From 945d1a77aebb2071b571598cb1d02fac5b1370c1 Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Fri, 22 Jun 2018 01:46:03 -0700 Subject: [PATCH] Replace unnecessary `()` in `run_in_graph_and_eager_modes()`. PiperOrigin-RevId: 201652888 --- .../checkpoint/python/containers_test.py | 6 +- .../python/split_dependency_test.py | 2 +- .../python/kernel_tests/cudnn_rnn_test.py | 6 +- .../kernel_tests/scan_dataset_op_test.py | 2 +- .../python/cross_tower_utils_test.py | 18 +- .../python/mirrored_strategy_multigpu_test.py | 6 +- .../python/mirrored_strategy_test.py | 2 +- .../python/one_device_strategy_test.py | 2 +- .../python/shared_variable_creator_test.py | 2 +- .../contrib/distribute/python/values_test.py | 6 +- .../bijectors/fill_triangular_test.py | 6 +- .../bijectors/matrix_inverse_tril_test.py | 14 +- .../kernel_tests/bijectors/ordered_test.py | 4 +- .../kernel_tests/bijectors/scale_tril_test.py | 2 +- .../kernel_tests/bijectors/softsign_test.py | 10 +- .../bijectors/transform_diagonal_test.py | 2 +- .../kernel_tests/distribution_util_test.py | 4 +- .../contrib/eager/python/metrics_test.py | 4 +- .../contrib/eager/python/network_test.py | 42 ++-- .../python/ops/critical_section_test.py | 8 +- tensorflow/contrib/lookup/lookup_ops_test.py | 4 +- .../python/loss_scale_manager_test.py | 22 +- .../python/loss_scale_optimizer_test.py | 12 +- .../optimizer_v2/checkpointable_utils_test.py | 12 +- .../contrib/optimizer_v2/optimizer_v2_test.py | 14 +- .../python/kernel_tests/core_rnn_cell_test.py | 2 +- .../rnn/python/kernel_tests/core_rnn_test.py | 6 +- .../python/data/util/random_seed_test.py | 2 +- tensorflow/python/eager/backprop_test.py | 28 +-- tensorflow/python/estimator/estimator_test.py | 2 +- .../feature_column/feature_column_test.py | 2 +- tensorflow/python/framework/ops_test.py | 6 +- .../python/framework/random_seed_test.py | 2 +- .../python/framework/tensor_util_test.py | 6 +- tensorflow/python/keras/engine/saving_test.py | 12 +- .../python/keras/engine/sequential_test.py | 12 +- .../python/keras/engine/topology_test.py | 4 +- .../keras/engine/training_eager_test.py | 8 +- .../python/keras/engine/training_test.py | 8 +- .../python/keras/layers/convolutional_test.py | 32 +-- tensorflow/python/keras/layers/core_test.py | 14 +- .../keras/layers/cudnn_recurrent_test.py | 6 +- tensorflow/python/keras/layers/gru_test.py | 8 +- tensorflow/python/keras/layers/local_test.py | 6 +- tensorflow/python/keras/layers/lstm_test.py | 8 +- tensorflow/python/keras/layers/merge_test.py | 16 +- tensorflow/python/keras/layers/noise_test.py | 2 +- .../python/keras/layers/pooling_test.py | 18 +- .../python/keras/layers/simplernn_test.py | 8 +- .../python/keras/layers/wrappers_test.py | 2 +- .../python/keras/model_subclassing_test.py | 34 +-- tensorflow/python/keras/models_test.py | 2 +- .../python/kernel_tests/array_ops_test.py | 8 +- .../kernel_tests/atrous_convolution_test.py | 10 +- .../python/kernel_tests/check_ops_test.py | 212 +++++++++--------- .../kernel_tests/confusion_matrix_test.py | 2 +- .../python/kernel_tests/conv_ops_test.py | 72 +++--- .../distributions/bernoulli_test.py | 32 +-- .../kernel_tests/distributions/normal_test.py | 40 ++-- .../distributions/special_math_test.py | 6 +- .../distributions/uniform_test.py | 32 +-- .../kernel_tests/distributions/util_test.py | 24 +- .../python/kernel_tests/fifo_queue_test.py | 4 +- .../kernel_tests/functional_ops_test.py | 48 ++-- .../python/kernel_tests/list_ops_test.py | 44 ++-- .../python/kernel_tests/logging_ops_test.py | 2 +- .../python/kernel_tests/py_func_test.py | 18 +- .../random/multinomial_op_test.py | 2 +- .../resource_variable_ops_test.py | 58 ++--- tensorflow/python/kernel_tests/rnn_test.py | 12 +- .../kernel_tests/scatter_nd_ops_test.py | 2 +- .../python/kernel_tests/split_op_test.py | 18 +- .../python/kernel_tests/template_test.py | 38 ++-- .../kernel_tests/tensor_array_ops_test.py | 46 ++-- .../kernel_tests/variable_scope_test.py | 28 +-- tensorflow/python/layers/base_test.py | 30 +-- tensorflow/python/layers/core_test.py | 22 +- .../python/ops/control_flow_ops_test.py | 4 +- tensorflow/python/ops/math_ops_test.py | 20 +- tensorflow/python/ops/nn_test.py | 12 +- .../checkpointable/data_structures_test.py | 6 +- .../training/checkpointable/util_test.py | 44 ++-- .../training/learning_rate_decay_test.py | 58 ++--- tensorflow/python/training/optimizer_test.py | 14 +- tensorflow/python/training/saver_test.py | 14 +- tensorflow/python/util/serialization_test.py | 4 +- 86 files changed, 727 insertions(+), 727 deletions(-) diff --git a/tensorflow/contrib/checkpoint/python/containers_test.py b/tensorflow/contrib/checkpoint/python/containers_test.py index 3717d7f583f..12b99d3e22d 100644 --- a/tensorflow/contrib/checkpoint/python/containers_test.py +++ b/tensorflow/contrib/checkpoint/python/containers_test.py @@ -32,7 +32,7 @@ from tensorflow.python.training.checkpointable import util as checkpointable_uti class UniqueNameTrackerTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNames(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -65,7 +65,7 @@ class UniqueNameTrackerTests(test.TestCase): self.assertEqual(4., self.evaluate(restore_slots.x_1_1)) self.assertEqual(5., self.evaluate(restore_slots.y)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testExample(self): class SlotManager(checkpointable.Checkpointable): @@ -97,7 +97,7 @@ class UniqueNameTrackerTests(test.TestCase): dependency_names, ["x", "x_1", "y", "slot_manager", "slotdeps", "save_counter"]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayers(self): tracker = containers.UniqueNameTracker() tracker.track(layers.Dense(3), "dense") diff --git a/tensorflow/contrib/checkpoint/python/split_dependency_test.py b/tensorflow/contrib/checkpoint/python/split_dependency_test.py index 69dc0b9be2d..43c5d6515b1 100644 --- a/tensorflow/contrib/checkpoint/python/split_dependency_test.py +++ b/tensorflow/contrib/checkpoint/python/split_dependency_test.py @@ -73,7 +73,7 @@ class OnlyOneDep(checkpointable.Checkpointable): class SplitTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestoreSplitDep(self): save_checkpoint = checkpointable_utils.Checkpoint( dep=SaveTensorSlicesAsDeps()) diff --git a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py index 8285ea04926..252ea1560d7 100644 --- a/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py +++ b/tensorflow/contrib/cudnn_rnn/python/kernel_tests/cudnn_rnn_test.py @@ -768,7 +768,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): @unittest.skipUnless(test.is_built_with_cuda(), "Test only applicable when running on GPUs") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLSTMCheckpointableSingleLayer(self): num_units = 2 direction = CUDNN_RNN_UNIDIRECTION @@ -781,7 +781,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): @unittest.skipUnless(test.is_built_with_cuda(), "Test only applicable when running on GPUs") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGRUCheckpointableSingleLayer(self): num_units = 2 direction = CUDNN_RNN_UNIDIRECTION @@ -826,7 +826,7 @@ class CudnnRNNTestSaveRestoreCheckpointable(test_util.TensorFlowTestCase): @unittest.skipUnless(test.is_built_with_cuda(), "Test only applicable when running on GPUs") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCudnnCompatibleLSTMCheckpointablMultiLayer(self): num_units = 2 num_layers = 3 diff --git a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py index d02b3abb92f..42cada0b97b 100644 --- a/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/scan_dataset_op_test.py @@ -63,7 +63,7 @@ class ScanDatasetTest(test.TestCase): with self.assertRaises(errors.OutOfRangeError): sess.run(next_element) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFibonacci(self): iterator = dataset_ops.Dataset.from_tensors(1).repeat(None).apply( scan_ops.scan([0, 1], lambda a, _: ([a[1], a[0] + a[1]], a[1])) diff --git a/tensorflow/contrib/distribute/python/cross_tower_utils_test.py b/tensorflow/contrib/distribute/python/cross_tower_utils_test.py index 4ef8db68150..d25964fa41a 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_utils_test.py +++ b/tensorflow/contrib/distribute/python/cross_tower_utils_test.py @@ -38,7 +38,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): self.evaluate(ops.convert_to_tensor(left)), self.evaluate(ops.convert_to_tensor(right))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAggregateTensors(self): t0 = constant_op.constant([[1., 2.], [0, 0], [3., 4.]]) t1 = constant_op.constant([[0., 0.], [5, 6], [7., 8.]]) @@ -46,7 +46,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): result = cross_tower_utils.aggregate_tensors_or_indexed_slices([t0, t1]) self._assert_values_equal(total, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAggregateIndexedSlices(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -57,7 +57,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): self.assertIsInstance(result, ops.IndexedSlices) self._assert_values_equal(total, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDivideTensor(self): t = constant_op.constant([[1., 2.], [0, 0], [3., 4.]]) n = 2 @@ -65,7 +65,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): result = cross_tower_utils.divide_by_n_tensors_or_indexed_slices(t, n) self._assert_values_equal(expected, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDivideIndexedSlices(self): t = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -75,13 +75,13 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): self.assertIsInstance(result, ops.IndexedSlices) self._assert_values_equal(expected, result) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testIsIndexedSlices(self): t = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) self.assertTrue(cross_tower_utils.contains_indexed_slices(t)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_List(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -89,7 +89,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): constant_op.constant([[0., 0.], [5, 6], [7., 8.]])) self.assertTrue(cross_tower_utils.contains_indexed_slices([t0, t1])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_Tuple(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -97,7 +97,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): constant_op.constant([[0., 0.], [5, 6], [7., 8.]])) self.assertTrue(cross_tower_utils.contains_indexed_slices((t0, t1))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_PerDevice(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) @@ -106,7 +106,7 @@ class IndexedSlicesUtilsTest(test.TestCase, parameterized.TestCase): per_device = value_lib.PerDevice({"/gpu:0": t0, "/cpu:0": t1}) self.assertTrue(cross_tower_utils.contains_indexed_slices(per_device)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContainsIndexedSlices_PerDeviceMapOutput(self): t0 = math_ops._as_indexed_slices( constant_op.constant([[1., 2.], [0, 0], [3., 4.]])) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index cb150692de8..647cf953d73 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -83,13 +83,13 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase): self.skipTest("Not GPU test") self.assertEqual(2, self._get_distribution_strategy().num_towers) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallAndMergeExceptions(self): if not GPU_TEST: self.skipTest("Not GPU test") self._test_call_and_merge_exceptions(self._get_distribution_strategy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRunRegroupError(self): def run_fn(device_id): @@ -101,7 +101,7 @@ class MirroredTwoDeviceDistributionTest(strategy_test_lib.DistributionTestBase): with dist.scope(), self.assertRaises(AssertionError): dist.call_for_each_tower(run_fn, dist.worker_device_index) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceToCpu(self): if not GPU_TEST: self.skipTest("Not GPU test") diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_test.py index 61cbe6df813..a066adf1246 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_test.py @@ -47,7 +47,7 @@ class MirroredOneCPUDistributionTest(strategy_test_lib.DistributionTestBase): def testTowerId(self): self._test_tower_id(self._get_distribution_strategy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallAndMergeExceptions(self): self._test_call_and_merge_exceptions(self._get_distribution_strategy()) diff --git a/tensorflow/contrib/distribute/python/one_device_strategy_test.py b/tensorflow/contrib/distribute/python/one_device_strategy_test.py index 7aad8a953cb..4fdc0f72e67 100644 --- a/tensorflow/contrib/distribute/python/one_device_strategy_test.py +++ b/tensorflow/contrib/distribute/python/one_device_strategy_test.py @@ -44,7 +44,7 @@ class OneDeviceStrategyTest(strategy_test_lib.DistributionTestBase): def testTowerId(self): self._test_tower_id(self._get_distribution_strategy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallAndMergeExceptions(self): self._test_call_and_merge_exceptions(self._get_distribution_strategy()) diff --git a/tensorflow/contrib/distribute/python/shared_variable_creator_test.py b/tensorflow/contrib/distribute/python/shared_variable_creator_test.py index a0b452fc2d4..2a9ab51fcfd 100644 --- a/tensorflow/contrib/distribute/python/shared_variable_creator_test.py +++ b/tensorflow/contrib/distribute/python/shared_variable_creator_test.py @@ -46,7 +46,7 @@ class CanonicalizeVariableNameTest(test.TestCase): class SharedVariableCreatorTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSharedVariable(self): shared_variable_store = {} diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index b0bd92c7b05..c5b246e8041 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -82,7 +82,7 @@ class DistributedValuesTest(test.TestCase): class DistributedDelegateTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetAttr(self): with ops.device("/device:CPU:0"): @@ -97,7 +97,7 @@ class DistributedDelegateTest(test.TestCase): with self.assertRaises(AttributeError): _ = v.y - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOperatorOverride(self): with ops.device("/device:CPU:0"): v = values.DistributedDelegate({"/device:CPU:0": 7, "/device:GPU:0": 8}) @@ -363,7 +363,7 @@ class PerDeviceDatasetTest(test.TestCase): self._test_iterator_no_prefetch(devices, dataset, expected_values) self._test_iterator_with_prefetch(devices, dataset, expected_values) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOneDevice(self): devices = ["/device:CPU:0"] dataset = dataset_ops.Dataset.range(10) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py index caeaf2a0c6e..3530e142e4d 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/fill_triangular_test.py @@ -31,7 +31,7 @@ from tensorflow.python.platform import test class FillTriangularBijectorTest(test.TestCase): """Tests the correctness of the FillTriangular bijector.""" - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijector(self): x = np.float32(np.array([1., 2., 3.])) y = np.float32(np.array([[3., 0.], @@ -51,7 +51,7 @@ class FillTriangularBijectorTest(test.TestCase): ildj = self.evaluate(b.inverse_log_det_jacobian(y, event_ndims=2)) self.assertAllClose(ildj, 0.) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShape(self): x_shape = tensor_shape.TensorShape([5, 4, 6]) y_shape = tensor_shape.TensorShape([5, 4, 3, 3]) @@ -76,7 +76,7 @@ class FillTriangularBijectorTest(test.TestCase): b.inverse_event_shape_tensor(y_shape.as_list())) self.assertAllEqual(x_shape_tensor, x_shape.as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShapeError(self): b = bijectors.FillTriangular(validate_args=True) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py index 18397035571..85d604e34ac 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/matrix_inverse_tril_test.py @@ -29,7 +29,7 @@ from tensorflow.python.platform import test class MatrixInverseTriLBijectorTest(test.TestCase): """Tests the correctness of the Y = inv(tril) transformation.""" - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputesCorrectValues(self): inv = bijectors.MatrixInverseTriL(validate_args=True) self.assertEqual("matrix_inverse_tril", inv.name) @@ -51,7 +51,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertNear(expected_fldj_, fldj_, err=1e-3) self.assertNear(-expected_fldj_, ildj_, err=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOneByOneMatrix(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([[5.]], dtype=np.float32) @@ -70,7 +70,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertNear(expected_fldj_, fldj_, err=1e-3) self.assertNear(-expected_fldj_, ildj_, err=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testZeroByZeroMatrix(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.eye(0, dtype=np.float32) @@ -89,7 +89,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertNear(expected_fldj_, fldj_, err=1e-3) self.assertNear(-expected_fldj_, ildj_, err=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBatch(self): # Test batch computation with input shape (2, 1, 2, 2), i.e. batch shape # (2, 1). @@ -114,7 +114,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): self.assertAllClose(expected_fldj_, fldj_, atol=0., rtol=1e-3) self.assertAllClose(-expected_fldj_, ildj_, atol=0., rtol=1e-3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testErrorOnInputRankTooLow(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([0.1], dtype=np.float32) @@ -149,7 +149,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): ## square_error_msg): ## inv.inverse_log_det_jacobian(x_, event_ndims=2).eval() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testErrorOnInputNotLowerTriangular(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([[1., 2.], @@ -169,7 +169,7 @@ class MatrixInverseTriLBijectorTest(test.TestCase): triangular_error_msg): inv.inverse_log_det_jacobian(x_, event_ndims=2).eval() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testErrorOnInputSingular(self): inv = bijectors.MatrixInverseTriL(validate_args=True) x_ = np.array([[1., 0.], diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py index a5f5219588f..cb42331a21a 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/ordered_test.py @@ -36,7 +36,7 @@ class OrderedBijectorTest(test.TestCase): def setUp(self): self._rng = np.random.RandomState(42) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorVector(self): with self.test_session(): ordered = Ordered() @@ -82,7 +82,7 @@ class OrderedBijectorTest(test.TestCase): atol=0., rtol=1e-7) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShapeGetters(self): with self.test_session(): x = tensor_shape.TensorShape([4]) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py index 566a7b3dff9..d5b3367f9a3 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/scale_tril_test.py @@ -46,7 +46,7 @@ class ScaleTriLBijectorTest(test.TestCase): x_ = self.evaluate(b.inverse(y)) self.assertAllClose(x, x_) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvertible(self): # Generate random inputs from an unconstrained space, with diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py index 2ac06fce55b..d0098c3c105 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/softsign_test.py @@ -40,7 +40,7 @@ class SoftsignBijectorTest(test.TestCase): def setUp(self): self._rng = np.random.RandomState(42) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorBounds(self): bijector = Softsign(validate_args=True) with self.test_session(): @@ -54,7 +54,7 @@ class SoftsignBijectorTest(test.TestCase): with self.assertRaisesOpError("less than 1"): bijector.inverse_log_det_jacobian(3., event_ndims=0).eval() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorForwardInverse(self): bijector = Softsign(validate_args=True) self.assertEqual("softsign", bijector.name) @@ -64,7 +64,7 @@ class SoftsignBijectorTest(test.TestCase): self.assertAllClose(y, self.evaluate(bijector.forward(x))) self.assertAllClose(x, self.evaluate(bijector.inverse(y))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorLogDetJacobianEventDimsZero(self): bijector = Softsign(validate_args=True) y = self._rng.rand(2, 10) @@ -74,7 +74,7 @@ class SoftsignBijectorTest(test.TestCase): self.assertAllClose(ildj, self.evaluate( bijector.inverse_log_det_jacobian(y, event_ndims=0))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorForwardInverseEventDimsOne(self): bijector = Softsign(validate_args=True) self.assertEqual("softsign", bijector.name) @@ -83,7 +83,7 @@ class SoftsignBijectorTest(test.TestCase): self.assertAllClose(y, self.evaluate(bijector.forward(x))) self.assertAllClose(x, self.evaluate(bijector.inverse(y))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijectorLogDetJacobianEventDimsOne(self): bijector = Softsign(validate_args=True) y = self._rng.rand(2, 10) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py index 6428a687022..efc9f266d1f 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/bijectors/transform_diagonal_test.py @@ -31,7 +31,7 @@ class TransformDiagonalBijectorTest(test.TestCase): def setUp(self): self._rng = np.random.RandomState(42) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBijector(self): x = np.float32(np.random.randn(3, 4, 4)) diff --git a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py index bbbec2103ae..181c46d2e52 100644 --- a/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py +++ b/tensorflow/contrib/distributions/python/kernel_tests/distribution_util_test.py @@ -544,7 +544,7 @@ class PadDynamicTest(_PadTest, test.TestCase): class TestMoveDimension(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_move_dimension_static_shape(self): x = random_ops.random_normal(shape=[200, 30, 4, 1, 6]) @@ -561,7 +561,7 @@ class TestMoveDimension(test.TestCase): x_perm = distribution_util.move_dimension(x, 4, 2) self.assertAllEqual(x_perm.shape.as_list(), [200, 30, 6, 4, 1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_move_dimension_dynamic_shape(self): x_ = random_ops.random_normal(shape=[200, 30, 4, 1, 6]) diff --git a/tensorflow/contrib/eager/python/metrics_test.py b/tensorflow/contrib/eager/python/metrics_test.py index 644d78f61fe..20d938d492b 100644 --- a/tensorflow/contrib/eager/python/metrics_test.py +++ b/tensorflow/contrib/eager/python/metrics_test.py @@ -206,7 +206,7 @@ class MetricsTest(test.TestCase): sess.run(accumulate, feed_dict={p: 7}) self.assertAllEqual(m.result().eval(), 7) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphAndEagerTensor(self): m = metrics.Mean() inputs = ops.convert_to_tensor([1.0, 2.0]) @@ -254,7 +254,7 @@ class MetricsTest(test.TestCase): self.assertAllEqual(m2.result().eval(), 2.0) self.assertAllEqual(m1.result().eval(), 1.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") diff --git a/tensorflow/contrib/eager/python/network_test.py b/tensorflow/contrib/eager/python/network_test.py index c92bd15b253..240f213c602 100644 --- a/tensorflow/contrib/eager/python/network_test.py +++ b/tensorflow/contrib/eager/python/network_test.py @@ -126,7 +126,7 @@ class NetworkTest(test.TestCase): self.assertAllEqual([[17.0], [34.0]], self.evaluate(result)) # TODO(allenl): This test creates garbage in some Python versions - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNetworkSaveRestoreAlreadyBuilt(self): net = MyNetwork(name="abcd") with self.assertRaisesRegexp( @@ -138,7 +138,7 @@ class NetworkTest(test.TestCase): self._save_modify_load_network_built(net, global_step=10) # TODO(allenl): This test creates garbage in some Python versions - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestoreDefaultGlobalStep(self): net = MyNetwork(name="abcd") net(constant_op.constant([[2.0]])) @@ -149,7 +149,7 @@ class NetworkTest(test.TestCase): self.assertIn("abcd-4242", save_path) # TODO(allenl): This test creates garbage in some Python versions - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNetworkSaveAndRestoreIntoUnbuilt(self): save_dir = self.get_temp_dir() net1 = MyNetwork() @@ -166,7 +166,7 @@ class NetworkTest(test.TestCase): self.assertAllEqual(self.evaluate(net1.variables[0]), self.evaluate(net2.variables[0])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNetworkMatchesLayerVariableNames(self): zero = constant_op.constant([[0.]]) layer_one = core.Dense(1, use_bias=False) @@ -193,7 +193,7 @@ class NetworkTest(test.TestCase): self.assertEqual("two_layer_net/" + layer_two.variables[0].name, net.second.variables[0].