.constant(0, axis_dtype) math_ops.cumprod(x, axis).eval(feed_dict={p: x}) @@ -209,7 +209,7 @@ class CumprodTest(xla_test.XLATestCase): def testInvalidAxis(self): x = np.arange(0, 10).reshape([2, 5]).astype(np.float32) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): input_tensor = ops.convert_to_tensor(x) with self.assertRaisesWithPredicateMatch( errors_impl.InvalidArgumentError, diff --git a/tensorflow/compiler/tests/scatter_nd_op_test.py b/tensorflow/compiler/tests/scatter_nd_op_test.py index f606f88545..693f8513bc 100644 --- a/tensorflow/compiler/tests/scatter_nd_op_test.py +++ b/tensorflow/compiler/tests/scatter_nd_op_test.py @@ -119,7 +119,7 @@ class ScatterNdTest(xla_test.XLATestCase): self._VariableRankTest(np_scatter, tf_scatter, vtype, itype) def _runScatterNd(self, indices, updates, shape): - with self.test_session(): + with self.cached_session(): updates_placeholder = array_ops.placeholder(updates.dtype) indices_placeholder = array_ops.placeholder(indices.dtype) with self.test_scope(): diff --git a/tensorflow/compiler/tests/segment_reduction_ops_test.py b/tensorflow/compiler/tests/segment_reduction_ops_test.py index 772c20fd42..287bb0d84e 100644 --- a/tensorflow/compiler/tests/segment_reduction_ops_test.py +++ b/tensorflow/compiler/tests/segment_reduction_ops_test.py @@ -32,7 +32,7 @@ class SegmentReductionOpsTest(xla_test.XLATestCase): """Test cases for segment reduction ops.""" def _segmentReduction(self, op, data, indices, num_segments): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): d = array_ops.placeholder(data.dtype, shape=data.shape) if isinstance(indices, int): i = array_ops.placeholder(np.int32, shape=[]) diff --git a/tensorflow/compiler/tests/slice_ops_test.py b/tensorflow/compiler/tests/slice_ops_test.py index 6c4890565d..8f10c2fe86 100644 --- a/tensorflow/compiler/tests/slice_ops_test.py +++ b/tensorflow/compiler/tests/slice_ops_test.py @@ -29,7 +29,7 @@ class SliceTest(xla_test.XLATestCase): def test1D(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[10]) with self.test_scope(): o = array_ops.slice(i, [2], [4]) @@ -42,7 +42,7 @@ class SliceTest(xla_test.XLATestCase): def test3D(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[3, 3, 10]) with self.test_scope(): o = array_ops.slice(i, [1, 2, 2], [1, 1, 4]) @@ -64,7 +64,7 @@ class SliceTest(xla_test.XLATestCase): def test3DWithDynamicBegin(self): """Tests a slice where the start offset is not known at compile time.""" for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[3, 3, 10]) begin = array_ops.placeholder(dtypes.int32, shape=[3]) with self.test_scope(): @@ -88,7 +88,7 @@ class SliceTest(xla_test.XLATestCase): def test3DWithDynamicBeginAndNegativeSize(self): """Tests a slice where `begin` is fed dynamically and `size` contains -1.""" for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[3, 3, 10]) begin = array_ops.placeholder(dtypes.int32, shape=[3]) with self.test_scope(): @@ -114,7 +114,7 @@ class StridedSliceTest(xla_test.XLATestCase): def test1D(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[10]) with self.test_scope(): o = array_ops.strided_slice(i, [2], [6], [2]) @@ -127,7 +127,7 @@ class StridedSliceTest(xla_test.XLATestCase): def test1DNegativeStride(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[10]) with self.test_scope(): o = array_ops.strided_slice(i, [6], [2], [-2]) @@ -140,7 +140,7 @@ class StridedSliceTest(xla_test.XLATestCase): def test2DDegenerate(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[2, 3]) with self.test_scope(): o = array_ops.strided_slice(i, [-1, 0], [0, 3]) @@ -154,7 +154,7 @@ class StridedSliceTest(xla_test.XLATestCase): def test2DDegenerateNegativeStride(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[2, 3]) with self.test_scope(): o = array_ops.strided_slice(i, [0, 0], [-1, 3], [-1, 1]) @@ -168,7 +168,7 @@ class StridedSliceTest(xla_test.XLATestCase): def test3D(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[3, 3, 10]) with self.test_scope(): o = array_ops.strided_slice(i, [0, 2, 2], [2, 3, 6], [1, 1, 2]) @@ -189,7 +189,7 @@ class StridedSliceTest(xla_test.XLATestCase): def test3DNegativeStride(self): for dtype in self.numeric_types: - with self.test_session(): + with self.cached_session(): i = array_ops.placeholder(dtype, shape=[3, 4, 10]) with self.test_scope(): o = array_ops.strided_slice(i, [2, 2, 6], [0, 0, 2], [-1, -1, -2]) diff --git a/tensorflow/compiler/tests/sort_ops_test.py b/tensorflow/compiler/tests/sort_ops_test.py index 7ff01be3cb..51c04b5c47 100644 --- a/tensorflow/compiler/tests/sort_ops_test.py +++ b/tensorflow/compiler/tests/sort_ops_test.py @@ -32,7 +32,7 @@ from tensorflow.python.platform import test class XlaSortOpTest(xla_test.XLATestCase): def _assertOpOutputMatchesExpected(self, op, args, expected): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): placeholders = [ array_ops.placeholder(dtypes.as_dtype(arg.dtype), arg.shape) @@ -131,7 +131,7 @@ class XlaSortOpTest(xla_test.XLATestCase): if bfloat16 not in self.numeric_types: return - with self.test_session() as sess: + with self.cached_session() as sess: p = array_ops.placeholder(dtypes.bfloat16) with self.test_scope(): topk = nn_ops.top_k(p, k=4) @@ -153,7 +153,7 @@ class XlaSortOpTest(xla_test.XLATestCase): if bfloat16 not in self.numeric_types: return - with self.test_session() as sess: + with self.cached_session() as sess: p = array_ops.placeholder(dtypes.bfloat16) with self.test_scope(): topk = nn_ops.top_k(p, k=6) diff --git a/tensorflow/compiler/tests/spacetobatch_op_test.py b/tensorflow/compiler/tests/spacetobatch_op_test.py index c685bc548f..33b84cec71 100644 --- a/tensorflow/compiler/tests/spacetobatch_op_test.py +++ b/tensorflow/compiler/tests/spacetobatch_op_test.py @@ -72,7 +72,7 @@ class SpaceToBatchTest(xla_test.XLATestCase): """Tests input-output pairs for the SpaceToBatch and BatchToSpace ops.""" def _testPad(self, inputs, paddings, block_size, outputs): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): for dtype in self.float_types: # outputs = space_to_batch(inputs) placeholder = array_ops.placeholder(dtype) @@ -155,7 +155,7 @@ class SpaceToBatchNDTest(xla_test.XLATestCase): def _testPad(self, inputs, block_shape, paddings, outputs): block_shape = np.array(block_shape) paddings = np.array(paddings).reshape((len(block_shape), 2)) - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): for dtype in self.float_types: # TODO(b/68813416): Skip bfloat16's as the input type for direct is # float32 and results in a mismatch, while making testDirect provide the diff --git a/tensorflow/compiler/tests/sparse_to_dense_op_test.py b/tensorflow/compiler/tests/sparse_to_dense_op_test.py index 3db8101c4b..07afd1ab3f 100644 --- a/tensorflow/compiler/tests/sparse_to_dense_op_test.py +++ b/tensorflow/compiler/tests/sparse_to_dense_op_test.py @@ -45,32 +45,32 @@ def _SparseToDense(sparse_indices, class SparseToDenseTest(xla_test.XLATestCase): def testInt(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): tf_ans = _SparseToDense([1, 3], [5], 1, 0) np_ans = np.array([0, 1, 0, 1, 0]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def testFloat(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): tf_ans = _SparseToDense([1, 3], [5], 1.0, 0.0) np_ans = np.array([0, 1, 0, 1, 0]).astype(np.float32) self.assertAllClose(np_ans, tf_ans) def testSetValue(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): tf_ans = _SparseToDense([1, 3], [5], [1, 2], -1) np_ans = np.array([-1, 1, -1, 2, -1]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def testSetSingleValue(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): tf_ans = _SparseToDense([1, 3], [5], 1, -1) np_ans = np.array([-1, 1, -1, 1, -1]).astype(np.int32) self.assertAllClose(np_ans, tf_ans) def test2d(self): # pylint: disable=bad-whitespace - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): tf_ans = _SparseToDense([[1, 3], [2, 0]], [3, 4], 1, -1) np_ans = np.array([[-1, -1, -1, -1], [-1, -1, -1, 1], @@ -78,12 +78,12 @@ class SparseToDenseTest(xla_test.XLATestCase): self.assertAllClose(np_ans, tf_ans) def testZeroDefault(self): - with self.test_session(): + with self.cached_session(): x = sparse_ops.sparse_to_dense(2, [4], 7).eval() self.assertAllEqual(x, [0, 0, 7, 0]) def test3d(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): tf_ans = _SparseToDense([[1, 3, 0], [2, 0, 1]], [3, 4, 2], 1, -1) np_ans = np.ones((3, 4, 2), dtype=np.int32) * -1 np_ans[1, 3, 0] = 1 @@ -91,25 +91,25 @@ class SparseToDenseTest(xla_test.XLATestCase): self.assertAllClose(np_ans, tf_ans) def testBadShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): with self.assertRaisesWithPredicateMatch(ValueError, "must be rank 1"): _SparseToDense([1, 3], [[5], [3]], 1, -1) def testBadValue(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): with self.assertRaisesOpError( r"sparse_values has incorrect shape \[2,1\], " r"should be \[\] or \[2\]"): _SparseToDense([1, 3], [5], [[5], [3]], -1) def testBadNumValues(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): with self.assertRaisesOpError( r"sparse_values has incorrect shape \[3\], should be \[\] or \[2\]"): _SparseToDense([1, 3], [5], [1, 2, 3], -1) def testBadDefault(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): with self.assertRaisesOpError("default_value should be a scalar"): _SparseToDense([1, 3], [5], [1, 2], [0]) diff --git a/tensorflow/compiler/tests/stack_ops_test.py b/tensorflow/compiler/tests/stack_ops_test.py index b7dd787fef..720595a159 100644 --- a/tensorflow/compiler/tests/stack_ops_test.py +++ b/tensorflow/compiler/tests/stack_ops_test.py @@ -31,7 +31,7 @@ from tensorflow.python.platform import test class StackOpTest(xla_test.XLATestCase): def testStackPushPop(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): size = array_ops.placeholder(dtypes.int32) v = array_ops.placeholder(dtypes.float32) h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo") @@ -41,7 +41,7 @@ class StackOpTest(xla_test.XLATestCase): self.assertAllClose([[4.0, 5.0]], c1.eval({size: 5, v: [[4.0, 5.0]]})) def testStackPushPopSwap(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): a = np.arange(2000) x = array_ops.placeholder(dtypes.float32) h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") @@ -51,7 +51,7 @@ class StackOpTest(xla_test.XLATestCase): self.assertAllClose(a, c1.eval({x: a})) def testMultiStack(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): v = array_ops.placeholder(dtypes.float32) h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") c1 = gen_data_flow_ops.stack_push_v2(h1, v) @@ -66,7 +66,7 @@ class StackOpTest(xla_test.XLATestCase): def testSameNameStacks(self): """Different stacks with the same name do not interfere.""" - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): v1 = array_ops.placeholder(dtypes.float32) v2 = array_ops.placeholder(dtypes.float32) h1 = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") @@ -84,14 +84,14 @@ class StackOpTest(xla_test.XLATestCase): self.assertAllClose(out2, 5.0) def testCloseStack(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): size = array_ops.placeholder(dtypes.int32) h = gen_data_flow_ops.stack_v2(size, dtypes.float32, stack_name="foo") c1 = gen_data_flow_ops.stack_close_v2(h) sess.run(c1, {size: 5}) def testPushCloseStack(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): v = array_ops.placeholder(dtypes.float32) h = gen_data_flow_ops.stack_v2(5, dtypes.float32, stack_name="foo") c = gen_data_flow_ops.stack_push_v2(h, v) diff --git a/tensorflow/compiler/tests/stateless_random_ops_test.py b/tensorflow/compiler/tests/stateless_random_ops_test.py index d162675ef8..1bea7d9355 100644 --- a/tensorflow/compiler/tests/stateless_random_ops_test.py +++ b/tensorflow/compiler/tests/stateless_random_ops_test.py @@ -38,7 +38,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase): def testDeterminism(self): # Stateless values should be equal iff the seeds are equal (roughly) - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) seeds = [(x, y) for x in range(5) for y in range(5)] * 3 for stateless_op in [ @@ -55,7 +55,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase): self.assertEqual(s0 == s1, np.all(v0 == v1)) def testRandomUniformIsInRange(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): for dtype in self._random_types(): seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) x = stateless.stateless_random_uniform( @@ -74,7 +74,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase): def testDistributionOfStatelessRandomUniform(self): """Use Pearson's Chi-squared test to test for uniformity.""" - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): for dtype in self._random_types(): seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) n = 1000 @@ -88,7 +88,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase): self.assertTrue(self._chi_squared(y, 10) < 16.92) def testRandomNormalIsFinite(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): for dtype in self._random_types(): seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) x = stateless.stateless_random_uniform( @@ -111,7 +111,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase): def testDistributionOfStatelessRandomNormal(self): """Use Anderson-Darling test to test distribution appears normal.""" - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): for dtype in self._random_types(): seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) n = 1000 @@ -126,7 +126,7 @@ class StatelessRandomOpsTest(xla_test.XLATestCase): def testTruncatedNormalIsInRange(self): # TODO(b/34339814): implement inverse erf support for non-F32 types. for dtype in [dtypes.float32]: - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): seed_t = array_ops.placeholder(dtypes.int32, shape=[2]) n = 10000000 x = stateless.stateless_truncated_normal( diff --git a/tensorflow/compiler/tests/tensor_array_ops_test.py b/tensorflow/compiler/tests/tensor_array_ops_test.py index f332aa2e9b..78244d0b36 100644 --- a/tensorflow/compiler/tests/tensor_array_ops_test.py +++ b/tensorflow/compiler/tests/tensor_array_ops_test.py @@ -44,7 +44,7 @@ def _make_converter(dtype): class TensorArrayTest(xla_test.XLATestCase): def testTensorArrayWriteRead(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -66,7 +66,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([], flow_val.shape) def _testTensorArrayWritePack(self, tf_dtype): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) @@ -86,7 +86,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayWritePack(dtype) def testEmptyTensorArrayPack(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) @@ -100,7 +100,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([3, 0, 1], c0.eval().shape) def _testTensorArrayWriteConcat(self, tf_dtype): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) @@ -121,7 +121,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayWriteConcat(dtype) def _testTensorArrayUnpackRead(self, tf_dtype): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) @@ -176,7 +176,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayUnpackReadMaybeLegacy() def _testTensorArraySplitRead(self, tf_dtype): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=tf_dtype, tensor_array_name="foo", size=3) @@ -228,7 +228,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArraySplitRead(dtype) def testTensorGradArrayWriteRead(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -261,7 +261,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([[-2.0]], g_d2) def testTensorGradArrayDynamicWriteRead(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -300,7 +300,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(3, g_vs) def testTensorGradAccessTwiceReceiveSameObject(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3, element_shape=[1, 2]) @@ -317,7 +317,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([[4.0, 5.0]], d_r1_0) def testTensorArrayWriteWrongIndexOrDataTypeFails(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) @@ -331,7 +331,7 @@ class TensorArrayTest(xla_test.XLATestCase): # the first type, but try to read the other type. if len(self.float_types) > 1: dtype1, dtype2 = list(self.float_types)[:2] - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtype1, tensor_array_name="foo", size=3) @@ -347,7 +347,7 @@ class TensorArrayTest(xla_test.XLATestCase): w0.read(1) def testTensorArraySplitIncompatibleShapesFails(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -379,7 +379,7 @@ class TensorArrayTest(xla_test.XLATestCase): ta.split([1.0], [1]).flow.eval() def _testTensorArrayWriteGradientAddMultipleAdds(self, dtype): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtype, tensor_array_name="foo", size=3, infer_shape=False) @@ -410,7 +410,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayWriteGradientAddMultipleAdds(dtype) def testMultiTensorArray(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): h1 = tensor_array_ops.TensorArray( size=1, dtype=dtypes.float32, tensor_array_name="foo") w1 = h1.write(0, 4.0) @@ -425,7 +425,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllClose(9.0, r.eval()) def _testTensorArrayGradientWriteReadType(self, dtype): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.as_dtype(dtype), tensor_array_name="foo", @@ -478,7 +478,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayGradientWriteReadType(dtype) def _testTensorArrayGradientWritePackConcatAndRead(self): - with self.test_session() as sess, self.test_scope(): + with self.cached_session() as