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoadIntoUnbuiltSharedLayer(self): class Owner(network.Network): @@ -272,7 +272,7 @@ class NetworkTest(test.TestCase): network.restore_network_checkpoint( load_into, save_path, map_func=_restore_map_func) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRestoreIntoSubNetwork(self): class Parent(network.Network): @@ -327,7 +327,7 @@ class NetworkTest(test.TestCase): # The checkpoint is incompatible. network.restore_network_checkpoint(save_into_parent, checkpoint) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCustomMapCollisionErrors(self): class Parent(network.Network): @@ -372,7 +372,7 @@ class NetworkTest(test.TestCase): network.restore_network_checkpoint( loader, checkpoint, map_func=lambda n: "foo") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDefaultMapCollisionErrors(self): one = constant_op.constant([[1.]]) @@ -571,7 +571,7 @@ class NetworkTest(test.TestCase): expected_start="my_network_1/dense/", actual=outside_net_after.trainable_weights[0].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVariableScopeStripping(self): with variable_scope.variable_scope("scope1"): with variable_scope.variable_scope("scope2"): @@ -596,7 +596,7 @@ class NetworkTest(test.TestCase): self.assertAllEqual([[42.]], self.evaluate(restore_net.variables[0])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerNamesRespected(self): class ParentNetwork(network.Network): @@ -677,7 +677,7 @@ class NetworkTest(test.TestCase): self.assertStartsWith(expected_start="my_network_1/dense/", actual=net2.trainable_weights[0].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableAnonymous(self): # The case where no explicit names are specified. We make up unique names, @@ -721,7 +721,7 @@ class NetworkTest(test.TestCase): self.assertEqual("my_network", net2.first.name) self.assertEqual("my_network_1", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicit(self): # We have explicit network names and everything is globally unique. @@ -750,7 +750,7 @@ class NetworkTest(test.TestCase): self.assertEqual("first_unique_child_name", net.first.name) self.assertEqual("second_unique_child_name", net.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerNetworkNameInteractions(self): # Same base name as core.Dense; Networks and non-Network Layers with the @@ -801,7 +801,7 @@ class NetworkTest(test.TestCase): actual=net.trainable_weights[4].name) self.assertEqual("mixed_layer_network", net.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicitCollisions(self): # We have explicit network names and they are unique within the layer @@ -831,7 +831,7 @@ class NetworkTest(test.TestCase): self.assertEqual("nonunique_name", net.first.name) self.assertEqual("second_unique_child_name", net.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicitWithAnonymousParent(self): # A parent network is instantiated multiple times with explicitly named @@ -873,7 +873,7 @@ class NetworkTest(test.TestCase): self.assertEqual("first_unique_child_name", net2.first.name) self.assertEqual("second_unique_child_name", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestableExplicitSameLayerCollisions(self): # We have explicit network names and they are _not_ unique within the layer @@ -891,7 +891,7 @@ class NetworkTest(test.TestCase): with self.assertRaisesRegexp(ValueError, "nonunique_name"): ParentNetwork() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAnonymousVariableSharing(self): # Two "owned" Networks @@ -989,7 +989,7 @@ class NetworkTest(test.TestCase): self.assertEqual("my_network", net4.first.name) self.assertEqual("my_network", net4.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRecursiveLayerRenaming(self): core.Dense(1) # Under default Layer naming, would change subsequent names. @@ -1041,7 +1041,7 @@ class NetworkTest(test.TestCase): self.assertEqual("dense", net.second.first.name) self.assertEqual("dense_1", net.second.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallInDifferentOrderThanConstruct(self): shared_network = MyNetwork() @@ -1091,7 +1091,7 @@ class NetworkTest(test.TestCase): self.assertTrue(net2.first is net1.first) self.assertEqual("my_network", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerCallInDifferentOrderThanConstruct(self): # Same idea as testCallInDifferentOrderThanConstruct, but this time with a # non-Network Layer shared between two Networks rather than a @@ -1144,7 +1144,7 @@ class NetworkTest(test.TestCase): self.assertTrue(net2.first is net1.first) self.assertEqual("dense", net2.second.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerAlreadyBuilt(self): one = constant_op.constant([[1.]]) core.Dense(1, use_bias=False) # pre-built layers use global naming diff --git a/tensorflow/contrib/framework/python/ops/critical_section_test.py b/tensorflow/contrib/framework/python/ops/critical_section_test.py index df7d7e9dae8..34fd5018af1 100644 --- a/tensorflow/contrib/framework/python/ops/critical_section_test.py +++ b/tensorflow/contrib/framework/python/ops/critical_section_test.py @@ -34,7 +34,7 @@ from tensorflow.python.platform import tf_logging as logging class CriticalSectionTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCreateCriticalSection(self): cs = critical_section_ops.CriticalSection(shared_name="cs") v = resource_variable_ops.ResourceVariable(0.0, name="v") @@ -53,7 +53,7 @@ class CriticalSectionTest(test.TestCase): self.assertAllClose([2.0 * i for i in range(num_concurrent)], sorted(r_value)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCriticalSectionWithControlFlow(self): for outer_cond in [False, True]: for inner_cond in [False, True]: @@ -109,7 +109,7 @@ class CriticalSectionTest(test.TestCase): with self.assertRaisesOpError("Error"): self.evaluate(r) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCreateCriticalSectionFnReturnsOp(self): cs = critical_section_ops.CriticalSection(shared_name="cs") v = resource_variable_ops.ResourceVariable(0.0, name="v") @@ -332,7 +332,7 @@ class CriticalSectionTest(test.TestCase): self.evaluate(v.initializer) self.assertEqual(10, self.evaluate(out)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInsideFunction(self): cs = critical_section_ops.CriticalSection() v = resource_variable_ops.ResourceVariable(1) diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py index 5a080cceabb..889accdd5aa 100644 --- a/tensorflow/contrib/lookup/lookup_ops_test.py +++ b/tensorflow/contrib/lookup/lookup_ops_test.py @@ -1397,7 +1397,7 @@ class KeyValueTensorInitializerTest(test.TestCase): class IndexTableFromTensor(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_index_table_from_tensor_with_tensor_init(self): table = lookup.index_table_from_tensor( mapping=("brain", "salad", "surgery"), num_oov_buckets=1) @@ -1670,7 +1670,7 @@ class InitializeTableFromFileOpTest(test.TestCase): f.write("\n".join(values) + "\n") return vocabulary_file - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitializeStringTable(self): vocabulary_file = self._createVocabFile("one_column_1.txt") default_value = -1 diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py b/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py index 480f5f6eaf4..1b0383d24c0 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_manager_test.py @@ -34,7 +34,7 @@ def _GetExampleIter(inputs): class FixedLossScaleManagerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_basic(self): itr = _GetExampleIter([True] * 10 + [False] * 10) @@ -84,13 +84,13 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): actual_outputs.append(self.evaluate(lsm.get_loss_scale())) self.assertEqual(actual_outputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increase_every_n_steps(self): inputs = [True] * 6 expected_outputs = [1, 2, 2, 4, 4, 8] self._test_helper(inputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_keep_increasing_until_capped(self): init_loss_scale = np.finfo(np.float32).max / 4 + 10 max_float = np.finfo(np.float32).max @@ -104,7 +104,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_decrease_every_n_steps(self): inputs = [False] * 6 init_loss_scale = 1024 @@ -112,7 +112,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_keep_decreasing_until_one(self): inputs = [False] * 10 init_loss_scale = 16 @@ -120,19 +120,19 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_incr_bad_step_clear_good_step(self): inputs = [True, True, True, False, True] expected_outputs = [1, 2, 2, 2, 2] self._test_helper(inputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_incr_good_step_does_not_clear_bad_step(self): inputs = [True, True, True, False, True, False] expected_outputs = [1, 2, 2, 2, 2, 1] self._test_helper(inputs, expected_outputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trigger_loss_scale_update_each_step(self): """Test when incr_every_n_step and decr_every_n_nan_or_inf is 1.""" init_loss_scale = 1 @@ -145,7 +145,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale, incr_every_n_step, decr_every_n_nan_or_inf) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_alternating_good_and_bad_gradients_trigger_each_step(self): init_loss_scale = 1 incr_every_n_step = 1 @@ -156,7 +156,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale, incr_every_n_step, decr_every_n_nan_or_inf) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_alternating_good_and_bad_gradients_trigger_incr_every_2steps(self): init_loss_scale = 32 incr_every_n_step = 2 @@ -167,7 +167,7 @@ class ExponentialUpdateLossScaleManagerTest(test.TestCase): self._test_helper(inputs, expected_outputs, init_loss_scale, incr_every_n_step, decr_every_n_nan_or_inf) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_random_mix_good_and_bad_gradients(self): init_loss_scale = 4 inputs = [ diff --git a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py index dded61ccd58..9009df0eefe 100644 --- a/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py +++ b/tensorflow/contrib/mixed_precision/python/loss_scale_optimizer_test.py @@ -54,7 +54,7 @@ class LossScaleOptimizerTest(test.TestCase): opt = loss_scale_opt_fn(opt) return x, loss, opt - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_float16_underflow_without_loss_scale(self): lr = 1 init_val = 1. @@ -73,7 +73,7 @@ class LossScaleOptimizerTest(test.TestCase): rtol=0, atol=min(symbolic_update, 1e-6)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_float16_with_loss_scale(self): lr = 1. init_val = 1. @@ -95,7 +95,7 @@ class LossScaleOptimizerTest(test.TestCase): rtol=0, atol=min(expected_update, 1e-6)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_compute_gradients_with_loss_scale(self): lr = 1 init_val = 1. @@ -115,7 +115,7 @@ class LossScaleOptimizerTest(test.TestCase): # Gradients aren't applied. self.assertAllClose(init_val, self.evaluate(x), rtol=0, atol=1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_compute_gradients_without_loss_scale(self): lr = 1 init_val = 1. @@ -127,7 +127,7 @@ class LossScaleOptimizerTest(test.TestCase): g_v = self.evaluate(grads_and_vars[0][0]) self.assertAllClose(g_v, 0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_apply_gradients(self): x = variable_scope.get_variable("x", initializer=1., dtype=dtypes.float32) @@ -155,7 +155,7 @@ class LossScaleOptimizerTest(test.TestCase): actual_output.append(self.evaluate(x)) self.assertAllClose(expected_output, actual_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_apply_gradients_loss_scale_is_updated(self): class SimpleLossScaleManager(lsm_lib.LossScaleManager): diff --git a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py index 64b95786b5c..b6972a7a457 100644 --- a/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py +++ b/tensorflow/contrib/optimizer_v2/checkpointable_utils_test.py @@ -226,7 +226,7 @@ class CheckpointingTests(test.TestCase): optimizer_node.slot_variables[0] .slot_variable_node_id].attributes[0].checkpoint_key) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): model = MyModel() optimizer = adam.AdamOptimizer(0.001) @@ -347,7 +347,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(training_continuation + 1, session.run(root.save_counter)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAgnosticUsage(self): """Graph/eager agnostic usage.""" # Does create garbage when executing eagerly due to ops.Graph() creation. @@ -381,7 +381,7 @@ class CheckpointingTests(test.TestCase): self.evaluate(root.save_counter)) # pylint: disable=cell-var-from-loop - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWithDefun(self): num_training_steps = 2 checkpoint_directory = self.get_temp_dir() @@ -453,7 +453,7 @@ class CheckpointingTests(test.TestCase): optimizer.apply_gradients( [(g, v) for g, v in zip(grad, model.vars)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDeferredSlotRestoration(self): checkpoint_directory = self.get_temp_dir() @@ -616,7 +616,7 @@ class CheckpointingTests(test.TestCase): class TemplateTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore(self): def _templated(): @@ -712,7 +712,7 @@ class CheckpointCompatibilityTests(test.TestCase): sess=session, save_path=checkpoint_prefix, global_step=root.optimizer_step) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoadFromNameBasedSaver(self): """Save a name-based checkpoint, load it using the object-based API.""" with test_util.device(use_gpu=True): diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py index 8599af32f6f..ec033c4a016 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py @@ -35,7 +35,7 @@ from tensorflow.python.platform import test class OptimizerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBasic(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -113,7 +113,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoVariables(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: # pylint: disable=cell-var-from-loop @@ -128,7 +128,7 @@ class OptimizerTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No.*variables'): sgd_op.minimize(loss) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -146,7 +146,7 @@ class OptimizerTest(test.TestCase): # var1 has no gradient sgd_op.minimize(loss, var_list=[var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_Minimize(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -162,7 +162,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.minimize(loss, var_list=[var0, var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_ApplyGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -176,7 +176,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.apply_gradients([(None, var0), (None, var1)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientsAsVariables(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -216,7 +216,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([-14., -13.], self.evaluate(var0)) self.assertAllClose([-6., -5.], self.evaluate(var1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputeGradientsWithTensors(self): x = ops.convert_to_tensor(1.0) def f(): diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py index b8840a8f242..86f1e27abd5 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_cell_test.py @@ -443,7 +443,7 @@ class RNNCellTest(test.TestCase): self.assertTrue( float(np.linalg.norm((res[1][0, :] - res[1][i, :]))) < 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWrapperCheckpointing(self): for wrapper_type in [ rnn_cell_impl.DropoutWrapper, diff --git a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py index be99a5d67a3..1c20d88fe4b 100644 --- a/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py +++ b/tensorflow/contrib/rnn/python/kernel_tests/core_rnn_test.py @@ -921,7 +921,7 @@ class LSTMTest(test.TestCase): # Smoke test, this should not raise an error rnn.dynamic_rnn(cell, inputs, dtype=dtypes.float32) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicRNNWithTupleStates(self): num_units = 3 input_size = 5 @@ -997,7 +997,7 @@ class LSTMTest(test.TestCase): self.assertAllEqual(array_ops.stack(outputs_static), outputs_dynamic) self.assertAllEqual(np.hstack(state_static), np.hstack(state_dynamic)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicRNNWithNestedTupleStates(self): num_units = 3 input_size = 5 @@ -1285,7 +1285,7 @@ class LSTMTest(test.TestCase): "Comparing individual variable gradients iteration %d" % i) self.assertAllEqual(a, b) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicEquivalentToStaticRNN(self): self._testDynamicEquivalentToStaticRNN(use_sequence_length=False) self._testDynamicEquivalentToStaticRNN(use_sequence_length=False) diff --git a/tensorflow/python/data/util/random_seed_test.py b/tensorflow/python/data/util/random_seed_test.py index 33227e82afe..a809151e6ef 100644 --- a/tensorflow/python/data/util/random_seed_test.py +++ b/tensorflow/python/data/util/random_seed_test.py @@ -30,7 +30,7 @@ from tensorflow.python.platform import test class RandomSeedTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRandomSeed(self): zero_t = constant_op.constant(0, dtype=dtypes.int64, name='zero') one_t = constant_op.constant(1, dtype=dtypes.int64, name='one') diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 826c6683b96..e129c2756af 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -46,7 +46,7 @@ from tensorflow.python.training import training class BackpropTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAggregateGradients(self): def fn(x): @@ -251,7 +251,7 @@ class BackpropTest(test.TestCase): g, = backprop.gradients_function(loss, [0])(logits, labels) self.assertAllEqual(g.numpy(), [[-0.5, 0.5]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientWithinTapeBlock(self): v1 = resource_variable_ops.ResourceVariable(1.) self.evaluate(v1.initializer) @@ -265,7 +265,7 @@ class BackpropTest(test.TestCase): grad = t.gradient(loss, v1) self.assertAllEqual(self.evaluate(grad), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestedSelfContexts(self): v1 = resource_variable_ops.ResourceVariable(1.) self.evaluate(v1.initializer) @@ -435,7 +435,7 @@ class BackpropTest(test.TestCase): self.assertEqual(backprop.implicit_grad(f)()[0][0], None) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeRepeatedSource(self): with backprop.GradientTape(persistent=False) as g: x = constant_op.constant(3.0) @@ -445,7 +445,7 @@ class BackpropTest(test.TestCase): self.assertEqual(self.evaluate(grad), [2.0, 2.0]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPersistentGradientTapeRepeatedSource(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -459,7 +459,7 @@ class BackpropTest(test.TestCase): self.assertEqual(self.evaluate(grad), [3.0, 11.0]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeStructure(self): with backprop.GradientTape(persistent=True) as g: # Using different constant values because constant tensors are @@ -482,7 +482,7 @@ class BackpropTest(test.TestCase): [1.0, {'x2': 2.0, 'x3': 3.0}]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTape(self): with backprop.GradientTape() as g: x = constant_op.constant(3.0) @@ -497,7 +497,7 @@ class BackpropTest(test.TestCase): grad = g.gradient(y, [x])[0] self.assertEqual(self.evaluate(grad), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeWithCond(self): x = constant_op.constant(3.0) @@ -518,7 +518,7 @@ class BackpropTest(test.TestCase): dy = g.gradient(y, [x])[0] self.assertEqual(self.evaluate(dy), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeWithWhileLoop(self): i = constant_op.constant(1) x = constant_op.constant(2.) @@ -553,7 +553,7 @@ class BackpropTest(test.TestCase): g.gradient(y, [x]) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPersistentTape(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -567,7 +567,7 @@ class BackpropTest(test.TestCase): del g @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testHigherOrderGradient(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -584,7 +584,7 @@ class BackpropTest(test.TestCase): del g @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPersistentNestedTape(self): with backprop.GradientTape(persistent=True) as g: x = constant_op.constant(3.0) @@ -605,7 +605,7 @@ class BackpropTest(test.TestCase): del g @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientTapeVariable(self): v = resource_variable_ops.ResourceVariable(1.0, name='v') self.evaluate(v.initializer) @@ -615,7 +615,7 @@ class BackpropTest(test.TestCase): self.assertAllEqual(self.evaluate(grad), 2.0) @test_util.assert_no_new_tensors - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNestedGradients(self): x = constant_op.constant(3.0) with backprop.GradientTape() as g: diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index a43b820f322..4459de99a64 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -2873,7 +2873,7 @@ class EstimatorHookOrderingTest(test.TestCase): class EstimatorIntegrationTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_complete_flow_with_a_simple_linear_model(self): def _model_fn(features, labels, mode): diff --git a/tensorflow/python/feature_column/feature_column_test.py b/tensorflow/python/feature_column/feature_column_test.py index d0023ffdd7c..a9013d8e426 100644 --- a/tensorflow/python/feature_column/feature_column_test.py +++ b/tensorflow/python/feature_column/feature_column_test.py @@ -2607,7 +2607,7 @@ class _LinearModelTest(test.TestCase): class InputLayerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_retrieving_input(self): features = {'a': [0.]} input_layer = InputLayer(fc.numeric_column('a')) diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index 81355a279c9..c72406e92b0 100644 --- a/tensorflow/python/framework/ops_test.py +++ b/tensorflow/python/framework/ops_test.py @@ -1681,7 +1681,7 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): # e should be dominated by c. self.assertEqual(e.op.control_inputs, []) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEager(self): def future(): future.calls += 1 @@ -1866,7 +1866,7 @@ class ControlDependenciesTest(test_util.TensorFlowTestCase): class OpScopeTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNames(self): with ops.name_scope("foo") as foo: self.assertEqual("foo/", foo) @@ -1897,7 +1897,7 @@ class OpScopeTest(test_util.TensorFlowTestCase): with ops.name_scope("a//b/c") as foo10: self.assertEqual("a//b/c/", foo10) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerDefaultScopeName(self): with ops.name_scope(None, "default") as scope: self.assertEqual(scope, "default/") diff --git a/tensorflow/python/framework/random_seed_test.py b/tensorflow/python/framework/random_seed_test.py index 19449226863..6696bffc6c5 100644 --- a/tensorflow/python/framework/random_seed_test.py +++ b/tensorflow/python/framework/random_seed_test.py @@ -26,7 +26,7 @@ from tensorflow.python.platform import test class RandomSeedTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRandomSeed(self): test_cases = [ # Each test case is a tuple with input to get_seed: diff --git a/tensorflow/python/framework/tensor_util_test.py b/tensorflow/python/framework/tensor_util_test.py index 35fff80c61b..d6edc136436 100644 --- a/tensorflow/python/framework/tensor_util_test.py +++ b/tensorflow/python/framework/tensor_util_test.py @@ -941,7 +941,7 @@ class ConstantValueTest(test.TestCase): class ConstantValueAsShapeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConstant(self): np_val = np.random.rand(3).astype(np.int32) tf_val = constant_op.constant(np_val) @@ -954,13 +954,13 @@ class ConstantValueAsShapeTest(test.TestCase): tensor_shape.TensorShape([]), tensor_util.constant_value_as_shape(tf_val)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShape(self): tf_val = array_ops.shape(constant_op.constant(0.0, shape=[1, 2, 3])) c_val = tensor_util.constant_value_as_shape(tf_val) self.assertEqual(tensor_shape.TensorShape([1, 2, 3]), c_val) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMinusOneBecomesNone(self): tf_val = constant_op.constant([-1, 1, -1], shape=[3]) c_val = tensor_util.constant_value_as_shape(tf_val) diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 7e82db028b8..1a0aa606092 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -587,7 +587,7 @@ class SubclassedModel(training.Model): class TestWeightSavingAndLoadingTFFormat(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_tensorflow_format_overwrite(self): with self.test_session() as session: model = SubclassedModel() @@ -676,7 +676,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): restore_on_create_y = self.evaluate(restore_on_create_y_tensor) self.assertAllClose(ref_y, restore_on_create_y) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model(self): def _make_graph_model(): a = keras.layers.Input(shape=(2,)) @@ -686,7 +686,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): self._weight_loading_test_template(_make_graph_model) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_subclassed_model(self): self._weight_loading_test_template(SubclassedModel) @@ -720,7 +720,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): y = self.evaluate(model(x)) self.assertAllClose(ref_y, y) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model_added_layer(self): def _save_graph_model(): a = keras.layers.Input(shape=(2,)) @@ -740,7 +740,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): _save_graph_model, _restore_graph_model, _restore_init_fn) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model_added_no_weight_layer(self): def _save_graph_model(): a = keras.layers.Input(shape=(2,)) @@ -761,7 +761,7 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): _save_graph_model, _restore_graph_model, _restore_init_fn) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_weight_loading_subclassed_model_added_layer(self): class SubclassedModelRestore(training.Model): diff --git a/tensorflow/python/keras/engine/sequential_test.py b/tensorflow/python/keras/engine/sequential_test.py index cdaf9162de8..0f54e29cee3 100644 --- a/tensorflow/python/keras/engine/sequential_test.py +++ b/tensorflow/python/keras/engine/sequential_test.py @@ -33,7 +33,7 @@ class TestSequential(test.TestCase): """Most Sequential model API tests are covered in `training_test.py`. """ - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_basic_methods(self): model = keras.models.Sequential() model.add(keras.layers.Dense(1, input_dim=2)) @@ -44,7 +44,7 @@ class TestSequential(test.TestCase): self.assertEqual(len(model.weights), 2 * 2) self.assertEqual(model.get_layer(name='dp').name, 'dp') - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_pop(self): num_hidden = 5 input_dim = 3 @@ -77,7 +77,7 @@ class TestSequential(test.TestCase): with self.assertRaises(TypeError): model.pop() - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_deferred_build_with_np_arrays(self): num_hidden = 5 input_dim = 3 @@ -102,7 +102,7 @@ class TestSequential(test.TestCase): [None, num_classes]) self.assertEqual(len(model.weights), 2 * 2) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_sequential_deferred_build_with_dataset_iterators(self): if not context.executing_eagerly(): # TODO(psv/fchollet): Add support for this use case in graph mode. @@ -136,7 +136,7 @@ class TestSequential(test.TestCase): [None, num_classes]) self.assertEqual(len(model.weights), 2 * 2) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_invalid_use_cases(self): # Added objects must be layer instances with self.assertRaises(TypeError): @@ -160,7 +160,7 @@ class TestSequential(test.TestCase): model.add(keras.layers.Dense(1, input_dim=1)) model.add(MyLayer()) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_nested_sequential_trainability(self): input_dim = 20 num_units = 10 diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index 183e26e8bf8..f10a16e6dc4 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -922,7 +922,7 @@ class DeferredModeTest(test.TestCase): self.assertEqual(repr(x), '') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSimpleNetworkBuilding(self): inputs = keras.engine.Input(shape=(32,)) if context.executing_eagerly(): @@ -947,7 +947,7 @@ class DeferredModeTest(test.TestCase): outputs = network(inputs) self.assertEqual(outputs.shape.as_list(), [10, 4]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMultiIONetworkbuilding(self): input_a = keras.engine.Input(shape=(32,)) input_b = keras.engine.Input(shape=(16,)) diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py index 1571a7782aa..bdb30351290 100644 --- a/tensorflow/python/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/engine/training_eager_test.py @@ -647,7 +647,7 @@ class LossWeightingTest(test.TestCase): class CorrectnessTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_loss_correctness(self): # Test that training loss is the same in eager and graph # (by comparing it to a reference value in a deterministic case) @@ -668,7 +668,7 @@ class CorrectnessTest(test.TestCase): self.assertEqual( np.around(history.history['loss'][-1], decimals=4), 0.6173) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness(self): model = keras.Sequential() model.add(keras.layers.Dense(3, @@ -689,7 +689,7 @@ class CorrectnessTest(test.TestCase): outs = model.evaluate(x, y) self.assertEqual(outs[1], 0.) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_loss_correctness_with_iterator(self): # Test that training loss is the same in eager and graph # (by comparing it to a reference value in a deterministic case) @@ -712,7 +712,7 @@ class CorrectnessTest(test.TestCase): history = model.fit(iterator, epochs=1, steps_per_epoch=10) self.assertEqual(np.around(history.history['loss'][-1], decimals=4), 0.6173) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_metrics_correctness_with_iterator(self): model = keras.Sequential() model.add( diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index a1ab7201895..d9e548f01f8 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -1696,7 +1696,7 @@ class TestTrainingWithDataTensors(test.TestCase): model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np]) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_metric_names_are_identical_in_graph_and_eager(self): a = keras.layers.Input(shape=(3,), name='input_a') b = keras.layers.Input(shape=(3,), name='input_b') @@ -1723,7 +1723,7 @@ class TestTrainingWithDataTensors(test.TestCase): class TestTrainingWithDatasetIterators(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_training_and_eval_methods_on_iterators_single_io(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -1813,7 +1813,7 @@ class TestTrainingWithDatasetIterators(test.TestCase): ops.get_default_graph().finalize() model.fit(iterator, epochs=1, steps_per_epoch=2, verbose=1) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_iterators_running_out_of_data(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') @@ -1867,7 +1867,7 @@ class TestTrainingWithDataset(test.TestCase): model.fit(dataset, epochs=1, steps_per_epoch=2, verbose=0, validation_data=dataset, validation_steps=2) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_training_and_eval_methods_on_dataset(self): with self.test_session(): x = keras.layers.Input(shape=(3,), name='input') diff --git a/tensorflow/python/keras/layers/convolutional_test.py b/tensorflow/python/keras/layers/convolutional_test.py index 39988ba33ae..f904744422a 100644 --- a/tensorflow/python/keras/layers/convolutional_test.py +++ b/tensorflow/python/keras/layers/convolutional_test.py @@ -45,7 +45,7 @@ class Convolution1DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, length, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv1d(self): kwargs = { 'filters': 2, @@ -117,7 +117,7 @@ class Conv2DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv2d(self): kwargs = { 'filters': 2, @@ -192,7 +192,7 @@ class Conv2DTransposeTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv2dtranspose(self): kwargs = { 'filters': 2, @@ -258,7 +258,7 @@ class Conv3DTransposeTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, depth, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv3dtranspose(self): kwargs = { 'filters': 2, @@ -322,7 +322,7 @@ class SeparableConv1DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, length, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_separable_conv1d(self): kwargs = { 'filters': 2, @@ -398,7 +398,7 @@ class SeparableConv2DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_separable_conv2d(self): kwargs = { 'filters': 2, @@ -477,7 +477,7 @@ class Conv3DTest(test.TestCase): kwargs=test_kwargs, input_shape=(num_samples, depth, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_conv3d(self): kwargs = { 'filters': 2, @@ -529,7 +529,7 @@ class Conv3DTest(test.TestCase): class ZeroPaddingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_zero_padding_1d(self): num_samples = 2 input_dim = 2 @@ -581,7 +581,7 @@ class ZeroPaddingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.ZeroPadding1D(padding=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_zero_padding_2d(self): num_samples = 2 stack_size = 2 @@ -660,7 +660,7 @@ class ZeroPaddingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.ZeroPadding2D(padding=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_zero_padding_3d(self): num_samples = 2 stack_size = 2 @@ -702,13 +702,13 @@ class ZeroPaddingTest(test.TestCase): class UpSamplingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_1d(self): with self.test_session(use_gpu=True): testing_utils.layer_test( keras.layers.UpSampling1D, kwargs={'size': 2}, input_shape=(3, 5, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_2d(self): num_samples = 2 stack_size = 2 @@ -758,7 +758,7 @@ class UpSamplingTest(test.TestCase): np.testing.assert_allclose(np_output, expected_out) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_upsampling_3d(self): num_samples = 2 stack_size = 2 @@ -818,7 +818,7 @@ class UpSamplingTest(test.TestCase): class CroppingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_cropping_1d(self): num_samples = 2 time_length = 4 @@ -837,7 +837,7 @@ class CroppingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.Cropping1D(cropping=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_cropping_2d(self): num_samples = 2 stack_size = 2 @@ -905,7 +905,7 @@ class CroppingTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.Cropping2D(cropping=None) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_cropping_3d(self): num_samples = 2 stack_size = 2 diff --git a/tensorflow/python/keras/layers/core_test.py b/tensorflow/python/keras/layers/core_test.py index ff8af976b99..226403c5927 100644 --- a/tensorflow/python/keras/layers/core_test.py +++ b/tensorflow/python/keras/layers/core_test.py @@ -51,7 +51,7 @@ class CoreLayersTest(test.TestCase): dropout = keras.layers.Dropout(0.5) self.assertEqual(True, dropout.supports_masking) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_spatial_dropout(self): testing_utils.layer_test( keras.layers.SpatialDropout1D, @@ -78,7 +78,7 @@ class CoreLayersTest(test.TestCase): kwargs={'rate': 0.5, 'data_format': 'channels_first'}, input_shape=(2, 3, 4, 4, 5)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_activation(self): # with string argument testing_utils.layer_test( @@ -92,7 +92,7 @@ class CoreLayersTest(test.TestCase): kwargs={'activation': keras.backend.relu}, input_shape=(3, 2)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_reshape(self): testing_utils.layer_test( keras.layers.Reshape, @@ -114,12 +114,12 @@ class CoreLayersTest(test.TestCase): kwargs={'target_shape': (-1, 1)}, input_shape=(None, None, 2)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_permute(self): testing_utils.layer_test( keras.layers.Permute, kwargs={'dims': (2, 1)}, input_shape=(3, 2, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_flatten(self): testing_utils.layer_test( keras.layers.Flatten, kwargs={}, input_shape=(3, 2, 4)) @@ -134,7 +134,7 @@ class CoreLayersTest(test.TestCase): np.transpose(inputs, (0, 2, 3, 1)), (-1, 5 * 5 * 3)) self.assertAllClose(outputs, target_outputs) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_repeat_vector(self): testing_utils.layer_test( keras.layers.RepeatVector, kwargs={'n': 3}, input_shape=(3, 2)) @@ -173,7 +173,7 @@ class CoreLayersTest(test.TestCase): config = ld.get_config() ld = keras.layers.Lambda.from_config(config) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dense(self): testing_utils.layer_test( keras.layers.Dense, kwargs={'units': 3}, input_shape=(3, 2)) diff --git a/tensorflow/python/keras/layers/cudnn_recurrent_test.py b/tensorflow/python/keras/layers/cudnn_recurrent_test.py index 9d186f8c586..f1ee441f5f4 100644 --- a/tensorflow/python/keras/layers/cudnn_recurrent_test.py +++ b/tensorflow/python/keras/layers/cudnn_recurrent_test.py @@ -30,7 +30,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class CuDNNTest(test.TestCase, parameterized.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_cudnn_rnn_basics(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): @@ -58,7 +58,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): 'go_backwards': go_backwards}, input_shape=(num_samples, timesteps, input_size)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trainability(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): @@ -288,7 +288,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): self.assertAllClose( model.predict(inputs), cudnn_model.predict(inputs), atol=1e-4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_cudnnrnn_bidirectional(self): if test.is_gpu_available(cuda_only=True): with self.test_session(use_gpu=True): diff --git a/tensorflow/python/keras/layers/gru_test.py b/tensorflow/python/keras/layers/gru_test.py index 234434f7a02..57f660b6d5a 100644 --- a/tensorflow/python/keras/layers/gru_test.py +++ b/tensorflow/python/keras/layers/gru_test.py @@ -29,7 +29,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class GRULayerTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_return_sequences_GRU(self): num_samples = 2 timesteps = 3 @@ -41,7 +41,7 @@ class GRULayerTest(test.TestCase): 'return_sequences': True}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dynamic_behavior_GRU(self): num_samples = 2 timesteps = 3 @@ -55,7 +55,7 @@ class GRULayerTest(test.TestCase): y = np.random.random((num_samples, units)) model.train_on_batch(x, y) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dropout_GRU(self): num_samples = 2 timesteps = 3 @@ -68,7 +68,7 @@ class GRULayerTest(test.TestCase): 'recurrent_dropout': 0.1}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_implementation_mode_GRU(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/local_test.py b/tensorflow/python/keras/layers/local_test.py index 8df3f6b7bd7..9639e0251f5 100644 --- a/tensorflow/python/keras/layers/local_test.py +++ b/tensorflow/python/keras/layers/local_test.py @@ -28,7 +28,7 @@ from tensorflow.python.platform import test class LocallyConnectedLayersTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_locallyconnected_1d(self): num_samples = 2 num_steps = 8 @@ -92,7 +92,7 @@ class LocallyConnectedLayersTest(test.TestCase): self.assertEqual(layer.kernel.constraint, k_constraint) self.assertEqual(layer.bias.constraint, b_constraint) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_locallyconnected_2d(self): num_samples = 8 filters = 3 @@ -118,7 +118,7 @@ class LocallyConnectedLayersTest(test.TestCase): }, input_shape=(num_samples, num_row, num_col, stack_size)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_locallyconnected_2d_channels_first(self): num_samples = 8 filters = 3 diff --git a/tensorflow/python/keras/layers/lstm_test.py b/tensorflow/python/keras/layers/lstm_test.py index 87cb344bf82..ae381f59556 100644 --- a/tensorflow/python/keras/layers/lstm_test.py +++ b/tensorflow/python/keras/layers/lstm_test.py @@ -29,7 +29,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class LSTMLayerTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_return_sequences_LSTM(self): num_samples = 2 timesteps = 3 @@ -56,7 +56,7 @@ class LSTMLayerTest(test.TestCase): outputs = model.layers[-1].output self.assertEquals(outputs.get_shape().as_list(), [None, timesteps, units]) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dynamic_behavior_LSTM(self): num_samples = 2 timesteps = 3 @@ -70,7 +70,7 @@ class LSTMLayerTest(test.TestCase): y = np.random.random((num_samples, units)) model.train_on_batch(x, y) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dropout_LSTM(self): num_samples = 2 timesteps = 3 @@ -83,7 +83,7 @@ class LSTMLayerTest(test.TestCase): 'recurrent_dropout': 0.1}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_implementation_mode_LSTM(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/merge_test.py b/tensorflow/python/keras/layers/merge_test.py index 8a097cf7f57..39bc98d0396 100644 --- a/tensorflow/python/keras/layers/merge_test.py +++ b/tensorflow/python/keras/layers/merge_test.py @@ -28,7 +28,7 @@ from tensorflow.python.platform import test class MergeLayersTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_add(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -76,7 +76,7 @@ class MergeLayersTest(test.TestCase): with self.assertRaises(ValueError): keras.layers.add([i1]) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_multiply(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -92,7 +92,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, x1 * x2 * x3, atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_average(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -106,7 +106,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, 0.5 * (x1 + x2), atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_maximum(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -120,7 +120,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, np.maximum(x1, x2), atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_minimum(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -134,7 +134,7 @@ class MergeLayersTest(test.TestCase): self.assertEqual(out.shape, (2, 4, 5)) self.assertAllClose(out, np.minimum(x1, x2), atol=1e-4) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_concatenate(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) @@ -169,7 +169,7 @@ class MergeLayersTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'called on a list'): keras.layers.concatenate([i1], axis=-1) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_dot(self): i1 = keras.layers.Input(shape=(4,)) i2 = keras.layers.Input(shape=(4,)) @@ -215,7 +215,7 @@ class MergeLayersTest(test.TestCase): dot = keras.layers.Dot(1) dot.compute_output_shape(1) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_merge_subtract(self): i1 = keras.layers.Input(shape=(4, 5)) i2 = keras.layers.Input(shape=(4, 5)) diff --git a/tensorflow/python/keras/layers/noise_test.py b/tensorflow/python/keras/layers/noise_test.py index bde2185f03b..aa2be62390b 100644 --- a/tensorflow/python/keras/layers/noise_test.py +++ b/tensorflow/python/keras/layers/noise_test.py @@ -40,7 +40,7 @@ class NoiseLayersTest(test.TestCase): kwargs={'rate': 0.5}, input_shape=(3, 2, 3)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_AlphaDropout(self): testing_utils.layer_test( keras.layers.AlphaDropout, diff --git a/tensorflow/python/keras/layers/pooling_test.py b/tensorflow/python/keras/layers/pooling_test.py index cbd58a22879..2cd9939e66f 100644 --- a/tensorflow/python/keras/layers/pooling_test.py +++ b/tensorflow/python/keras/layers/pooling_test.py @@ -27,14 +27,14 @@ from tensorflow.python.platform import test class GlobalPoolingTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_globalpooling_1d(self): testing_utils.layer_test(keras.layers.pooling.GlobalMaxPooling1D, input_shape=(3, 4, 5)) testing_utils.layer_test( keras.layers.pooling.GlobalAveragePooling1D, input_shape=(3, 4, 5)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_globalpooling_2d(self): testing_utils.layer_test( keras.layers.pooling.GlobalMaxPooling2D, @@ -53,7 +53,7 @@ class GlobalPoolingTest(test.TestCase): kwargs={'data_format': 'channels_last'}, input_shape=(3, 5, 6, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_globalpooling_3d(self): testing_utils.layer_test( keras.layers.pooling.GlobalMaxPooling3D, @@ -75,7 +75,7 @@ class GlobalPoolingTest(test.TestCase): class Pooling2DTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_maxpooling_2d(self): pool_size = (3, 3) for strides in [(1, 1), (2, 2)]: @@ -88,7 +88,7 @@ class Pooling2DTest(test.TestCase): }, input_shape=(3, 5, 6, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_averagepooling_2d(self): testing_utils.layer_test( keras.layers.AveragePooling2D, @@ -122,7 +122,7 @@ class Pooling2DTest(test.TestCase): class Pooling3DTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_maxpooling_3d(self): pool_size = (3, 3, 3) testing_utils.layer_test( @@ -141,7 +141,7 @@ class Pooling3DTest(test.TestCase): }, input_shape=(3, 4, 11, 12, 10)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_averagepooling_3d(self): pool_size = (3, 3, 3) testing_utils.layer_test( @@ -163,7 +163,7 @@ class Pooling3DTest(test.TestCase): class Pooling1DTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_maxpooling_1d(self): for padding in ['valid', 'same']: for stride in [1, 2]: @@ -173,7 +173,7 @@ class Pooling1DTest(test.TestCase): 'padding': padding}, input_shape=(3, 5, 4)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_averagepooling_1d(self): for padding in ['valid', 'same']: for stride in [1, 2]: diff --git a/tensorflow/python/keras/layers/simplernn_test.py b/tensorflow/python/keras/layers/simplernn_test.py index 3d24b0d5045..18fefbe84f6 100644 --- a/tensorflow/python/keras/layers/simplernn_test.py +++ b/tensorflow/python/keras/layers/simplernn_test.py @@ -29,7 +29,7 @@ from tensorflow.python.training.rmsprop import RMSPropOptimizer class SimpleRNNLayerTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_return_sequences_SimpleRNN(self): num_samples = 2 timesteps = 3 @@ -41,7 +41,7 @@ class SimpleRNNLayerTest(test.TestCase): 'return_sequences': True}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dynamic_behavior_SimpleRNN(self): num_samples = 2 timesteps = 3 @@ -55,7 +55,7 @@ class SimpleRNNLayerTest(test.TestCase): y = np.random.random((num_samples, units)) model.train_on_batch(x, y) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_dropout_SimpleRNN(self): num_samples = 2 timesteps = 3 @@ -68,7 +68,7 @@ class SimpleRNNLayerTest(test.TestCase): 'recurrent_dropout': 0.1}, input_shape=(num_samples, timesteps, embedding_dim)) - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_implementation_mode_SimpleRNN(self): num_samples = 2 timesteps = 3 diff --git a/tensorflow/python/keras/layers/wrappers_test.py b/tensorflow/python/keras/layers/wrappers_test.py index 3b997732b5e..c8f0d216e6f 100644 --- a/tensorflow/python/keras/layers/wrappers_test.py +++ b/tensorflow/python/keras/layers/wrappers_test.py @@ -71,7 +71,7 @@ class _RNNCellWithConstants(keras.layers.Layer): class TimeDistributedTest(test.TestCase): - @tf_test_util.run_in_graph_and_eager_modes() + @tf_test_util.run_in_graph_and_eager_modes def test_timedistributed_dense(self): model = keras.models.Sequential() model.add( diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 2971e2e1378..b7e16a41dda 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -173,7 +173,7 @@ def get_nested_model_3(input_dim, num_classes): class ModelSubclassingTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_io_workflow_with_np_arrays(self): num_classes = 2 num_samples = 100 @@ -192,7 +192,7 @@ class ModelSubclassingTest(test.TestCase): model.fit(x, y, epochs=2, batch_size=32, verbose=0) _ = model.evaluate(x, y, verbose=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_multi_io_workflow_with_np_arrays(self): num_classes = (2, 3) num_samples = 1000 @@ -251,7 +251,7 @@ class ModelSubclassingTest(test.TestCase): model.fit([x1, x2], [y1, y2], epochs=2, steps_per_epoch=10, verbose=0) _ = model.evaluate(steps=10, verbose=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_io_workflow_with_dataset_iterators(self): num_classes = 2 num_samples = 10 @@ -325,7 +325,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.inputs), 2) self.assertEqual(len(model.outputs), 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_updates(self): # test that updates get run during training num_samples = 100 @@ -419,7 +419,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.get_updates_for(x)), 2) self.assertEqual(len(model.get_losses_for(x)), 1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_training_and_inference_behavior(self): # test that dropout is applied in training and not inference @@ -447,7 +447,7 @@ class ModelSubclassingTest(test.TestCase): loss = model.train_on_batch(x, y) self.assertGreater(loss, 0.1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_training_methods(self): # test fit, train_on_batch # on different input types: list, dict @@ -500,14 +500,14 @@ class ModelSubclassingTest(test.TestCase): model = MultiIOTestModel(num_classes=num_classes, use_bn=True) model.predict_on_batch([x1, x2]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trainable_mutation(self): # test that you can change `trainable` on a model or layer, and that # it freezes the model state during training # TODO(fchollet): add test after we unify BN behavior in eager and symbolic. pass - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_saving(self): num_classes = (2, 3) @@ -549,7 +549,7 @@ class ModelSubclassingTest(test.TestCase): self.assertAllClose(y_ref_1, y1, atol=1e-5) self.assertAllClose(y_ref_2, y2, atol=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_summary(self): class ToString(object): @@ -575,7 +575,7 @@ class ModelSubclassingTest(test.TestCase): model.summary(print_fn=print_fn) self.assertTrue('Trainable params: 587' in print_fn.contents) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_subclass_nested_in_subclass(self): num_classes = 2 num_samples = 100 @@ -598,7 +598,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.trainable_weights), 6 + len(model.test_net.trainable_weights)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_graph_nested_in_subclass(self): num_classes = 2 num_samples = 100 @@ -621,7 +621,7 @@ class ModelSubclassingTest(test.TestCase): self.assertEqual(len(model.trainable_weights), 6 + len(model.test_net.trainable_weights)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_subclass_nested_in_graph(self): num_classes = 2 num_samples = 100 @@ -643,7 +643,7 @@ class ModelSubclassingTest(test.TestCase): len(model.non_trainable_weights), 4) self.assertEqual(len(model.trainable_weights), 12) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_support_for_manual_training_arg(self): # In most cases, the `training` argument is left unspecified, in which # case it defaults to value corresponding to the Model method being used @@ -752,7 +752,7 @@ class CustomCallModel(keras.Model): class CustomCallSignatureTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_no_inputs_in_signature(self): model = CustomCallModel() first = array_ops.ones([2, 3]) @@ -766,7 +766,7 @@ class CustomCallSignatureTests(test.TestCase): output = model(first, second=second, training=False) self.assertAllClose(expected_output, self.evaluate(output)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_inputs_in_signature(self): class HasInputsAndOtherPositional(keras.Model): @@ -783,7 +783,7 @@ class CustomCallSignatureTests(test.TestCase): x1, x2 = keras.Input((1, 1)), keras.Input((1, 1)) model(x1, x2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_kwargs_in_signature(self): class HasKwargs(keras.Model): @@ -797,7 +797,7 @@ class CustomCallSignatureTests(test.TestCase): if not context.executing_eagerly(): six.assertCountEqual(self, [arg], model.inputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_args_in_signature(self): class HasArgs(keras.Model): diff --git a/tensorflow/python/keras/models_test.py b/tensorflow/python/keras/models_test.py index e6e45902a8f..ad3819e6e73 100644 --- a/tensorflow/python/keras/models_test.py +++ b/tensorflow/python/keras/models_test.py @@ -129,7 +129,7 @@ class TestModelCloning(test.TestCase): class CheckpointingTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_optimizer_dependency(self): model = keras.models.Sequential() model.add(keras.layers.Dense(1, input_shape=(4,))) diff --git a/tensorflow/python/kernel_tests/array_ops_test.py b/tensorflow/python/kernel_tests/array_ops_test.py index 08bf2d9c644..40567571e6d 100644 --- a/tensorflow/python/kernel_tests/array_ops_test.py +++ b/tensorflow/python/kernel_tests/array_ops_test.py @@ -1006,7 +1006,7 @@ class SliceAssignTest(test_util.TensorFlowTestCase): class ShapeSizeRankTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDenseShape(self): t_value = [[0, 42], [24, 0]] self.assertAllEqual((2, 2), self.evaluate(array_ops.shape(t_value))) @@ -1018,7 +1018,7 @@ class ShapeSizeRankTest(test_util.TensorFlowTestCase): self.assertEqual(4, self.evaluate(array_ops.size(t))) self.assertEqual(2, self.evaluate(array_ops.rank(t))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSparseShape(self): sp_value = sparse_tensor.SparseTensorValue( indices=((0, 1), (1, 0)), values=(42, 24), dense_shape=(2, 2)) @@ -1031,7 +1031,7 @@ class ShapeSizeRankTest(test_util.TensorFlowTestCase): self.assertEqual(4, self.evaluate(array_ops.size(sp))) self.assertEqual(2, self.evaluate(array_ops.rank(sp))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSizeDtype(self): tensor = [1] self.assertEqual(dtypes.int32, self.evaluate(array_ops.size(tensor)).dtype) @@ -1123,7 +1123,7 @@ class SequenceMaskTest(test_util.TensorFlowTestCase): class ConcatSliceResourceTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConcatSlice(self): r1 = test_ops.stub_resource_handle_op(container="a", shared_name="b") r2 = test_ops.stub_resource_handle_op(container="a", shared_name="c") diff --git a/tensorflow/python/kernel_tests/atrous_convolution_test.py b/tensorflow/python/kernel_tests/atrous_convolution_test.py index 0ef08581c9f..b98e5fd3866 100644 --- a/tensorflow/python/kernel_tests/atrous_convolution_test.py +++ b/tensorflow/python/kernel_tests/atrous_convolution_test.py @@ -124,7 +124,7 @@ class AtrousConvolutionTest(test.TestCase): x, w, "VALID", dilation_rate=[2, 2], data_format="NCHW") self.assertEqual(y.shape.as_list(), [1, 20, None, None]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolution2D(self): with self._delay_checks() as add_check: for padding in ["SAME", "VALID"]: @@ -139,7 +139,7 @@ class AtrousConvolutionTest(test.TestCase): dilation_rate=dilation_rate, ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolution3D(self): with self._delay_checks() as add_check: for padding in ["SAME", "VALID"]: @@ -158,7 +158,7 @@ class AtrousConvolutionTest(test.TestCase): dilation_rate=dilation_rate, ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolution1D(self): with self._delay_checks() as add_check: for padding in ["SAME", "VALID"]: @@ -173,7 +173,7 @@ class AtrousConvolutionTest(test.TestCase): dilation_rate=[rate], ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousConvolutionNC(self): if test.is_gpu_available(cuda_only=True): # "NCW" and "NCHW" formats are currently supported only on CUDA. @@ -197,7 +197,7 @@ class AtrousConvolutionTest(test.TestCase): data_format="NCHW", ) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAtrousSequence(self): """Tests optimization of sequence of atrous convolutions. diff --git a/tensorflow/python/kernel_tests/check_ops_test.py b/tensorflow/python/kernel_tests/check_ops_test.py index 7ef841c96b5..bda6ca5ca91 100644 --- a/tensorflow/python/kernel_tests/check_ops_test.py +++ b/tensorflow/python/kernel_tests/check_ops_test.py @@ -34,45 +34,45 @@ from tensorflow.python.platform import test class AssertProperIterableTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_tensor_raises(self): tensor = constant_op.constant(1) with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(tensor) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_sparse_tensor_raises(self): ten = sparse_tensor.SparseTensor( indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4]) with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(ten) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_ndarray_raises(self): array = np.array([1, 2, 3]) with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(array) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_single_string_raises(self): mystr = "hello" with self.assertRaisesRegexp(TypeError, "proper"): check_ops.assert_proper_iterable(mystr) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_non_iterable_object_raises(self): non_iterable = 1234 with self.assertRaisesRegexp(TypeError, "to be iterable"): check_ops.assert_proper_iterable(non_iterable) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_list_does_not_raise(self): list_of_stuff = [ constant_op.constant([11, 22]), constant_op.constant([1, 2]) ] check_ops.assert_proper_iterable(list_of_stuff) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_generator_does_not_raise(self): generator_of_stuff = (constant_op.constant([11, 22]), constant_op.constant( [1, 2])) @@ -81,14 +81,14 @@ class AssertProperIterableTest(test.TestCase): class AssertEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): small = constant_op.constant([1, 2], name="small") with ops.control_dependencies([check_ops.assert_equal(small, small)]): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_scalar_comparison(self): const_true = constant_op.constant(True, name="true") const_false = constant_op.constant(False, name="false") @@ -101,7 +101,7 @@ class AssertEqualTest(test.TestCase): x = check_ops.assert_equal(small, small) assert x is None - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater(self): # Static check static_small = constant_op.constant([1, 2], name="small") @@ -179,7 +179,7 @@ First 2 elements of y: check_ops.assert_equal(big, small, message="big does not equal small", summarize=2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less(self): # Static check static_small = constant_op.constant([3, 1], name="small") @@ -196,7 +196,7 @@ First 2 elements of y: with self.assertRaisesOpError("small.*big"): out.eval(feed_dict={small: [3, 1], big: [4, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal_and_broadcastable_shapes(self): small = constant_op.constant([[1, 2], [1, 2]], name="small") small_2 = constant_op.constant([1, 2], name="small_2") @@ -204,7 +204,7 @@ First 2 elements of y: out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="small") small_2 = constant_op.constant([1, 1], name="small_2") @@ -219,13 +219,13 @@ First 2 elements of y: out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_not_equal_and_broadcastable_shapes(self): cond = constant_op.constant([True, False], name="small") with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): check_ops.assert_equal(cond, False, message="fail") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -236,7 +236,7 @@ First 2 elements of y: class AssertNoneEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_not_equal(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([10, 20], name="small") @@ -245,7 +245,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal(self): small = constant_op.constant([3, 1], name="small") with self.assertRaisesOpError("x != y did not hold"): @@ -254,7 +254,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_not_equal_and_broadcastable_shapes(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3], name="big") @@ -263,7 +263,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_not_equal_but_non_broadcastable_shapes(self): with self.test_session(): small = constant_op.constant([1, 1, 1], name="small") @@ -280,7 +280,7 @@ class AssertNoneEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): with self.test_session(): larry = constant_op.constant([]) @@ -300,7 +300,7 @@ class AssertNoneEqualTest(test.TestCase): class AssertAllCloseTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): x = constant_op.constant(1., name="x") y = constant_op.constant(1., name="y") @@ -309,7 +309,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_32_bit_due_to_default_rtol(self): eps = np.finfo(np.float32).eps # Default rtol/atol is 10*eps @@ -320,7 +320,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_32_bit_due_to_default_atol(self): eps = np.finfo(np.float32).eps # Default rtol/atol is 10*eps @@ -331,7 +331,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_64_bit_due_to_default_rtol(self): eps = np.finfo(np.float64).eps # Default rtol/atol is 10*eps @@ -342,7 +342,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_64_bit_due_to_default_atol(self): eps = np.finfo(np.float64).eps # Default rtol/atol is 10*eps @@ -353,7 +353,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_due_to_custom_rtol(self): x = constant_op.constant(1., name="x") y = constant_op.constant(1.1, name="y") @@ -363,7 +363,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_close_enough_due_to_custom_atol(self): x = constant_op.constant(0., name="x") y = constant_op.constant(0.1, name="y", dtype=np.float32) @@ -373,7 +373,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -381,7 +381,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(larry) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_atol_violated(self): x = constant_op.constant(10., name="x") y = constant_op.constant(10.2, name="y") @@ -392,7 +392,7 @@ class AssertAllCloseTest(test.TestCase): out = array_ops.identity(x) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_default_rtol_violated(self): x = constant_op.constant(0.1, name="x") y = constant_op.constant(0.0, name="y") @@ -412,7 +412,7 @@ class AssertAllCloseTest(test.TestCase): class AssertLessTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal(self): small = constant_op.constant([1, 2], name="small") with self.assertRaisesOpError("failure message.*\n*.* x < y did not hold"): @@ -422,7 +422,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -431,7 +431,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less(self): small = constant_op.constant([3, 1], name="small") big = constant_op.constant([4, 2], name="big") @@ -439,7 +439,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 2], name="big") @@ -447,7 +447,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="small") big = constant_op.constant([3, 2], name="big") @@ -462,7 +462,7 @@ class AssertLessTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -480,7 +480,7 @@ class AssertLessTest(test.TestCase): class AssertLessEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): small = constant_op.constant([1, 2], name="small") with ops.control_dependencies( @@ -488,7 +488,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -499,7 +499,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less_equal(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 2], name="big") @@ -507,7 +507,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_less_equal_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 1], name="big") @@ -515,7 +515,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less_equal_but_non_broadcastable_shapes(self): small = constant_op.constant([3, 1], name="small") big = constant_op.constant([1, 1, 1], name="big") @@ -531,7 +531,7 @@ class AssertLessEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -543,7 +543,7 @@ class AssertLessEqualTest(test.TestCase): class AssertGreaterTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_equal(self): small = constant_op.constant([1, 2], name="small") with self.assertRaisesOpError("fail"): @@ -553,7 +553,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -562,7 +562,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(big) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater(self): small = constant_op.constant([3, 1], name="small") big = constant_op.constant([4, 2], name="big") @@ -570,7 +570,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 2], name="big") @@ -578,7 +578,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_greater_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="small") big = constant_op.constant([3, 2], name="big") @@ -593,7 +593,7 @@ class AssertGreaterTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -604,7 +604,7 @@ class AssertGreaterTest(test.TestCase): class AssertGreaterEqualTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_equal(self): small = constant_op.constant([1, 2], name="small") with ops.control_dependencies( @@ -612,7 +612,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 4], name="big") @@ -623,7 +623,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater_equal(self): small = constant_op.constant([1, 2], name="small") big = constant_op.constant([3, 2], name="big") @@ -632,7 +632,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_greater_equal_and_broadcastable_shapes(self): small = constant_op.constant([1], name="small") big = constant_op.constant([3, 1], name="big") @@ -641,7 +641,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_less_equal_but_non_broadcastable_shapes(self): small = constant_op.constant([1, 1, 1], name="big") big = constant_op.constant([3, 1], name="small") @@ -657,7 +657,7 @@ class AssertGreaterEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_both_empty(self): larry = constant_op.constant([]) curly = constant_op.constant([]) @@ -669,14 +669,14 @@ class AssertGreaterEqualTest(test.TestCase): class AssertNegativeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_negative(self): frank = constant_op.constant([-1, -2], name="frank") with ops.control_dependencies([check_ops.assert_negative(frank)]): out = array_ops.identity(frank) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_positive(self): doug = constant_op.constant([1, 2], name="doug") with self.assertRaisesOpError("fail"): @@ -686,7 +686,7 @@ class AssertNegativeTest(test.TestCase): out = array_ops.identity(doug) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_zero(self): claire = constant_op.constant([0], name="claire") with self.assertRaisesOpError("x < 0 did not hold"): @@ -694,7 +694,7 @@ class AssertNegativeTest(test.TestCase): out = array_ops.identity(claire) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is negative when it satisfies: # For every element x_i in x, x_i < 0 @@ -708,7 +708,7 @@ class AssertNegativeTest(test.TestCase): class AssertPositiveTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_negative(self): freddie = constant_op.constant([-1, -2], name="freddie") with self.assertRaisesOpError("fail"): @@ -718,14 +718,14 @@ class AssertPositiveTest(test.TestCase): out = array_ops.identity(freddie) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_positive(self): remmy = constant_op.constant([1, 2], name="remmy") with ops.control_dependencies([check_ops.assert_positive(remmy)]): out = array_ops.identity(remmy) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_zero(self): meechum = constant_op.constant([0], name="meechum") with self.assertRaisesOpError("x > 0 did not hold"): @@ -733,7 +733,7 @@ class AssertPositiveTest(test.TestCase): out = array_ops.identity(meechum) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is positive when it satisfies: # For every element x_i in x, x_i > 0 @@ -747,7 +747,7 @@ class AssertPositiveTest(test.TestCase): class AssertRankTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 1 @@ -768,7 +768,7 @@ class AssertRankTest(test.TestCase): with self.assertRaisesOpError("fail.*my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 0 @@ -784,7 +784,7 @@ class AssertRankTest(test.TestCase): [check_ops.assert_rank(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_too_large_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 0 @@ -802,7 +802,7 @@ class AssertRankTest(test.TestCase): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 1 @@ -818,7 +818,7 @@ class AssertRankTest(test.TestCase): [check_ops.assert_rank(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 2 @@ -836,7 +836,7 @@ class AssertRankTest(test.TestCase): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_scalar_static(self): tensor = constant_op.constant([1, 2], name="my_tensor") with self.assertRaisesRegexp(ValueError, "Rank must be a scalar"): @@ -852,7 +852,7 @@ class AssertRankTest(test.TestCase): [check_ops.assert_rank(tensor, rank_tensor)]): array_ops.identity(tensor).eval(feed_dict={rank_tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_integer_static(self): tensor = constant_op.constant([1, 2], name="my_tensor") with self.assertRaisesRegexp(TypeError, @@ -873,7 +873,7 @@ class AssertRankTest(test.TestCase): class AssertRankInTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_raises_if_rank_mismatch_static_rank(self): tensor_rank0 = constant_op.constant(42, name="my_tensor") with self.assertRaisesRegexp( @@ -890,7 +890,7 @@ class AssertRankInTest(test.TestCase): with self.assertRaisesOpError("fail.*my_tensor.*rank"): array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_doesnt_raise_if_rank_matches_static_rank(self): tensor_rank0 = constant_op.constant(42, name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): @@ -906,7 +906,7 @@ class AssertRankInTest(test.TestCase): check_ops.assert_rank_in(tensor_rank0, desired_ranks)]): array_ops.identity(tensor_rank0).eval(feed_dict={tensor_rank0: 42.0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_doesnt_raise_if_rank_matches_static_rank(self): tensor_rank1 = constant_op.constant([42, 43], name="my_tensor") for desired_ranks in ((0, 1, 2), (1, 0, 2), (1, 2, 0)): @@ -924,7 +924,7 @@ class AssertRankInTest(test.TestCase): tensor_rank1: (42.0, 43.0) }) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_mismatches_static_rank(self): tensor_rank1 = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(ValueError, "rank"): @@ -942,7 +942,7 @@ class AssertRankInTest(test.TestCase): tensor_rank1: (42.0, 43.0) }) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_scalar_static(self): tensor = constant_op.constant((42, 43), name="my_tensor") desired_ranks = ( @@ -966,7 +966,7 @@ class AssertRankInTest(test.TestCase): desired_ranks[1]: [2, 1], }) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_if_rank_is_not_integer_static(self): tensor = constant_op.constant((42, 43), name="my_tensor") with self.assertRaisesRegexp(TypeError, @@ -987,7 +987,7 @@ class AssertRankInTest(test.TestCase): class AssertRankAtLeastTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 1 @@ -1005,7 +1005,7 @@ class AssertRankAtLeastTest(test.TestCase): with self.assertRaisesOpError("my_tensor.*rank"): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_zero_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant(1, name="my_tensor") desired_rank = 0 @@ -1021,7 +1021,7 @@ class AssertRankAtLeastTest(test.TestCase): [check_ops.assert_rank_at_least(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: 0}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_ten_doesnt_raise_raise_if_rank_too_large_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 0 @@ -1037,7 +1037,7 @@ class AssertRankAtLeastTest(test.TestCase): [check_ops.assert_rank_at_least(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_doesnt_raise_if_rank_just_right_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 1 @@ -1053,7 +1053,7 @@ class AssertRankAtLeastTest(test.TestCase): [check_ops.assert_rank_at_least(tensor, desired_rank)]): array_ops.identity(tensor).eval(feed_dict={tensor: [1, 2]}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_rank_one_tensor_raises_if_rank_too_small_static_rank(self): tensor = constant_op.constant([1, 2], name="my_tensor") desired_rank = 2 @@ -1074,7 +1074,7 @@ class AssertRankAtLeastTest(test.TestCase): class AssertNonNegativeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_negative(self): zoe = constant_op.constant([-1, -2], name="zoe") with self.assertRaisesOpError("x >= 0 did not hold"): @@ -1082,14 +1082,14 @@ class AssertNonNegativeTest(test.TestCase): out = array_ops.identity(zoe) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_zero_and_positive(self): lucas = constant_op.constant([0, 2], name="lucas") with ops.control_dependencies([check_ops.assert_non_negative(lucas)]): out = array_ops.identity(lucas) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is non-negative when it satisfies: # For every element x_i in x, x_i >= 0 @@ -1103,14 +1103,14 @@ class AssertNonNegativeTest(test.TestCase): class AssertNonPositiveTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_zero_and_negative(self): tom = constant_op.constant([0, -2], name="tom") with ops.control_dependencies([check_ops.assert_non_positive(tom)]): out = array_ops.identity(tom) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_positive(self): rachel = constant_op.constant([0, 2], name="rachel") with self.assertRaisesOpError("x <= 0 did not hold"): @@ -1118,7 +1118,7 @@ class AssertNonPositiveTest(test.TestCase): out = array_ops.identity(rachel) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_doesnt_raise(self): # A tensor is non-positive when it satisfies: # For every element x_i in x, x_i <= 0 @@ -1132,14 +1132,14 @@ class AssertNonPositiveTest(test.TestCase): class AssertIntegerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_integer(self): integers = constant_op.constant([1, 2], name="integers") with ops.control_dependencies([check_ops.assert_integer(integers)]): out = array_ops.identity(integers) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_float(self): floats = constant_op.constant([1.0, 2.0], name="floats") with self.assertRaisesRegexp(TypeError, "Expected.