sess, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -513,7 +513,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayGradientWritePackConcatAndRead() def testTensorArrayReadTwice(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) ta_readtwice = tensor_array_ops.TensorArray( @@ -529,7 +529,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([1.0, -1.0], r1_readtwice.eval()) def _testTensorArrayGradientUnpackRead(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -557,7 +557,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayGradientUnpackRead() def testTensorArrayGradientSplitConcat(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=2) @@ -581,21 +581,21 @@ class TensorArrayTest(xla_test.XLATestCase): grad_vals[0]) def testCloseTensorArray(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) c1 = ta.close() session.run(c1) def testSizeTensorArray(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) s = ta.size() self.assertAllEqual(3, s.eval()) def testWriteCloseTensorArray(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -608,7 +608,7 @@ class TensorArrayTest(xla_test.XLATestCase): # TODO(phawkins): implement while loops. # def _testWhileLoopWritePackGradients(self, dynamic_size, dtype): # np_dtype = dtype.as_numpy_dtype - # with self.test_session() as session, self.test_scope(): + # with self.cached_session() as session, self.test_scope(): # v0 = array_ops.identity(np.arange(3 * 5, dtype=np_dtype).reshape(3, 5)) # var = variables.Variable(np.arange(100, 105, dtype=np_dtype)) # state0 = array_ops.identity(np.array([1] * 5, dtype=np_dtype)) @@ -692,7 +692,7 @@ class TensorArrayTest(xla_test.XLATestCase): # dynamic_size=True, dtype=dtypes.float32) # def testGradSerialTwoLoops(self): - # with self.test_session(), self.test_scope(): + # with self.cached_session(), self.test_scope(): # num_steps = 100 # acc = tensor_array_ops.TensorArray( # dtype=dtypes.float32, @@ -725,7 +725,7 @@ class TensorArrayTest(xla_test.XLATestCase): # self.assertAllClose(31.0, grad.eval()) def testSumOfTwoReadVariablesWithoutRepeatGrad(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): a = array_ops.identity( np.arange( 3 * 5, dtype=np.float32).reshape(3, 5) + 1) @@ -757,7 +757,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(joint_grad_b_t, g0) def testWriteShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) c0 = constant_op.constant([4.0, 5.0]) @@ -781,7 +781,7 @@ class TensorArrayTest(xla_test.XLATestCase): w0.write(0, c2) def testPartlyUnknownShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=6) @@ -821,7 +821,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([5, 4, 2, 3], r5.get_shape().as_list()) def _testUnpackShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -846,7 +846,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testUnpackShape() def testSplitShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -867,7 +867,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(r0.get_shape(), tensor_shape.unknown_shape()) def testWriteUnknownShape(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -879,7 +879,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(r0.get_shape(), tensor_shape.unknown_shape()) def _testGradientWhenNotAllComponentsRead(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2) x = constant_op.constant([2.0, 3.0]) w = ta.unstack(x) @@ -893,7 +893,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testGradientWhenNotAllComponentsRead() def _testTensorArrayEvalEmpty(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=0, infer_shape=False) with self.assertRaisesOpError( @@ -906,7 +906,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayEvalEmpty() def _testTensorArrayEvalEmptyWithDefault(self): - with self.test_session(), self.test_scope(): + with self.cached_session(), self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, size=0, infer_shape=True) self.assertEqual(0, ta.size().eval()) @@ -921,7 +921,7 @@ class TensorArrayTest(xla_test.XLATestCase): self._testTensorArrayEvalEmptyWithDefault() def testTensorArrayScatterReadAndGradients(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -946,7 +946,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual([[2.0, 3.0], [4.0, 5.0]], grad_vals[0]) def testTensorArrayWriteGatherAndGradients(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", @@ -974,7 +974,7 @@ class