*integer"): @@ -1148,7 +1148,7 @@ class AssertIntegerTest(test.TestCase): class AssertTypeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_doesnt_raise_when_correct_type(self): integers = constant_op.constant([1, 2], dtype=dtypes.int64) with ops.control_dependencies([ @@ -1156,7 +1156,7 @@ class AssertTypeTest(test.TestCase): out = array_ops.identity(integers) self.evaluate(out) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_raises_when_wrong_type(self): floats = constant_op.constant([1.0, 2.0], dtype=dtypes.float16) with self.assertRaisesRegexp(TypeError, "must be of type.*float32"): @@ -1165,74 +1165,74 @@ class AssertTypeTest(test.TestCase): class IsStrictlyIncreasingTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_constant_tensor_is_not_strictly_increasing(self): self.assertFalse(self.evaluate(check_ops.is_strictly_increasing([1, 1, 1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_decreasing_tensor_is_not_strictly_increasing(self): self.assertFalse(self.evaluate( check_ops.is_strictly_increasing([1, 0, -1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_2d_decreasing_tensor_is_not_strictly_increasing(self): self.assertFalse( self.evaluate(check_ops.is_strictly_increasing([[1, 3], [2, 4]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_tensor_is_increasing(self): self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([1, 2, 3]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_rank_two_tensor(self): self.assertTrue( self.evaluate(check_ops.is_strictly_increasing([[-1, 2], [3, 4]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_tensor_with_one_element_is_strictly_increasing(self): self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_is_strictly_increasing(self): self.assertTrue(self.evaluate(check_ops.is_strictly_increasing([]))) class IsNonDecreasingTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_constant_tensor_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1, 1, 1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_decreasing_tensor_is_not_non_decreasing(self): self.assertFalse(self.evaluate(check_ops.is_non_decreasing([3, 2, 1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_2d_decreasing_tensor_is_not_non_decreasing(self): self.assertFalse(self.evaluate( check_ops.is_non_decreasing([[1, 3], [2, 4]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_rank_one_tensor_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1, 2, 3]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_increasing_rank_two_tensor(self): self.assertTrue(self.evaluate( check_ops.is_non_decreasing([[-1, 2], [3, 3]]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_tensor_with_one_element_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([1]))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_empty_tensor_is_non_decreasing(self): self.assertTrue(self.evaluate(check_ops.is_non_decreasing([]))) class FloatDTypeTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_assert_same_float_dtype(self): self.assertIs(dtypes.float32, check_ops.assert_same_float_dtype(None, None)) @@ -1286,7 +1286,7 @@ class FloatDTypeTest(test.TestCase): class AssertScalarTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_assert_scalar(self): check_ops.assert_scalar(constant_op.constant(3)) check_ops.assert_scalar(constant_op.constant("foo")) diff --git a/tensorflow/python/kernel_tests/confusion_matrix_test.py b/tensorflow/python/kernel_tests/confusion_matrix_test.py index 79e419867d7..ae6875340e7 100644 --- a/tensorflow/python/kernel_tests/confusion_matrix_test.py +++ b/tensorflow/python/kernel_tests/confusion_matrix_test.py @@ -34,7 +34,7 @@ from tensorflow.python.platform import test class ConfusionMatrixTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testExample(self): """This is a test of the example provided in pydoc.""" with self.test_session(): diff --git a/tensorflow/python/kernel_tests/conv_ops_test.py b/tensorflow/python/kernel_tests/conv_ops_test.py index 80ba7dafc9d..474d06b8f3a 100644 --- a/tensorflow/python/kernel_tests/conv_ops_test.py +++ b/tensorflow/python/kernel_tests/conv_ops_test.py @@ -345,7 +345,7 @@ class Conv2DTest(test.TestCase): self.assertAllClose(expected, np.ravel(value), atol=tol, rtol=tol) self.assertShapeEqual(value, conv) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D1x1Filter(self): expected_output = [ 30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, @@ -358,7 +358,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Filter2x1Dilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 4, 4, 1], @@ -367,7 +367,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 1], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmpty(self): expected_output = [] self._VerifyValues( @@ -377,7 +377,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmptyDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[0, 2, 3, 3], @@ -386,7 +386,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 1], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Filter(self): # The outputs are computed using third_party/py/IPython/notebook. expected_output = [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0] @@ -397,7 +397,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 2, 3, 3], @@ -406,7 +406,7 @@ class Conv2DTest(test.TestCase): dilations=[1, 2], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D1x2Filter(self): # The outputs are computed using third_party/py/IPython/notebook. expected_output = [ @@ -420,7 +420,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D1x2FilterDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 2, 3, 3], @@ -429,7 +429,7 @@ class Conv2DTest(test.TestCase): dilations=[2, 1], padding="VALID") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterStride2(self): expected_output = [2271.0, 2367.0, 2463.0] self._VerifyValues( @@ -439,7 +439,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterStride2Same(self): expected_output = [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0] self._VerifyValues( @@ -449,7 +449,7 @@ class Conv2DTest(test.TestCase): padding="SAME", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2FilterStride1x2(self): expected_output = [58.0, 78.0, 98.0, 118.0, 138.0, 158.0] self._VerifyValues( @@ -459,7 +459,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSmallerThanStrideValid(self): expected_output = [65, 95, 275, 305] self._VerifyValues( @@ -469,7 +469,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=expected_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSmallerThanStrideSame(self): self._VerifyValues( tensor_in_sizes=[1, 3, 3, 1], @@ -492,7 +492,7 @@ class Conv2DTest(test.TestCase): padding="SAME", expected=[44, 28, 41, 16]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSize(self): self._VerifyValues( tensor_in_sizes=[1, 2, 2, 1], @@ -501,7 +501,7 @@ class Conv2DTest(test.TestCase): padding="VALID", expected=[50, 60]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSizeDilation(self): self._VerifyDilatedConvValues( tensor_in_sizes=[1, 3, 3, 1], @@ -589,7 +589,7 @@ class Conv2DTest(test.TestCase): for i in range(1, len(values)): self.assertAllClose(values[0], values[i], rtol=1e-2, atol=1e-2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth1ValidBackpropInput(self): expected_output = [1.0, 4.0, 4.0, 3.0, 10.0, 8.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -604,7 +604,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmptyBackpropInput(self): expected_output = [] for (data_format, use_gpu) in GetTestConfigs(): @@ -619,7 +619,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropInput(self): expected_output = [ 14.0, 32.0, 50.0, 100.0, 163.0, 226.0, 167.0, 212.0, 257.0, 122.0, @@ -639,7 +639,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropInputStride1x2(self): expected_output = [ 1.0, 2.0, 2.0, 4.0, 3.0, 6.0, 7.0, 12.0, 11.0, 18.0, 15.0, 24.0, 12.0, @@ -657,7 +657,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DStrideTwoFilterOneSameBackpropInput(self): expected_output = [ 1.0, 0.0, 2.0, 0.0, 0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 4.0, 0.0, 0.0, 0.0, @@ -675,7 +675,7 @@ class Conv2DTest(test.TestCase): use_gpu=use_gpu, err=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSizeBackpropInput(self): expected_output = [5.0, 11.0, 17.0, 23.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -759,7 +759,7 @@ class Conv2DTest(test.TestCase): for i in range(1, len(values)): self.assertAllClose(values[0], values[i], rtol=1e-4, atol=1e-4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth1ValidBackpropFilter(self): expected = [5.0, 8.0, 14.0, 17.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -773,7 +773,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DEmptyBackpropFilter(self): expected = [] for (data_format, use_gpu) in GetTestConfigs(): @@ -787,7 +787,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DBackpropFilterWithEmptyInput(self): expected = [0, 0, 0, 0] for (data_format, use_gpu) in GetTestConfigs(): @@ -801,7 +801,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropFilter(self): expected = [ 17.0, 22.0, 27.0, 22.0, 29.0, 36.0, 27.0, 36.0, 45.0, 32.0, 43.0, 54.0, @@ -820,7 +820,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2D2x2Depth3ValidBackpropFilterStride1x2(self): expected = [161.0, 182.0, 287.0, 308.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -834,7 +834,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DStrideTwoFilterOneSameBackpropFilter(self): expected_output = [78.] for (data_format, use_gpu) in GetTestConfigs(): @@ -848,7 +848,7 @@ class Conv2DTest(test.TestCase): data_format=data_format, use_gpu=use_gpu) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConv2DKernelSizeMatchesInputSizeBackpropFilter(self): expected_output = [1.0, 2.0, 2.0, 4.0, 3.0, 6.0, 4.0, 8.0] for (data_format, use_gpu) in GetTestConfigs(): @@ -1897,19 +1897,19 @@ if __name__ == "__main__": for index, (input_size_, filter_size_, output_size_, stride_, padding_) in enumerate(GetShrunkInceptionShapes()): setattr(Conv2DTest, "testInceptionFwd_" + str(index), - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionFwdTest(input_size_, filter_size_, stride_, padding_))) setattr( Conv2DTest, "testInceptionFwdDilatedConv_" + str(index), - test_util.run_in_graph_and_eager_modes()(GetInceptionFwdDilatedConvTest( + test_util.run_in_graph_and_eager_modes(GetInceptionFwdDilatedConvTest( input_size_, filter_size_, stride_, padding_))) setattr(Conv2DTest, "testInceptionBackInput_" + str(index), - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackInputTest(input_size_, filter_size_, output_size_, stride_, padding_))) setattr(Conv2DTest, "testInceptionBackFilter_" + str(index), - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackFilterTest(input_size_, filter_size_, output_size_, [stride_, stride_], padding_))) @@ -1924,17 +1924,17 @@ if __name__ == "__main__": fshape = [1, 1, 1, 256] oshape = [1, 400, 400, 256] setattr(Conv2DTest, "testInceptionFwd_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionFwdTest(ishape, fshape, 1, "SAME", gpu_only=True))) setattr(Conv2DTest, "testInceptionFwdDilatedConv_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionFwdDilatedConvTest(ishape, fshape, 1, "SAME"))) setattr(Conv2DTest, "testInceptionBackInput_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackInputTest(ishape, fshape, oshape, 1, "SAME", gpu_only=True))) setattr(Conv2DTest, "testInceptionBackFilter_No_Winograd_Nonfused", - test_util.run_in_graph_and_eager_modes()( + test_util.run_in_graph_and_eager_modes( GetInceptionBackFilterTest(ishape, fshape, oshape, [1, 1], "SAME", gpu_only=True))) test.main() diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py index ed5ea8b0349..ab9c0a34d53 100644 --- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py +++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py @@ -58,14 +58,14 @@ def entropy(p): class BernoulliTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testP(self): p = [0.2, 0.4] dist = bernoulli.Bernoulli(probs=p) with self.test_session(): self.assertAllClose(p, self.evaluate(dist.probs)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogits(self): logits = [-42., 42.] dist = bernoulli.Bernoulli(logits=logits) @@ -83,7 +83,7 @@ class BernoulliTest(test.TestCase): with self.test_session(): self.assertAllClose(special.logit(p), self.evaluate(dist.logits)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvalidP(self): invalid_ps = [1.01, 2.] for p in invalid_ps: @@ -105,7 +105,7 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(probs=p) self.assertEqual(p, self.evaluate(dist.probs)) # Should not fail - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShapes(self): with self.test_session(): for batch_shape in ([], [1], [2, 3, 4]): @@ -116,7 +116,7 @@ class BernoulliTest(test.TestCase): self.assertAllEqual([], dist.event_shape.as_list()) self.assertAllEqual([], self.evaluate(dist.event_shape_tensor())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDtype(self): dist = make_bernoulli([]) self.assertEqual(dist.dtype, dtypes.int32) @@ -134,7 +134,7 @@ class BernoulliTest(test.TestCase): self.assertEqual(dist64.dtype, dist64.sample(5).dtype) self.assertEqual(dist64.dtype, dist64.mode().dtype) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def _testPmf(self, **kwargs): dist = bernoulli.Bernoulli(**kwargs) with self.test_session(): @@ -175,7 +175,7 @@ class BernoulliTest(test.TestCase): p: [0.2, 0.3, 0.4] }), [[0.2, 0.7, 0.4]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPmfInvalid(self): p = [0.1, 0.2, 0.7] with self.test_session(): @@ -185,7 +185,7 @@ class BernoulliTest(test.TestCase): with self.assertRaisesOpError("Elements cannot exceed 1."): self.evaluate(dist.prob([2, 0, 1])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPmfWithP(self): p = [[0.2, 0.4], [0.3, 0.6]] self._testPmf(probs=p) @@ -227,21 +227,21 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(probs=[[0.5], [0.5]]) self.assertEqual((2, 1), dist.log_prob(1).get_shape()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBoundaryConditions(self): with self.test_session(): dist = bernoulli.Bernoulli(probs=1.0) self.assertAllClose(np.nan, self.evaluate(dist.log_prob(0))) self.assertAllClose([np.nan], [self.evaluate(dist.log_prob(1))]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEntropyNoBatch(self): p = 0.2 dist = bernoulli.Bernoulli(probs=p) with self.test_session(): self.assertAllClose(self.evaluate(dist.entropy()), entropy(p)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEntropyWithBatch(self): p = [[0.1, 0.7], [0.2, 0.6]] dist = bernoulli.Bernoulli(probs=p, validate_args=False) @@ -251,7 +251,7 @@ class BernoulliTest(test.TestCase): [[entropy(0.1), entropy(0.7)], [entropy(0.2), entropy(0.6)]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSampleN(self): with self.test_session(): p = [0.2, 0.6] @@ -273,7 +273,7 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(np.log([.2, .4])) self.assertAllEqual((1, 2), dist.sample(1, seed=42).get_shape().as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNotReparameterized(self): p = constant_op.constant([0.2, 0.6]) with backprop.GradientTape() as tape: @@ -297,14 +297,14 @@ class BernoulliTest(test.TestCase): feed_dict={n: 1000}) self.assertAllEqual(sample, sample) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMean(self): with self.test_session(): p = np.array([[0.2, 0.7], [0.5, 0.4]], dtype=np.float32) dist = bernoulli.Bernoulli(probs=p) self.assertAllEqual(self.evaluate(dist.mean()), p) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarianceAndStd(self): var = lambda p: p * (1. - p) with self.test_session(): @@ -321,7 +321,7 @@ class BernoulliTest(test.TestCase): [np.sqrt(var(0.5)), np.sqrt(var(0.4))]], dtype=np.float32)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBernoulliBernoulliKL(self): batch_size = 6 a_p = np.array([0.5] * batch_size, dtype=np.float32) diff --git a/tensorflow/python/kernel_tests/distributions/normal_test.py b/tensorflow/python/kernel_tests/distributions/normal_test.py index c7e00ff8d8d..d08685c4d94 100644 --- a/tensorflow/python/kernel_tests/distributions/normal_test.py +++ b/tensorflow/python/kernel_tests/distributions/normal_test.py @@ -78,20 +78,20 @@ class NormalTest(test.TestCase): self.assertEqual(expected, mu_shape) self.assertEqual(expected, sigma_shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testParamShapes(self): sample_shape = [10, 3, 4] self._testParamShapes(sample_shape, sample_shape) self._testParamShapes(constant_op.constant(sample_shape), sample_shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testParamStaticShapes(self): sample_shape = [10, 3, 4] self._testParamStaticShapes(sample_shape, sample_shape) self._testParamStaticShapes( tensor_shape.TensorShape(sample_shape), sample_shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalWithSoftplusScale(self): with self.test_session(): mu = array_ops.zeros((10, 3)) @@ -101,7 +101,7 @@ class NormalTest(test.TestCase): self.assertAllEqual( self.evaluate(nn_ops.softplus(rho)), self.evaluate(normal.scale)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogPDF(self): with self.test_session(): batch_size = 6 @@ -135,7 +135,7 @@ class NormalTest(test.TestCase): self.assertAllClose(expected_log_pdf, self.evaluate(log_pdf)) self.assertAllClose(np.exp(expected_log_pdf), self.evaluate(pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogPDFMultidimensional(self): with self.test_session(): batch_size = 6 @@ -173,7 +173,7 @@ class NormalTest(test.TestCase): self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalCDF(self): with self.test_session(): batch_size = 50 @@ -195,7 +195,7 @@ class NormalTest(test.TestCase): expected_cdf = stats.norm(mu, sigma).cdf(x) self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalSurvivalFunction(self): with self.test_session(): batch_size = 50 @@ -218,7 +218,7 @@ class NormalTest(test.TestCase): expected_sf = stats.norm(mu, sigma).sf(x) self.assertAllClose(expected_sf, self.evaluate(sf), atol=0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogCDF(self): with self.test_session(): batch_size = 50 @@ -262,7 +262,7 @@ class NormalTest(test.TestCase): self.assertAllFinite(grads[0]) self.assertAllFinite(grads[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalLogSurvivalFunction(self): with self.test_session(): batch_size = 50 @@ -286,7 +286,7 @@ class NormalTest(test.TestCase): expected_sf = stats.norm(mu, sigma).logsf(x) self.assertAllClose(expected_sf, self.evaluate(sf), atol=0, rtol=1e-5) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalEntropyWithScalarInputs(self): # Scipy.stats.norm cannot deal with the shapes in the other test. with self.test_session(): @@ -308,7 +308,7 @@ class NormalTest(test.TestCase): expected_entropy = stats.norm(mu_v, sigma_v).entropy() self.assertAllClose(expected_entropy, self.evaluate(entropy)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalEntropy(self): with self.test_session(): mu_v = np.array([1.0, 1.0, 1.0]) @@ -329,7 +329,7 @@ class NormalTest(test.TestCase): self.assertAllEqual(normal.batch_shape, entropy.get_shape()) self.assertAllEqual(normal.batch_shape, self.evaluate(entropy).shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalMeanAndMode(self): with self.test_session(): # Mu will be broadcast to [7, 7, 7]. @@ -344,7 +344,7 @@ class NormalTest(test.TestCase): self.assertAllEqual((3,), normal.mode().get_shape()) self.assertAllEqual([7., 7, 7], self.evaluate(normal.mode())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalQuantile(self): with self.test_session(): batch_size = 52 @@ -396,7 +396,7 @@ class NormalTest(test.TestCase): def testQuantileFiniteGradientAtDifficultPointsFloat64(self): self._baseQuantileFiniteGradientAtDifficultPoints(np.float64) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalVariance(self): with self.test_session(): # sigma will be broadcast to [7, 7, 7] @@ -408,7 +408,7 @@ class NormalTest(test.TestCase): self.assertAllEqual((3,), normal.variance().get_shape()) self.assertAllEqual([49., 49, 49], self.evaluate(normal.variance())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalStandardDeviation(self): with self.test_session(): # sigma will be broadcast to [7, 7, 7] @@ -420,7 +420,7 @@ class NormalTest(test.TestCase): self.assertAllEqual((3,), normal.stddev().get_shape()) self.assertAllEqual([7., 7, 7], self.evaluate(normal.stddev())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalSample(self): with self.test_session(): mu = constant_op.constant(3.0) @@ -466,7 +466,7 @@ class NormalTest(test.TestCase): self.assertIsNotNone(grad_mu) self.assertIsNotNone(grad_sigma) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalSampleMultiDimensional(self): with self.test_session(): batch_size = 2 @@ -502,7 +502,7 @@ class NormalTest(test.TestCase): self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNegativeSigmaFails(self): with self.test_session(): with self.assertRaisesOpError("Condition x > 0 did not hold"): @@ -510,7 +510,7 @@ class NormalTest(test.TestCase): loc=[1.], scale=[-5.], validate_args=True, name="G") self.evaluate(normal.mean()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalShape(self): with self.test_session(): mu = constant_op.constant([-3.0] * 5) @@ -537,7 +537,7 @@ class NormalTest(test.TestCase): feed_dict={mu: 5.0, sigma: [1.0, 2.0]}), [2]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNormalNormalKL(self): batch_size = 6 mu_a = np.array([3.0] * batch_size) diff --git a/tensorflow/python/kernel_tests/distributions/special_math_test.py b/tensorflow/python/kernel_tests/distributions/special_math_test.py index 4565bf5c466..a634194ce52 100644 --- a/tensorflow/python/kernel_tests/distributions/special_math_test.py +++ b/tensorflow/python/kernel_tests/distributions/special_math_test.py @@ -89,7 +89,7 @@ class NdtriTest(test.TestCase): all_true = np.ones_like(is_finite, dtype=np.bool) self.assertAllEqual(all_true, is_finite) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNdtri(self): """Verifies that ndtri computation is correct.""" with self.test_session(): @@ -138,11 +138,11 @@ class NdtriTest(test.TestCase): lambda x: special_math.ndtri(x), p) # pylint: disable=unnecessary-lambda self.assertAllFinite(self.evaluate(grads[0])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNdtriFiniteGradientFloat32(self): self._baseNdtriFiniteGradientTest(np.float32) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNdtriFiniteGradientFloat64(self): self._baseNdtriFiniteGradientTest(np.float64) diff --git a/tensorflow/python/kernel_tests/distributions/uniform_test.py b/tensorflow/python/kernel_tests/distributions/uniform_test.py index 978fff1cc1f..140ec569dd8 100644 --- a/tensorflow/python/kernel_tests/distributions/uniform_test.py +++ b/tensorflow/python/kernel_tests/distributions/uniform_test.py @@ -48,7 +48,7 @@ stats = try_import("scipy.stats") class UniformTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformRange(self): with self.test_session(): a = 3.0 @@ -58,7 +58,7 @@ class UniformTest(test.TestCase): self.assertAllClose(b, self.evaluate(uniform.high)) self.assertAllClose(b - a, self.evaluate(uniform.range())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformPDF(self): with self.test_session(): a = constant_op.constant([-3.0] * 5 + [15.0]) @@ -84,7 +84,7 @@ class UniformTest(test.TestCase): log_pdf = uniform.log_prob(x) self.assertAllClose(np.log(expected_pdf), self.evaluate(log_pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformShape(self): with self.test_session(): a = constant_op.constant([-3.0] * 5) @@ -96,7 +96,7 @@ class UniformTest(test.TestCase): self.assertAllEqual(self.evaluate(uniform.event_shape_tensor()), []) self.assertEqual(uniform.event_shape, tensor_shape.TensorShape([])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformPDFWithScalarEndpoint(self): with self.test_session(): a = constant_op.constant([0.0, 5.0]) @@ -109,7 +109,7 @@ class UniformTest(test.TestCase): pdf = uniform.prob(x) self.assertAllClose(expected_pdf, self.evaluate(pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformCDF(self): with self.test_session(): batch_size = 6 @@ -133,7 +133,7 @@ class UniformTest(test.TestCase): log_cdf = uniform.log_cdf(x) self.assertAllClose(np.log(_expected_cdf()), self.evaluate(log_cdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformEntropy(self): with self.test_session(): a_v = np.array([1.0, 1.0, 1.0]) @@ -143,7 +143,7 @@ class UniformTest(test.TestCase): expected_entropy = np.log(b_v - a_v) self.assertAllClose(expected_entropy, self.evaluate(uniform.entropy())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformAssertMaxGtMin(self): with self.test_session(): a_v = np.array([1.0, 1.0, 1.0], dtype=np.float32) @@ -154,7 +154,7 @@ class UniformTest(test.TestCase): uniform = uniform_lib.Uniform(low=a_v, high=b_v, validate_args=True) self.evaluate(uniform.low) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformSample(self): with self.test_session(): a = constant_op.constant([3.0, 4.0]) @@ -177,7 +177,7 @@ class UniformTest(test.TestCase): self.assertFalse( np.any(sample_values[::, 1] < a2_v) or np.any(sample_values >= b_v)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def _testUniformSampleMultiDimensional(self): # DISABLED: Please enable this test once b/issues/30149644 is resolved. with self.test_session(): @@ -208,7 +208,7 @@ class UniformTest(test.TestCase): self.assertAllClose( sample_values[:, 0, 1].mean(), (a_v[1] + b_v[1]) / 2, atol=1e-2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformMean(self): with self.test_session(): a = 10.0 @@ -219,7 +219,7 @@ class UniformTest(test.TestCase): s_uniform = stats.uniform(loc=a, scale=b - a) self.assertAllClose(self.evaluate(uniform.mean()), s_uniform.mean()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformVariance(self): with self.test_session(): a = 10.0 @@ -230,7 +230,7 @@ class UniformTest(test.TestCase): s_uniform = stats.uniform(loc=a, scale=b - a) self.assertAllClose(self.evaluate(uniform.variance()), s_uniform.var()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformStd(self): with self.test_session(): a = 10.0 @@ -241,7 +241,7 @@ class UniformTest(test.TestCase): s_uniform = stats.uniform(loc=a, scale=b - a) self.assertAllClose(self.evaluate(uniform.stddev()), s_uniform.std()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformNans(self): with self.test_session(): a = 10.0 @@ -259,7 +259,7 @@ class UniformTest(test.TestCase): self.assertFalse(is_nan[0]) self.assertTrue(is_nan[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformSamplePdf(self): with self.test_session(): a = 10.0 @@ -269,7 +269,7 @@ class UniformTest(test.TestCase): self.evaluate( math_ops.reduce_all(uniform.prob(uniform.sample(10)) > 0))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformBroadcasting(self): with self.test_session(): a = 10.0 @@ -280,7 +280,7 @@ class UniformTest(test.TestCase): expected_pdf = np.array([[1.0, 0.1], [0.0, 0.1], [1.0, 0.0]]) self.assertAllClose(expected_pdf, self.evaluate(pdf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUniformSampleWithShape(self): with self.test_session(): a = 10.0 diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py index 08fb21e9760..9d38ffcb4a9 100644 --- a/tensorflow/python/kernel_tests/distributions/util_test.py +++ b/tensorflow/python/kernel_tests/distributions/util_test.py @@ -91,21 +91,21 @@ class AssertCloseTest(test.TestCase): class MaybeGetStaticTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetStaticInt(self): x = 2 self.assertEqual(x, du.maybe_get_static_value(x)) self.assertAllClose( np.array(2.), du.maybe_get_static_value(x, dtype=np.float64)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetStaticNumpyArray(self): x = np.array(2, dtype=np.int32) self.assertEqual(x, du.maybe_get_static_value(x)) self.assertAllClose( np.array(2.), du.maybe_get_static_value(x, dtype=np.float64)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetStaticConstant(self): x = constant_op.constant(2, dtype=dtypes.int32) self.assertEqual(np.array(2, dtype=np.int32), du.maybe_get_static_value(x)) @@ -120,7 +120,7 @@ class MaybeGetStaticTest(test.TestCase): class GetLogitsAndProbsTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testImproperArguments(self): with self.test_session(): with self.assertRaises(ValueError): @@ -129,7 +129,7 @@ class GetLogitsAndProbsTest(test.TestCase): with self.assertRaises(ValueError): du.get_logits_and_probs(logits=[0.1], probs=[0.1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogits(self): p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32) logits = _logit(p) @@ -141,7 +141,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(p, self.evaluate(new_p), rtol=1e-5, atol=0.) self.assertAllClose(logits, self.evaluate(new_logits), rtol=1e-5, atol=0.) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogitsMultidimensional(self): p = np.array([0.2, 0.3, 0.5], dtype=np.float32) logits = np.log(p) @@ -153,7 +153,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(self.evaluate(new_p), p) self.assertAllClose(self.evaluate(new_logits), logits) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbability(self): p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32) @@ -164,7 +164,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(_logit(p), self.evaluate(new_logits)) self.assertAllClose(p, self.evaluate(new_p)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbabilityMultidimensional(self): p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32) @@ -175,7 +175,7 @@ class GetLogitsAndProbsTest(test.TestCase): self.assertAllClose(np.log(p), self.evaluate(new_logits)) self.assertAllClose(p, self.evaluate(new_p)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbabilityValidateArgs(self): p = [0.01, 0.2, 0.5, 0.7, .99] # Component less than 0. @@ -206,7 +206,7 @@ class GetLogitsAndProbsTest(test.TestCase): probs=p3, validate_args=False) self.evaluate(prob) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testProbabilityValidateArgsMultidimensional(self): p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32) # Component less than 0. Still sums to 1. @@ -308,7 +308,7 @@ class EmbedCheckCategoricalEventShapeTest(test.TestCase): param) checked_param.eval(feed_dict={param: np.ones([int(2**11+1)])}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUnsupportedDtype(self): with self.test_session(): with self.assertRaises(TypeError): @@ -493,7 +493,7 @@ class RotateTransposeTest(test.TestCase): x = np.array(x) return np.transpose(x, np.roll(np.arange(len(x.shape)), shift)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRollStatic(self): with self.test_session(): if context.executing_eagerly(): diff --git a/tensorflow/python/kernel_tests/fifo_queue_test.py b/tensorflow/python/kernel_tests/fifo_queue_test.py index 14a336c6881..9e7b5283381 100644 --- a/tensorflow/python/kernel_tests/fifo_queue_test.py +++ b/tensorflow/python/kernel_tests/fifo_queue_test.py @@ -126,14 +126,14 @@ class FIFOQueueTest(test.TestCase): q.enqueue_many([[1, 2, 3, 4], [[1, 1], [2, 2], [3, 3], [4, 4]]]).run() self.assertEqual(4, q.size().eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMultipleDequeues(self): q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) self.evaluate(q.enqueue_many([[1, 2, 3]])) a, b, c = self.evaluate([q.dequeue(), q.dequeue(), q.dequeue()]) self.assertAllEqual(set([1, 2, 3]), set([a, b, c])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testQueuesDontShare(self): q = data_flow_ops.FIFOQueue(10, [dtypes_lib.int32], shapes=[()]) self.evaluate(q.enqueue(1)) diff --git a/tensorflow/python/kernel_tests/functional_ops_test.py b/tensorflow/python/kernel_tests/functional_ops_test.py index facadc971ff..1beb0e396e6 100644 --- a/tensorflow/python/kernel_tests/functional_ops_test.py +++ b/tensorflow/python/kernel_tests/functional_ops_test.py @@ -56,7 +56,7 @@ def simple_scoped_fn(a, x): class FunctionalOpsTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldl_Simple(self): with self.test_session(): elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -72,7 +72,7 @@ class FunctionalOpsTest(test.TestCase): initializer=10) self.assertAllEqual(880, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldl_SingleInputMultiOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -83,7 +83,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(22, r_value[0]) self.assertAllEqual(20, r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldl_MultiInputSingleOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -111,7 +111,7 @@ class FunctionalOpsTest(test.TestCase): self.assertEqual(len(variables.trainable_variables()), 1) self.assertAllEqual(880, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldr_Simple(self): with self.test_session(): elems = constant_op.constant([1, 2, 3, 4, 5, 6], name="data") @@ -127,7 +127,7 @@ class FunctionalOpsTest(test.TestCase): initializer=10) self.assertAllEqual(1282, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldr_SingleInputMultiOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -138,7 +138,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(22, r_value[0]) self.assertAllEqual(20, r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldr_MultiInputSingleOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -182,7 +182,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(720.0, self.evaluate(r)) # pylint: enable=unnecessary-lambda - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_Simple(self): with self.test_session(): nums = [1, 2, 3, 4, 5, 6] @@ -202,7 +202,7 @@ class FunctionalOpsTest(test.TestCase): values=constant_op.constant([0, 1, 2]), dense_shape=[2, 2])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMapOverScalarErrors(self): with self.assertRaisesRegexp(ValueError, "not scalars"): functional_ops.map_fn(lambda x: x, [1, 2]) @@ -251,7 +251,7 @@ class FunctionalOpsTest(test.TestCase): r = gradients_impl.gradients(y, elems)[0] self.assertAllEqual([4.0, 8.0, 12.0, 16.0, 20.0, 24.0], self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_SimpleNotTensor(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -260,7 +260,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual( np.array([(x + 3) * 2 for x in nums]), self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_SingleInputMultiOutput(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -275,7 +275,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual((nums + 3) * 2, received[0]) self.assertAllEqual(-(nums + 3) * 2, received[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_MultiOutputMismatchedDtype(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -287,7 +287,7 @@ class FunctionalOpsTest(test.TestCase): nums, dtype=[dtypes.int64, dtypes.int64]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_MultiInputSingleOutput(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -298,7 +298,7 @@ class FunctionalOpsTest(test.TestCase): received = self.evaluate(r) self.assertAllEqual(nums * nums + (-nums), received) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMap_MultiInputSameStructureOutput(self): with self.test_session(): nums = np.array([1, 2, 3, 4, 5, 6]) @@ -313,7 +313,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(-nums, received[1]) self.assertAllEqual(nums, received[2]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_Simple(self): with self.test_session(): elems = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name="data") @@ -328,7 +328,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual([2., 4., 12., 48., 240., 1440.], self.evaluate(r)) # pylint: enable=unnecessary-lambda - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_Reverse(self): with self.test_session(): elems = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], name="data") @@ -345,7 +345,7 @@ class FunctionalOpsTest(test.TestCase): self.evaluate(r)) # pylint: enable=unnecessary-lambda - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_SingleInputMultiOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -357,7 +357,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual([1.0, 2.0, 6.0, 24.0, 120.0, 720.0], r_value[0]) self.assertAllEqual([1.0, -2.0, 6.0, -24.0, 120.0, -720.0], r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_MultiInputSingleOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -367,7 +367,7 @@ class FunctionalOpsTest(test.TestCase): (elems + 1, -elems), initializer) self.assertAllEqual([1.0, 1.0, 1.0, 1.0, 1.0, 1.0], self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_MultiInputSameTypeOutput(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -377,7 +377,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual(np.cumsum(elems), r_value[0]) self.assertAllEqual(np.cumsum(-elems), r_value[1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScan_MultiOutputMismatchedInitializer(self): with self.test_session(): elems = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0]) @@ -408,7 +408,7 @@ class FunctionalOpsTest(test.TestCase): results = np.array([6, 16, 38, 84, 178, 368]) self.assertAllEqual(results, self.evaluate(r)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScanFoldl_Nested(self): with self.test_session(): elems = constant_op.constant([1.0, 2.0, 3.0, 4.0], name="data") @@ -467,7 +467,7 @@ class FunctionalOpsTest(test.TestCase): variables.global_variables_initializer().run() sess.run(grad) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFoldShape(self): with self.test_session(): x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) @@ -479,7 +479,7 @@ class FunctionalOpsTest(test.TestCase): y = functional_ops.foldl(fn, x, initializer=initializer) self.assertAllEqual(y.get_shape(), self.evaluate(y).shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMapShape(self): with self.test_session(): x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) @@ -491,7 +491,7 @@ class FunctionalOpsTest(test.TestCase): y = functional_ops.map_fn(lambda e: e, x) self.assertIs(None, y.get_shape().dims) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMapEmptyScalar(self): with self.test_session(): map_return = functional_ops.map_fn(lambda x: 1, constant_op.constant([])) @@ -507,7 +507,7 @@ class FunctionalOpsTest(test.TestCase): self.assertAllEqual([0, 3, 2], map_return.get_shape().dims) self.assertAllEqual([0, 3, 2], self.evaluate(map_return).shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScanShape(self): with self.test_session(): x = constant_op.constant([[1, 2, 3], [4, 5, 6]]) diff --git a/tensorflow/python/kernel_tests/list_ops_test.py b/tensorflow/python/kernel_tests/list_ops_test.py index 49855200c24..bf82e08551e 100644 --- a/tensorflow/python/kernel_tests/list_ops_test.py +++ b/tensorflow/python/kernel_tests/list_ops_test.py @@ -46,7 +46,7 @@ def scalar_shape(): @test_util.with_c_shapes class ListOpsTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushPop(self): l = list_ops.empty_tensor_list(element_dtype=dtypes.float32, element_shape=scalar_shape()) @@ -54,14 +54,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 1.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushPopGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testPushPop() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStack(self): l = list_ops.empty_tensor_list(element_dtype=dtypes.float32, element_shape=scalar_shape()) @@ -70,14 +70,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(t), [1.0, 2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStackGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testStack() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorListFromTensor(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape()) @@ -87,14 +87,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(self.evaluate(e), 1.0) self.assertAllEqual(self.evaluate(list_ops.tensor_list_length(l)), 0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testFromTensorGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testTensorListFromTensor() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetSetItem(self): t = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(t, element_shape=scalar_shape()) @@ -104,14 +104,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): t = list_ops.tensor_list_stack(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(t), [3.0, 2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetSetGPU(self): if not context.num_gpus(): return with context.device("gpu:0"): self.testGetSetItem() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUnknownShape(self): l = list_ops.empty_tensor_list( element_dtype=dtypes.float32, element_shape=-1) @@ -122,7 +122,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): l, e = list_ops.tensor_list_pop_back(l, element_dtype=dtypes.float32) self.assertAllEqual(self.evaluate(e), 1.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCPUGPUCopy(self): if not context.num_gpus(): return @@ -140,7 +140,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): list_ops.tensor_list_pop_back( l_cpu, element_dtype=dtypes.float32)[1]), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStack(self): with context.graph_mode(), self.test_session(): tl = list_ops.empty_tensor_list( @@ -152,7 +152,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): list_ops.tensor_list_stack(tl, element_dtype=dtypes.int32)), [[1]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStackInLoop(self): with context.graph_mode(), self.test_session(): t1 = list_ops.empty_tensor_list( @@ -170,7 +170,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): s1 = list_ops.tensor_list_stack(t1, element_dtype=dtypes.int32) self.assertAllEqual(self.evaluate(s1), [0, 1, 2, 3]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStackSwitchDtype(self): with context.graph_mode(), self.test_session(): list_ = list_ops.empty_tensor_list( @@ -192,7 +192,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): np_s1 = np.array([[1, 2, 3], [1, 2, 3]], dtype=np.float32) self.assertAllEqual(self.evaluate(s1), np_s1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGraphStackInLoopSwitchDtype(self): with context.graph_mode(), self.test_session(): t1 = list_ops.empty_tensor_list( @@ -216,7 +216,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): np_s1 = np.vstack([np.arange(1, 4) * i for i in range(4)]) self.assertAllEqual(self.evaluate(s1), np_s1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSerialize(self): # pylint: disable=g-import-not-at-top try: @@ -248,7 +248,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): worker_e = array_ops.identity(e) self.assertAllEqual(self.evaluate(worker_e), [2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushPopGradients(self): with backprop.GradientTape() as tape: l = list_ops.empty_tensor_list(element_dtype=dtypes.float32, @@ -260,7 +260,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): e = 2 * e self.assertAllEqual(self.evaluate(tape.gradient(e, [c])[0]), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStackFromTensorGradients(self): with backprop.GradientTape() as tape: c = constant_op.constant([1.0, 2.0]) @@ -272,7 +272,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): grad = tape.gradient(result, [c])[0] self.assertAllEqual(self.evaluate(grad), [2.0, 2.0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetSetGradients(self): with backprop.GradientTape() as tape: c = constant_op.constant([1.0, 2.0]) @@ -288,14 +288,14 @@ class ListOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(self.evaluate(grad_c), [0.0, 4.0]) self.assertAllEqual(self.evaluate(grad_c2), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSetOutOfBounds(self): c = constant_op.constant([1.0, 2.0]) l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) with self.assertRaises(errors.InvalidArgumentError): self.evaluate(list_ops.tensor_list_set_item(l, 20, 3.0)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResourceVariableScatterGather(self): c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32) l = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) @@ -319,7 +319,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): [[1.0, 2.0]] * 4) self.assertAllEqual(self.evaluate(updated_v_stacked), expected) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConcat(self): c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32) l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) @@ -379,7 +379,7 @@ class ListOpsTest(test_util.TensorFlowTestCase): list_ops.tensor_list_concat_lists(l_batch_0, l_batch_of_int_tls, element_dtype=dtypes.float32)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPushBackBatch(self): c = constant_op.constant([1.0, 2.0], dtype=dtypes.float32) l0 = list_ops.tensor_list_from_tensor(c, element_shape=scalar_shape()) diff --git a/tensorflow/python/kernel_tests/logging_ops_test.py b/tensorflow/python/kernel_tests/logging_ops_test.py index 28c85fa13ad..e635a71c784 100644 --- a/tensorflow/python/kernel_tests/logging_ops_test.py +++ b/tensorflow/python/kernel_tests/logging_ops_test.py @@ -59,7 +59,7 @@ class LoggingOpsTest(test.TestCase): class PrintGradientTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPrintShape(self): inp = constant_op.constant(2.0, shape=[100, 32]) inp_printed = logging_ops.Print(inp, [inp]) diff --git a/tensorflow/python/kernel_tests/py_func_test.py b/tensorflow/python/kernel_tests/py_func_test.py index 253e43920ba..50154a45a8b 100644 --- a/tensorflow/python/kernel_tests/py_func_test.py +++ b/tensorflow/python/kernel_tests/py_func_test.py @@ -460,7 +460,7 @@ class PyFuncTest(test.TestCase): self.assertEqual(initial_size, script_ops._py_funcs.size()) # ----- Tests for eager_py_func ----- - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerSingleOutputInt32(self): a = array_ops.ones((3, 3), dtype=dtypes.int32) x = array_ops.ones((3, 1), dtype=dtypes.int32) @@ -468,7 +468,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(output) self.assertAllEqual(ret, [[3], [3], [3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerSingleOutputFloat32(self): with test_util.device(use_gpu=True): a = array_ops.ones((3, 3), dtype=dtypes.float32) @@ -477,7 +477,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(output) self.assertAllClose(ret, [[3.0], [3.0], [3.0]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerArrayOutput(self): with test_util.device(use_gpu=True): a = array_ops.ones((3, 3), dtype=dtypes.float32) @@ -487,7 +487,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(output) self.assertAllEqual(ret, [[[3.0], [3.0], [3.0]]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerReturnNone(self): with test_util.device(use_gpu=True): def no_return_value(): @@ -500,7 +500,7 @@ class PyFuncTest(test.TestCase): else: self.assertIsNone(ret) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerPyFuncInDefun(self): with test_util.device(use_gpu=True): def wrapper(): @@ -512,7 +512,7 @@ class PyFuncTest(test.TestCase): ret = self.evaluate(wrapped()) self.assertAllEqual(ret, [[3.0], [3.0], [3.0]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerExceptionHandling(self): with test_util.device(use_gpu=True): self._testExceptionHandling( @@ -531,7 +531,7 @@ class PyFuncTest(test.TestCase): self._testExceptionHandling(WeirdError, errors.UnknownError, eager=True) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerReturningVariableRaisesError(self): def return_variable(): return resource_variable_ops.ResourceVariable(0.0) @@ -542,7 +542,7 @@ class PyFuncTest(test.TestCase): return_variable, inp=[], Tout=dtypes.float32) self.evaluate(output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerGradientTape(self): def f(x): @@ -565,7 +565,7 @@ class PyFuncTest(test.TestCase): dy_dx = gradients_impl.gradients(y, x)[0] self.assertEqual(self.evaluate(dy_dx), 6.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerGradientTapeMultipleArgs(self): def f(x, y): diff --git a/tensorflow/python/kernel_tests/random/multinomial_op_test.py b/tensorflow/python/kernel_tests/random/multinomial_op_test.py index 051c7d86bf2..bd64d61af8e 100644 --- a/tensorflow/python/kernel_tests/random/multinomial_op_test.py +++ b/tensorflow/python/kernel_tests/random/multinomial_op_test.py @@ -54,7 +54,7 @@ native_sampler = random_ops.multinomial class MultinomialTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSmallEntropy(self): random_seed.set_random_seed(1618) for output_dtype in [np.int32, np.int64]: diff --git a/tensorflow/python/kernel_tests/resource_variable_ops_test.py b/tensorflow/python/kernel_tests/resource_variable_ops_test.py index 5267eabf0e4..0fb0b8895cb 100644 --- a/tensorflow/python/kernel_tests/resource_variable_ops_test.py +++ b/tensorflow/python/kernel_tests/resource_variable_ops_test.py @@ -145,7 +145,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertIn("", str(handle)) self.assertIn("", repr(handle)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDtypeSurvivesIdentity(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) id_handle = array_ops.identity(handle) @@ -156,7 +156,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): v = resource_variable_ops.ResourceVariable(1.0) self.assertNotEqual(v.name, v.assign_add(1.0).name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCreateRead(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) self.evaluate(resource_variable_ops.assign_variable_op( @@ -165,7 +165,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)) self.assertAllEqual(1, value) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testManyAssigns(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) create = resource_variable_ops.assign_variable_op( @@ -183,7 +183,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEqual(f, 1) self.assertEqual(s, 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignAdd(self): handle = resource_variable_ops.var_handle_op(dtype=dtypes.int32, shape=[]) self.evaluate(resource_variable_ops.assign_variable_op( @@ -194,7 +194,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32)) self.assertEqual(read, 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterAdd(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -207,7 +207,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterSub(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -220,7 +220,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[-1]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMul(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -233,7 +233,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[5]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterDiv(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -246,7 +246,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[2]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMin(self): with ops.device("cpu:0"): handle = resource_variable_ops.var_handle_op( @@ -283,7 +283,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): meta_graph_two = saver.export_meta_graph(graph=graph) self.assertEqual(meta_graph_def, meta_graph_two) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMax(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -296,7 +296,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[6]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterAddScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -309,7 +309,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterSubScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -322,7 +322,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[-1]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMulScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -335,7 +335,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[5]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterDivScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -348,7 +348,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[2]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMinScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -361,7 +361,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): read = resource_variable_ops.read_variable_op(handle, dtype=dtypes.int32) self.assertEqual(self.evaluate(read), [[3]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterMaxScalar(self): handle = resource_variable_ops.var_handle_op( dtype=dtypes.int32, shape=[1, 1]) @@ -426,7 +426,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): state_ops.scatter_update(ref, indices, updates) self.assertAllEqual(ref.read_value(), [True, True, True]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConstraintArg(self): constraint = lambda x: x v = resource_variable_ops.ResourceVariable( @@ -466,32 +466,32 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): with self.assertRaises(errors.OutOfRangeError): state_ops.count_up_to(v, 1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFnDtype(self): v = resource_variable_ops.ResourceVariable( initial_value=lambda: 1, dtype=dtypes.float32, name="var0") self.assertEqual(dtypes.float32, v.value().dtype) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFnNoDtype(self): v = resource_variable_ops.ResourceVariable(initial_value=lambda: 1, name="var2") self.assertEqual(dtypes.int32, v.value().dtype) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitializeAllVariables(self): v = resource_variable_ops.ResourceVariable(1, dtype=dtypes.float32, name="var0") self.evaluate(variables.global_variables_initializer()) self.assertEqual(1.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOperatorOverload(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) self.assertEqual(2.0, self.evaluate(v + v)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignMethod(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -509,7 +509,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(assign_without_read) self.assertEqual(4.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoad(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -561,7 +561,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): variable_def=trainable_variable.to_proto()) .trainable) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSparseRead(self): with self.test_session(): init_value = np.reshape(np.arange(np.power(4, 3)), (4, 4, 4)) @@ -583,7 +583,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.assertEquals(v._handle, w._handle) self.assertEquals(v._graph_element, w._graph_element) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignAddMethod(self): v = resource_variable_ops.ResourceVariable(1.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -601,7 +601,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(assign_without_read) self.assertEqual(4.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAssignSubMethod(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -619,7 +619,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): self.evaluate(assign_without_read) self.assertEqual(0.0, self.evaluate(v.value())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDestroyResource(self): v = resource_variable_ops.ResourceVariable(3.0, name="var0") self.evaluate(variables.global_variables_initializer()) @@ -708,7 +708,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): w_read = resource_variable_ops.read_variable_op(w, v.dtype.base_dtype) self.assertEqual(300.0, self.evaluate(w_read)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testShape(self): v = resource_variable_ops.ResourceVariable( name="var4", initial_value=array_ops.ones(shape=[10, 20, 35])) @@ -842,7 +842,7 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase): state_ops.scatter_update(v, [1], [3]) self.assertAllEqual([1.0, 3.0], v.numpy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScatterUpdateInvalidArgs(self): v = resource_variable_ops.ResourceVariable([0, 1, 2, 3], name="update") # The exact error and message differ between graph construction (where the diff --git a/tensorflow/python/kernel_tests/rnn_test.py b/tensorflow/python/kernel_tests/rnn_test.py index e9ae105c282..957baf8c608 100644 --- a/tensorflow/python/kernel_tests/rnn_test.py +++ b/tensorflow/python/kernel_tests/rnn_test.py @@ -127,7 +127,7 @@ class RNNTest(test.TestCase): self._seed = 23489 np.random.seed(self._seed) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvalidSequenceLengthShape(self): cell = Plus1RNNCell() if context.executing_eagerly(): @@ -141,7 +141,7 @@ class RNNTest(test.TestCase): dtype=dtypes.float32, sequence_length=[[4]]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBatchSizeFromInput(self): cell = Plus1RNNCell() in_eager_mode = context.executing_eagerly() @@ -181,7 +181,7 @@ class RNNTest(test.TestCase): self.assertEqual(None, outputs.shape[0].value) self.assertEqual(None, state.shape[0].value) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScalarStateIsAccepted(self): cell = ScalarStateRNNCell() in_eager_mode = context.executing_eagerly() @@ -201,7 +201,7 @@ class RNNTest(test.TestCase): self.assertAllEqual([[[1], [2], [3], [4]]], outputs) self.assertAllEqual(4, state) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testUnbalancedOutputIsAccepted(self): cell = UnbalancedOutputRNNCell() in_eager_mode = context.executing_eagerly() @@ -223,7 +223,7 @@ class RNNTest(test.TestCase): self.assertAllEqual([[[1, 1], [2, 2], [3, 3], [4, 4]]], outputs[1]) self.assertAllEqual(4, state) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayStateIsAccepted(self): cell = TensorArrayStateRNNCell() in_eager_mode = context.executing_eagerly() @@ -256,7 +256,7 @@ class RNNTest(test.TestCase): cell_output, _ = cell(array_ops.zeros(in_shape, dtype), state_output) self.assertAllEqual([batch_size, out_size], cell_output.shape.as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCellsBuild(self): f32 = dtypes.float32 f64 = dtypes.float64 diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index faa4b49a8d7..f9b9c77bbf7 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -369,7 +369,7 @@ class ScatterNdTest(test.TestCase): del input_ # input_ is not used in scatter_nd return array_ops.scatter_nd(indices, updates, shape) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInvalidShape(self): # TODO(apassos) figure out how to unify these errors with self.assertRaises(errors.InvalidArgumentError diff --git a/tensorflow/python/kernel_tests/split_op_test.py b/tensorflow/python/kernel_tests/split_op_test.py index 8cfee3eb933..419cd5ecdaf 100644 --- a/tensorflow/python/kernel_tests/split_op_test.py +++ b/tensorflow/python/kernel_tests/split_op_test.py @@ -95,7 +95,7 @@ class SplitOpTest(test.TestCase): sess.run(array_ops.split(value, size_splits), {size_splits: [2, 2, 6]}) self.assertTrue("Cannot infer num from shape" in str(context.exception)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testExplicitNum(self): size_splits = array_ops.constant([2, 2, 6], dtype=dtypes.int32) value = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] @@ -109,7 +109,7 @@ class SplitOpTest(test.TestCase): self.assertAllEqual(r[1], value[2:4]) self.assertAllEqual(r[2], value[4:]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testListOfScalarTensors(self): a = math_ops.to_int32(5) b = math_ops.to_int32(6) @@ -168,7 +168,7 @@ class SplitOpTest(test.TestCase): offset += size_splits[i] self.assertAllEqual(result[i], inp[slices]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSpecialCasesVariable(self): self._testSpecialCasesVariable() for dtype in _TEST_DTYPES: @@ -210,13 +210,13 @@ class SplitOpTest(test.TestCase): self.assertAllEqual(np_ans[i], out[i]) self.assertShapeEqual(np_ans[i], tf_ans[i]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitRows(self): for dtype in _TEST_DTYPES: inp = self._makeData((4, 4), dtype) self._compare(inp, 0, 4) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitCols(self): for dtype in _TEST_DTYPES: inp = self._makeData((4, 4), dtype) @@ -232,7 +232,7 @@ class SplitOpTest(test.TestCase): self.assertEqual(out[i].shape, expected_shape) self.assertEqual(expected_shape, tf_ans[i].get_shape()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEmpty(self): # Note: np.split returns a rank-0 empty ndarray # if the input ndarray is empty. @@ -244,7 +244,7 @@ class SplitOpTest(test.TestCase): self._testEmpty(inp, 2, 3, (8, 0, 7)) self._testEmpty(inp, 2, 7, (8, 0, 3)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testIdentity(self): for dtype in _TEST_DTYPES: inp = self._makeData((2, 2, 2), dtype) @@ -252,7 +252,7 @@ class SplitOpTest(test.TestCase): self._compare(inp, 1, 1) self._compare(inp, 2, 1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitDim0(self): for dtype in _TEST_DTYPES: self._compare(self._makeData((6, 10, 18), dtype), 0, 3) @@ -281,7 +281,7 @@ class SplitOpTest(test.TestCase): offset += length self.assertAllEqual(result[i], inp[slices]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRandom(self): for dtype in _TEST_DTYPES: for _ in range(5): diff --git a/tensorflow/python/kernel_tests/template_test.py b/tensorflow/python/kernel_tests/template_test.py index 1b935d52867..0b3a396d6bf 100644 --- a/tensorflow/python/kernel_tests/template_test.py +++ b/tensorflow/python/kernel_tests/template_test.py @@ -150,7 +150,7 @@ class TemplateTest(test.TestCase): # Parameters are tied, so the loss should have gone down after training. self.assertLess(final_test_loss.numpy(), initial_test_loss.numpy()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_skip_stack_frames(self): first = traceback.format_stack() second = traceback.format_stack() @@ -158,7 +158,7 @@ class TemplateTest(test.TestCase): self.assertEqual(1, len(result)) self.assertNotEqual(len(first), len(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_with_name(self): tmpl1 = template.make_template("s1", variable_scoped_function) tmpl2 = template.make_template("s1", variable_scoped_function) @@ -204,7 +204,7 @@ class TemplateTest(test.TestCase): self.assertEqual(v1, v3) self.assertEqual("s1/dummy:0", v1.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_in_scope(self): tmpl1 = template.make_template("s1", variable_scoped_function) tmpl2 = template.make_template("s1", variable_scoped_function) @@ -221,7 +221,7 @@ class TemplateTest(test.TestCase): self.assertEqual("scope/s1/dummy:0", v1.name) self.assertEqual("scope/s1_1/dummy:0", v3.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_with_internal_reuse(self): tmpl1 = template.make_template("s1", internally_variable_scoped_function) tmpl2 = template.make_template("s1", internally_variable_scoped_function) @@ -237,13 +237,13 @@ class TemplateTest(test.TestCase): with self.assertRaises(ValueError): tmpl1("not_test") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_template_without_name(self): with self.assertRaisesRegexp( ValueError, "name cannot be None."): template.make_template(None, variable_scoped_function) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_make_template(self): # Test both that we can call it with positional and keywords. tmpl1 = template.make_template( @@ -266,7 +266,7 @@ class TemplateTest(test.TestCase): with self.assertRaises(ValueError): tmpl() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_enforces_no_extra_trainable_variables_eager(self): tmpl = template.make_template("s", function_with_side_create, @@ -287,7 +287,7 @@ class TemplateTest(test.TestCase): trainable=False) self.assertEqual(tmpl(name="1"), tmpl(name="2")) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_internal_variable_reuse(self): def nested(): @@ -310,7 +310,7 @@ class TemplateTest(test.TestCase): self.assertEqual("s1/nested/x:0", v1.name) self.assertEqual("s1_1/nested/x:0", v3.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_nested_templates(self): def nested_template(): @@ -360,7 +360,7 @@ class TemplateTest(test.TestCase): self.assertEqual("nested", tmpl1._checkpoint_dependencies[0].name) self.assertEqual("nested_1", tmpl1._checkpoint_dependencies[1].name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_nested_templates_with_defun(self): def variable_scoped_function_no_return_value(trainable=True): @@ -429,7 +429,7 @@ class TemplateTest(test.TestCase): "a", partial, create_graph_function_=True) self.assertAllEqual(tmpl(ops.convert_to_tensor(1.0)), 2.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_immediate_scope_creation(self): # Create templates in scope a then call in scope b. make_template should # capture the scope the first time it is called, and make_immediate_template @@ -454,7 +454,7 @@ class TemplateTest(test.TestCase): self.assertEqual("ctor_scope/a/dummy:0", inner_imm_var.name) self.assertEqual("call_scope/b/dummy:0", inner_defer_var.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_scope_access(self): # Ensure that we can access the scope inside the template, because the name # of that scope may be different from the name we pass to make_template, due @@ -479,7 +479,7 @@ class TemplateTest(test.TestCase): # Template is called at the top level, so there is no preceding "foo_2". self.assertEqual(tc.variable_scope.name, "blah") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_custom_getter(self): # Custom getter that maintains call count and forwards to true getter custom_getter_count = [0] @@ -512,7 +512,7 @@ class TemplateTest(test.TestCase): tmpl2() self.assertEqual(custom_getter_count[0], 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_fails_gracefully(self): for create_scope_now in [True, False]: def module_function_with_one_arg(inputs): @@ -535,7 +535,7 @@ class TemplateTest(test.TestCase): templatized_function(data) self.assertTrue(templatized_function._variables_created) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_name_scopes_for_variable_scopes(self): # Test that name scopes are not unnecessarily uniquified (but are # still uniquified when necessary). @@ -586,7 +586,7 @@ class TemplateTest(test.TestCase): "Second application of template should also get " "a freshly uniquified name scope.") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_global_variables(self): # Make sure global_variables are created. with variable_scope.variable_scope("foo"): @@ -608,7 +608,7 @@ class TemplateTest(test.TestCase): self.assertEqual(1, len(ta.global_variables)) self.assertEqual(2, len(tb.global_variables)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_trainable_variables(self): # Make sure trainable_variables are created. with variable_scope.variable_scope("foo2"): @@ -632,7 +632,7 @@ class TemplateTest(test.TestCase): self.assertEqual(1, len(ta.variables)) self.assertEqual(1, len(tb.variables)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_non_trainable_variables(self): # Make sure non_trainable_variables are created. with variable_scope.variable_scope("foo2"): @@ -675,7 +675,7 @@ class TemplateTest(test.TestCase): self.assertEqual(0, len(ta.local_variables)) self.assertEqual(1, len(tb.local_variables)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_make_template_with_defun(self): def variable_scoped_function_no_return_value(scope_name): diff --git a/tensorflow/python/kernel_tests/tensor_array_ops_test.py b/tensorflow/python/kernel_tests/tensor_array_ops_test.py index ea06357804f..6de6fbe7679 100644 --- a/tensorflow/python/kernel_tests/tensor_array_ops_test.py +++ b/tensorflow/python/kernel_tests/tensor_array_ops_test.py @@ -75,7 +75,7 @@ class TensorArrayTest(test.TestCase): super(TensorArrayTest, cls).tearDownClass() session_lib.Session.reset(cls._workers[0].target) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteRead(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -123,11 +123,11 @@ class TensorArrayTest(test.TestCase): self._testTensorArrayWritePack(dtypes.complex128) self._testTensorArrayWritePack(dtypes.string) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWritePack(self): self._testTensorArrayWritePackMaybeLegacy() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEmptyTensorArrayPack(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -161,7 +161,7 @@ class TensorArrayTest(test.TestCase): convert([[4.0, 5.0], [104.0, 105.0], [204.0, 205.0], [6.0, 7.0], [106.0, 107.0], [8.0, 9.0]]), c0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteConcat(self): self._testTensorArrayWriteConcat(dtypes.float32) self._testTensorArrayWriteConcat(dtypes.float64) @@ -184,7 +184,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([[0.0, 0.0], [4.0, 5.0], [0.0, 0.0]], self.evaluate(ta.write(1, [[4.0, 5.0]]).concat())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadOrPackNotAllValuesAvailableFillsZeros(self): self._testTensorArrayReadOrPackNotAllValuesAvailableFillsZeros() @@ -200,7 +200,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([[0.0, 0.0], [4.0, 5.0], [0.0, 0.0]], self.evaluate(ta.write(1, [[4.0, 5.0]]).concat())) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadOrPackNotAllValuesAvailableInferShapeFillsZeros(self): self._testTensorArrayReadOrPackNotAllValuesAvailableInferShapeFillsZeros() @@ -251,7 +251,7 @@ class TensorArrayTest(test.TestCase): self._testTensorArrayUnpackRead(dtypes.complex128) self._testTensorArrayUnpackRead(dtypes.string) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayUnpackRead(self): self._testTensorArrayUnpackReadMaybeLegacy() @@ -297,7 +297,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(convert([]).reshape(0, 2), d1) self.assertAllEqual(convert([[3.0, 301.0]]), d2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArraySplitRead(self): self._testTensorArraySplitRead(dtypes.float32) self._testTensorArraySplitRead(dtypes.float64) @@ -397,7 +397,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual(t_g_ta_0, t_g_ta_1) self.assertAllEqual([[4.0, 5.0]], d_r1_0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteWrongIndexOrDataTypeFails(self): with self.test_session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -416,7 +416,7 @@ class TensorArrayTest(test.TestCase): "resizeable and size is: 3"): self.evaluate(ta.write(3, 3.0).flow) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadWrongIndexOrDataTypeFails(self): with self.test_session(use_gpu=True): ta = _make_ta(3, "foo", dtype=dtypes.float32) @@ -450,7 +450,7 @@ class TensorArrayTest(test.TestCase): "it has already been written to."): self.evaluate(ta.write(2, 3.0).write(2, 3.0).flow) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayConcatIncompatibleShapesFails(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -482,7 +482,7 @@ class TensorArrayTest(test.TestCase): with self.assertRaisesOpError("shape"): self.evaluate(w3.concat()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArraySplitIncompatibleShapesFails(self): with self.test_session(use_gpu=True): in_eager_mode = context.executing_eagerly() @@ -603,7 +603,7 @@ class TensorArrayTest(test.TestCase): self.assertAllClose(fed_value, sess.run(read_value, feed_dict={value: fed_value})) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMultiTensorArray(self): with self.test_session(use_gpu=True): h1 = tensor_array_ops.TensorArray( @@ -706,7 +706,7 @@ class TensorArrayTest(test.TestCase): def testTensorArrayGradientWritePackConcatAndRead(self): self._testTensorArrayGradientWritePackConcatAndRead() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayReadTwice(self): with self.test_session(use_gpu=True): value = constant_op.constant([[1.0, -1.0], [10.0, -10.0]]) @@ -811,14 +811,14 @@ class TensorArrayTest(test.TestCase): def