TensorArrayTest(xla_test.XLATestCase): self.assertAllEqual(expected_grad, grad_vals[0]) def testTensorArrayIdentity(self): - with self.test_session() as session, self.test_scope(): + with self.cached_session() as session, self.test_scope(): ta0 = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2, infer_shape=False) ta1 = tensor_array_ops.TensorArray(dtype=dtypes.int32, size=4, diff --git a/tensorflow/compiler/tests/ternary_ops_test.py b/tensorflow/compiler/tests/ternary_ops_test.py index effa5a59fe..55a992195f 100644 --- a/tensorflow/compiler/tests/ternary_ops_test.py +++ b/tensorflow/compiler/tests/ternary_ops_test.py @@ -31,7 +31,7 @@ from tensorflow.python.platform import googletest class TernaryOpsTest(xla_test.XLATestCase): def _testTernary(self, op, a, b, c, expected): - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): pa = array_ops.placeholder(dtypes.as_dtype(a.dtype), a.shape, name="a") pb = array_ops.placeholder(dtypes.as_dtype(b.dtype), b.shape, name="b") diff --git a/tensorflow/compiler/tests/unary_ops_test.py b/tensorflow/compiler/tests/unary_ops_test.py index 124cf9da81..5b0e57f83f 100644 --- a/tensorflow/compiler/tests/unary_ops_test.py +++ b/tensorflow/compiler/tests/unary_ops_test.py @@ -65,7 +65,7 @@ class UnaryOpsTest(xla_test.XLATestCase): rtol: relative tolerance for equality test. atol: absolute tolerance for equality test. """ - with self.test_session() as session: + with self.cached_session() as session: with self.test_scope(): pinp = array_ops.placeholder( dtypes.as_dtype(inp.dtype), inp.shape, name="a") @@ -202,7 +202,7 @@ class UnaryOpsTest(xla_test.XLATestCase): # Disable float16 testing for now if dtype != np.float16: x = np.arange(-10, 10, 1).astype(dtype) - with self.test_session() as session: + with self.cached_session() as session: erf_x = session.run(math_ops.erf(x)) erfc_x = session.run(math_ops.erfc(x)) diff --git a/tensorflow/compiler/tests/while_test.py b/tensorflow/compiler/tests/while_test.py index b637cf31cf..4ee144beb7 100644 --- a/tensorflow/compiler/tests/while_test.py +++ b/tensorflow/compiler/tests/while_test.py @@ -43,7 +43,7 @@ class WhileTest(xla_test.XLATestCase): def loop_cond(step): return step < 10 - with self.test_session() as sess: + with self.cached_session() as sess: init_index = array_ops.placeholder(dtypes.int32, []) with self.test_scope(): loop_outputs = xla.while_loop([init_index], loop_cond, loop_body) @@ -65,7 +65,7 @@ class WhileTest(xla_test.XLATestCase): del rsum return step < 10 - with self.test_session() as sess: + with self.cached_session() as sess: init_index = array_ops.placeholder(dtypes.int32, []) init_sum = array_ops.placeholder(dtypes.float32, []) with self.test_scope(): @@ -91,7 +91,7 @@ class WhileTest(xla_test.XLATestCase): del rsum return step < 10 - with self.test_session() as sess: + with self.cached_session() as sess: init_index = array_ops.placeholder(dtypes.int32, []) init_sum = array_ops.placeholder(dtypes.complex64, []) with self.test_scope(): @@ -117,7 +117,7 @@ class WhileTest(xla_test.XLATestCase): del x return step < 10 - with self.test_session() as sess: + with self.cached_session() as sess: init_index = array_ops.placeholder(dtypes.int32, []) with self.test_scope(): loop_outputs = xla.while_loop([init_index, 42], loop_cond, loop_body) diff --git a/tensorflow/compiler/tests/xla_device_test.py b/tensorflow/compiler/tests/xla_device_test.py index 85084bb124..28d61fb07d 100644 --- a/tensorflow/compiler/tests/xla_device_test.py +++ b/tensorflow/compiler/tests/xla_device_test.py @@ -37,7 +37,7 @@ class XlaDeviceTest(xla_test.XLATestCase): [16384, 1], [1, 16384], [1, 20000, 1, 1]] for dtype in self.numeric_types: for shape in shapes: - with self.test_session() as sess: + with self.cached_session() as sess: with ops.device("CPU"): x = array_ops.placeholder(dtype, shape) with self.test_scope(): @@ -58,7 +58,7 @@ class XlaDeviceTest(xla_test.XLATestCase): ]) shape = (10, 10) for unsupported_dtype in test_types - self.all_types: - with self.test_session() as sess: + with self.cached_session() as sess: with ops.device("CPU"): x = array_ops.placeholder(unsupported_dtype, shape) with self.test_scope(): @@ -78,7 +78,7 @@ class XlaDeviceTest(xla_test.XLATestCase): pass def testControlTrigger(self): - with self.test_session() as sess: + with self.cached_session() as sess: with self.test_scope(): x = gen_control_flow_ops.control_trigger() sess.run(x) |