testTensorArrayGradientDynamicUnpackRead(self): self._testTensorArrayGradientDynamicUnpackRead() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCloseTensorArray(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( dtype=dtypes.float32, tensor_array_name="foo", size=3) self.evaluate(ta.close()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSizeTensorArray(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -826,7 +826,7 @@ class TensorArrayTest(test.TestCase): s = ta.size() self.assertAllEqual(3, self.evaluate(s)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWriteCloseTensorArray(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -924,7 +924,7 @@ class TensorArrayTest(test.TestCase): self.assertAllClose(grad_val.sum(axis=0), var_grad_t) self.assertAllClose(grad_val.sum(axis=0), state0_grad_t) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWhileLoopWritePackGradients(self): self._testWhileLoopWritePackGradients( dynamic_size=False, dtype=dtypes.float32) @@ -936,7 +936,7 @@ class TensorArrayTest(test.TestCase): self._testWhileLoopWritePackGradients( dynamic_size=True, dtype=dtypes.float32) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradSerialTwoLoops(self): with self.test_session(use_gpu=True): def loop(x): @@ -1113,7 +1113,7 @@ class TensorArrayTest(test.TestCase): r5 = w5.read(0) self.assertAllEqual([5, 4, 2, 3], r5.get_shape().as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def _testUnpackShape(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -1147,7 +1147,7 @@ class TensorArrayTest(test.TestCase): def testUnpackShape(self): self._testUnpackShape() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSplitShape(self): with self.test_session(use_gpu=True): ta = tensor_array_ops.TensorArray( @@ -1289,7 +1289,7 @@ class TensorArrayTest(test.TestCase): self.assertAllEqual([10.0, -10.0], read_vals[1]) self.assertAllEqual([[2.0, 3.0], [4.0, 5.0]], grad_vals[0]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayWriteGatherAndGradients(self): with self.test_session(use_gpu=True) as session: ta = tensor_array_ops.TensorArray( @@ -1433,7 +1433,7 @@ class TensorArrayTest(test.TestCase): self.assertFalse( [s for s in dev_stats[d] if "/TensorArray" in s.node_name]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTensorArrayIdentity(self): with self.test_session(use_gpu=True): ta0 = tensor_array_ops.TensorArray(dtype=dtypes.float32, size=2, diff --git a/tensorflow/python/kernel_tests/variable_scope_test.py b/tensorflow/python/kernel_tests/variable_scope_test.py index 2ee53df9317..1e59a8c9bf5 100644 --- a/tensorflow/python/kernel_tests/variable_scope_test.py +++ b/tensorflow/python/kernel_tests/variable_scope_test.py @@ -57,7 +57,7 @@ class VariableScopeTest(test.TestCase): v1 = vs.get_variable("v", [1]) self.assertEqual(v, v1) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResource(self): vs = variable_scope._get_default_variable_store() v1 = vs.get_variable("v", [1], use_resource=True) @@ -87,7 +87,7 @@ class VariableScopeTest(test.TestCase): self.assertEqual( set(expected_names), set([v.name for v in vs._vars.values()])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeInitializer(self): init = init_ops.constant_initializer(0.3) with variable_scope.variable_scope("tower0") as tower: @@ -100,7 +100,7 @@ class VariableScopeTest(test.TestCase): self.evaluate(variables_lib.variables_initializer([w])) self.assertAllClose(self.evaluate(w.value()), 0.3) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeConstraint(self): constraint = lambda x: 0. * x with variable_scope.variable_scope("tower1") as tower: @@ -117,7 +117,7 @@ class VariableScopeTest(test.TestCase): variables_lib.global_variables_initializer().run() self.assertAllEqual(compat.as_bytes(v.eval()), b"") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeDType(self): with variable_scope.variable_scope("tower2") as tower: with variable_scope.variable_scope("foo", dtype=dtypes.float16): @@ -197,7 +197,7 @@ class VariableScopeTest(test.TestCase): self.assertAllEqual([v1, v2], [v3, v4]) f() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerVariablesStoreAddsToCollections(self): store = variable_scope.EagerVariableStore() with store.as_default(): @@ -214,7 +214,7 @@ class VariableScopeTest(test.TestCase): self.assertEqual( ops.get_collection(ops.GraphKeys.CONCATENATED_VARIABLES), [concat]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerVariablesOutsideStoreNotAddedToCollections(self): if not context.executing_eagerly(): return @@ -223,7 +223,7 @@ class VariableScopeTest(test.TestCase): self.assertFalse(ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)) self.assertFalse(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFromNonTensorValue(self): v = variable_scope.get_variable("v4", initializer=4, dtype=dtypes.int32) self.evaluate(variables_lib.variables_initializer([v])) @@ -239,7 +239,7 @@ class VariableScopeTest(test.TestCase): with self.assertRaises(error): variable_scope.get_variable("x4", initializer={}) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitFromNonInitializer(self): # Test various dtypes with zeros initializer as following: types = [ @@ -294,7 +294,7 @@ class VariableScopeTest(test.TestCase): v_tower = variable_scope.get_variable("v", []) self.assertFalse(v_tower.value().device.startswith(caching_device)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeRegularizer(self): init = init_ops.constant_initializer(0.3) @@ -339,7 +339,7 @@ class VariableScopeTest(test.TestCase): losses = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES) self.assertEqual(3, len(losses)) # No new loss added. - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInitializeFromValue(self): init = constant_op.constant(0.1) w = variable_scope.get_variable("v", initializer=init) @@ -428,7 +428,7 @@ class VariableScopeTest(test.TestCase): sess.run(v0.initializer) sess.run(add) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetVariableScope(self): # Test the get_variable_scope() function and setting properties of result. init = init_ops.constant_initializer(0.3) @@ -449,7 +449,7 @@ class VariableScopeTest(test.TestCase): new_init = variable_scope.get_variable_scope().initializer self.assertEqual(new_init, None) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScope(self): with variable_scope.variable_scope("tower4") as tower: self.assertEqual(tower.name, "tower4") @@ -468,7 +468,7 @@ class VariableScopeTest(test.TestCase): with ops.name_scope("scope") as sc: self.assertEqual(sc, "tower6/tower4/scope/") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testVarScopeNameScope(self): with ops.name_scope("testVarScopeNameScope1"): with variable_scope.variable_scope("tower") as tower: @@ -961,7 +961,7 @@ class VariableScopeTest(test.TestCase): self.assertEqual( constant_op.constant([], name="c").name, "another/inner/c:0") - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGetLocalVar(self): # Check that local variable respects naming. with variable_scope.variable_scope("outer") as outer: diff --git a/tensorflow/python/layers/base_test.py b/tensorflow/python/layers/base_test.py index fcacc8d6038..298e96e711c 100644 --- a/tensorflow/python/layers/base_test.py +++ b/tensorflow/python/layers/base_test.py @@ -39,7 +39,7 @@ from tensorflow.python.platform import test class BaseLayerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLayerProperties(self): layer = base_layers.Layer(name='my_layer') self.assertEqual(layer.variables, []) @@ -53,13 +53,13 @@ class BaseLayerTest(test.TestCase): layer = base_layers.Layer(name='my_layer', trainable=False) self.assertEqual(layer.trainable, False) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInt64Layer(self): layer = base_layers.Layer(name='my_layer', dtype='int64') layer.add_variable('my_var', [2, 2]) self.assertEqual(layer.name, 'my_layer') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAddWeight(self): layer = base_layers.Layer(name='my_layer') @@ -116,7 +116,7 @@ class BaseLayerTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'activity_regularizer'): core_layers.Dense(1, activity_regularizer=lambda *args, **kwargs: 0.) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCall(self): class MyLayer(base_layers.Layer): @@ -132,7 +132,7 @@ class BaseLayerTest(test.TestCase): # op is only supported in GRAPH mode self.assertEqual(outputs.op.name, 'my_layer/Square') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDeepCopy(self): class MyLayer(base_layers.Layer): @@ -155,7 +155,7 @@ class BaseLayerTest(test.TestCase): self.assertEqual(layer_copy._graph, layer._graph) self.assertEqual(layer_copy._private_tensor, layer._private_tensor) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testScopeNaming(self): class PrivateLayer(base_layers.Layer): @@ -203,7 +203,7 @@ class BaseLayerTest(test.TestCase): my_layer_scoped1.apply(inputs) self.assertEqual(my_layer_scoped1._scope.name, 'var_scope/my_layer_1') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecNdimCheck(self): class CustomerLayer(base_layers.Layer): @@ -230,7 +230,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1], [2]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecMinNdimCheck(self): class CustomerLayer(base_layers.Layer): @@ -258,7 +258,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[[1], [2]]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecMaxNdimCheck(self): class CustomerLayer(base_layers.Layer): @@ -286,7 +286,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1], [2]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecDtypeCheck(self): class CustomerLayer(base_layers.Layer): @@ -306,7 +306,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant(1.0, dtype=dtypes.float32)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecAxesCheck(self): class CustomerLayer(base_layers.Layer): @@ -328,7 +328,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1, 2], [3, 4], [5, 6]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testInputSpecShapeCheck(self): class CustomerLayer(base_layers.Layer): @@ -348,7 +348,7 @@ class BaseLayerTest(test.TestCase): layer = CustomerLayer() layer.apply(constant_op.constant([[1, 2, 3], [4, 5, 6]])) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoInputSpec(self): class CustomerLayer(base_layers.Layer): @@ -369,7 +369,7 @@ class BaseLayerTest(test.TestCase): layer.apply(array_ops.placeholder('int32')) layer.apply(array_ops.placeholder('int32', shape=(2, 3))) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_count_params(self): dense = core_layers.Dense(16) dense.build((None, 4)) @@ -379,7 +379,7 @@ class BaseLayerTest(test.TestCase): with self.assertRaises(ValueError): dense.count_params() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDictInputOutput(self): class DictLayer(base_layers.Layer): diff --git a/tensorflow/python/layers/core_test.py b/tensorflow/python/layers/core_test.py index cf45b076371..040c1cddc0f 100644 --- a/tensorflow/python/layers/core_test.py +++ b/tensorflow/python/layers/core_test.py @@ -41,7 +41,7 @@ from tensorflow.python.platform import test class DenseTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDenseProperties(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') self.assertEqual(dense.units, 2) @@ -91,14 +91,14 @@ class DenseTest(test.TestCase): core_layers.Dense(5)(inputs) core_layers.Dense(2, activation=nn_ops.relu, name='my_dense')(inputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCallTensorDot(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='my_dense') inputs = random_ops.random_uniform((5, 4, 3), seed=1) outputs = dense(inputs) self.assertListEqual([5, 4, 2], outputs.get_shape().as_list()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoBias(self): dense = core_layers.Dense(2, use_bias=False, name='my_dense') inputs = random_ops.random_uniform((5, 2), seed=1) @@ -112,7 +112,7 @@ class DenseTest(test.TestCase): self.assertEqual(dense.kernel.name, 'my_dense/kernel:0') self.assertEqual(dense.bias, None) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNonTrainable(self): dense = core_layers.Dense(2, trainable=False, name='my_dense') inputs = random_ops.random_uniform((5, 2), seed=1) @@ -125,7 +125,7 @@ class DenseTest(test.TestCase): self.assertEqual( len(ops.get_collection(ops.GraphKeys.TRAINABLE_VARIABLES)), 0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOutputShape(self): dense = core_layers.Dense(7, activation=nn_ops.relu, name='my_dense') inputs = random_ops.random_uniform((5, 3), seed=1) @@ -165,7 +165,7 @@ class DenseTest(test.TestCase): dense = core_layers.Dense(4, name='my_dense') dense(inputs) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testActivation(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1') inputs = random_ops.random_uniform((5, 3), seed=1) @@ -325,7 +325,7 @@ class DenseTest(test.TestCase): var_key = 'test2/dense/kernel' self.assertEqual(var_dict[var_key].name, '%s:0' % var_key) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputeOutputShape(self): dense = core_layers.Dense(2, activation=nn_ops.relu, name='dense1') ts = tensor_shape.TensorShape @@ -347,7 +347,7 @@ class DenseTest(test.TestCase): dense.compute_output_shape(ts([None, 4, 3])).as_list()) # pylint: enable=protected-access - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testConstraints(self): k_constraint = lambda x: x / math_ops.reduce_sum(x) b_constraint = lambda x: x / math_ops.reduce_max(x) @@ -369,7 +369,7 @@ def _get_variable_dict_from_varstore(): class DropoutTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDropoutProperties(self): dp = core_layers.Dropout(0.5, name='dropout') self.assertEqual(dp.rate, 0.5) @@ -377,7 +377,7 @@ class DropoutTest(test.TestCase): dp.apply(array_ops.ones(())) self.assertEqual(dp.name, 'dropout') - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBooleanLearningPhase(self): dp = core_layers.Dropout(0.5) inputs = array_ops.ones((5, 3)) @@ -402,7 +402,7 @@ class DropoutTest(test.TestCase): np_output = sess.run(dropped, feed_dict={training: False}) self.assertAllClose(np.ones((5, 5)), np_output) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDynamicNoiseShape(self): inputs = array_ops.ones((5, 3, 2)) noise_shape = [None, 1, None] diff --git a/tensorflow/python/ops/control_flow_ops_test.py b/tensorflow/python/ops/control_flow_ops_test.py index 59bb925df0f..43fe045bcb1 100644 --- a/tensorflow/python/ops/control_flow_ops_test.py +++ b/tensorflow/python/ops/control_flow_ops_test.py @@ -939,7 +939,7 @@ class CaseTest(test_util.TensorFlowTestCase): class WhileLoopTestCase(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWhileLoopWithSingleVariable(self): i = constant_op.constant(0) c = lambda i: math_ops.less(i, 10) @@ -948,7 +948,7 @@ class WhileLoopTestCase(test_util.TensorFlowTestCase): self.assertEqual(self.evaluate(r), 10) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEagerWhileLoopWithSingleVariable_bodyReturnsTuple(self): i = constant_op.constant(0) c = lambda i: math_ops.less(i, 10) diff --git a/tensorflow/python/ops/math_ops_test.py b/tensorflow/python/ops/math_ops_test.py index 980c92b0d59..8417d8a7b10 100644 --- a/tensorflow/python/ops/math_ops_test.py +++ b/tensorflow/python/ops/math_ops_test.py @@ -37,14 +37,14 @@ log = np.log class ReduceTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceAllDims(self): x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) with test_util.device(use_gpu=True): y_tf = self.evaluate(math_ops.reduce_sum(x)) self.assertEqual(y_tf, 21) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceExplicitAxes(self): x = np.array([[1, 2, 3], [4, 5, 6]], dtype=np.int32) with test_util.device(use_gpu=True): @@ -57,7 +57,7 @@ class ReduceTest(test_util.TensorFlowTestCase): for axis in (None, (0, 1), (-1, -2), (-2, -1, 0, 1)): self.assertEqual(self.evaluate(math_ops.reduce_sum(x, axis=axis)), 21) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testReduceInvalidAxis(self): if context.executing_eagerly(): # The shape check is in run a graph construction time. In eager mode, @@ -150,7 +150,7 @@ class LogSumExpTest(test_util.TensorFlowTestCase): class RoundTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRounding(self): x = np.arange(-5.0, 5.0, .25) for dtype in [np.float32, np.double, np.int32]: @@ -194,7 +194,7 @@ class ModTest(test_util.TensorFlowTestCase): class SquaredDifferenceTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSquaredDifference(self): for dtype in [np.int32, np.float16]: x = np.array([[1, 2, 3], [4, 5, 6]], dtype=dtype) @@ -207,7 +207,7 @@ class SquaredDifferenceTest(test_util.TensorFlowTestCase): class ApproximateEqualTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testApproximateEqual(self): for dtype in [np.float32, np.double]: x = dtype(1) @@ -238,7 +238,7 @@ class ApproximateEqualTest(test_util.TensorFlowTestCase): class ScalarMulTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsRefs(self): if context.executing_eagerly(): var = resource_variable_ops.ResourceVariable(10, name="var") @@ -250,14 +250,14 @@ class ScalarMulTest(test_util.TensorFlowTestCase): self.evaluate(init) self.assertEqual(30, self.evaluate(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsConstant(self): const = constant_op.constant(10) result = math_ops.scalar_mul(3, const) with test_util.device(use_gpu=True): self.assertEqual(30, self.evaluate(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsTensor(self): tensor = array_ops.ones([10, 10]) result = math_ops.scalar_mul(3, tensor) @@ -266,7 +266,7 @@ class ScalarMulTest(test_util.TensorFlowTestCase): with test_util.device(use_gpu=True): self.assertAllEqual(self.evaluate(expected), self.evaluate(result)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAcceptsIndexedSlices(self): values = constant_op.constant([2, 3, 5, 7, 0, -1], shape=[3, 2]) indices = constant_op.constant([0, 2, 5]) diff --git a/tensorflow/python/ops/nn_test.py b/tensorflow/python/ops/nn_test.py index 035b4735aff..ae24ca0552e 100644 --- a/tensorflow/python/ops/nn_test.py +++ b/tensorflow/python/ops/nn_test.py @@ -76,7 +76,7 @@ class SoftmaxTest(test_lib.TestCase): z = u.sum(1)[:, np.newaxis] return u / z - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSoftmax(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) @@ -123,7 +123,7 @@ class LogPoissonLossTest(test_lib.TestCase): lpl += np.ma.masked_array(stirling_approx, mask=(z <= 1)).filled(0.) return lpl - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogPoissonLoss(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) @@ -164,7 +164,7 @@ class LogSoftmaxTest(test_lib.TestCase): u = x - m return u - np.log(np.sum(np.exp(u), 1, keepdims=True)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLogSoftmax(self): x_shape = [5, 10] x_np = np.random.randn(*x_shape).astype(np.float32) @@ -201,7 +201,7 @@ class LogSoftmaxTest(test_lib.TestCase): class L2LossTest(test_lib.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testL2Loss(self): for dtype in [dtypes.float32, dtypes.float64]: x = constant_op.constant( @@ -235,7 +235,7 @@ class L2NormalizeTest(test_lib.TestCase): norm = np.apply_along_axis(np.linalg.norm, dim, x) return x / np.expand_dims(norm, dim) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testL2Normalize(self): x_shape = [20, 7, 3] np.random.seed(1) @@ -246,7 +246,7 @@ class L2NormalizeTest(test_lib.TestCase): y_tf = nn_impl.l2_normalize(x_tf, dim) self.assertAllClose(y_np, self.evaluate(y_tf)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testL2NormalizeDimArray(self): x_shape = [20, 7, 3] np.random.seed(1) diff --git a/tensorflow/python/training/checkpointable/data_structures_test.py b/tensorflow/python/training/checkpointable/data_structures_test.py index b05b3a88002..ce5852dd6e1 100644 --- a/tensorflow/python/training/checkpointable/data_structures_test.py +++ b/tensorflow/python/training/checkpointable/data_structures_test.py @@ -66,7 +66,7 @@ class HasList(training.Model): class ListTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTracking(self): model = HasList() output = model(array_ops.ones([32, 2])) @@ -106,7 +106,7 @@ class ListTests(test.TestCase): model(model_input) self.assertEqual(0, len(model.updates)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLossesForwarded(self): model = HasList() model_input = array_ops.ones([32, 2]) @@ -190,7 +190,7 @@ class HasMapping(training.Model): class MappingTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTracking(self): model = HasMapping() output = model(array_ops.ones([32, 2])) diff --git a/tensorflow/python/training/checkpointable/util_test.py b/tensorflow/python/training/checkpointable/util_test.py index e2115417c44..9f479bcd1e9 100644 --- a/tensorflow/python/training/checkpointable/util_test.py +++ b/tensorflow/python/training/checkpointable/util_test.py @@ -355,7 +355,7 @@ class CheckpointingTests(test.TestCase): optimizer_node.slot_variables[0] .slot_variable_node_id].attributes[0].checkpoint_key) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturned(self): v = _OwnsMirroredVariables() checkpoint = checkpointable_utils.Checkpoint(v=v) @@ -375,7 +375,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(44., self.evaluate(v.non_dep_variable)) self.assertEqual(44., self.evaluate(v.mirrored)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturnedWithGlobalName(self): # The same object can also be saved using the name-based saver. v = _OwnsMirroredVariables() @@ -391,7 +391,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(42., self.evaluate(v.non_dep_variable)) self.assertEqual(42., self.evaluate(v.mirrored)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveRestore(self): model = MyModel() optimizer = adam.AdamOptimizer(0.001) @@ -512,7 +512,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(training_continuation + 1, session.run(root.save_counter)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAgnosticUsage(self): """Graph/eager agnostic usage.""" # Does create garbage when executing eagerly due to ops.Graph() creation. @@ -546,7 +546,7 @@ class CheckpointingTests(test.TestCase): self.evaluate(root.save_counter)) # pylint: disable=cell-var-from-loop - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testWithDefun(self): num_training_steps = 2 checkpoint_directory = self.get_temp_dir() @@ -619,7 +619,7 @@ class CheckpointingTests(test.TestCase): root, saveables_cache=None) self.assertEqual(r"leaf/v/.ATTRIBUTES/VARIABLE_VALUE", named_variable.name) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLocalNameValidation(self): root = checkpointable.Checkpointable() leaf = checkpointable.Checkpointable() @@ -660,7 +660,7 @@ class CheckpointingTests(test.TestCase): optimizer.apply_gradients( [(g, v) for g, v in zip(grad, model.vars)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLateDependencyTracking(self): class Dependency(checkpointable.Checkpointable): @@ -692,7 +692,7 @@ class CheckpointingTests(test.TestCase): status.run_restore_ops() self.assertEqual(123., self.evaluate(load_into.dep.var)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDepAfterVar(self): class Dependency(checkpointable.Checkpointable): @@ -724,7 +724,7 @@ class CheckpointingTests(test.TestCase): status.run_restore_ops() self.assertEqual(-14., self.evaluate(loaded_dep_after_var.dep.var)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDeferredSlotRestoration(self): checkpoint_directory = self.get_temp_dir() @@ -789,7 +789,7 @@ class CheckpointingTests(test.TestCase): self.evaluate(train_op) slot_status.assert_consumed() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testOverlappingRestores(self): checkpoint_directory = self.get_temp_dir() save_root = checkpointable.Checkpointable() @@ -840,7 +840,7 @@ class CheckpointingTests(test.TestCase): second_status.run_restore_ops() self.assertEqual(12., self.evaluate(load_dep.var)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAmbiguousLoad(self): # Not OK to split one checkpoint object into two checkpoint_directory = self.get_temp_dir() @@ -866,7 +866,7 @@ class CheckpointingTests(test.TestCase): with self.assertRaises(AssertionError): status.assert_consumed() - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testObjectsCombined(self): # Currently fine to load two checkpoint objects into one Python object checkpoint_directory = self.get_temp_dir() @@ -893,7 +893,7 @@ class CheckpointingTests(test.TestCase): self.assertEqual(32., self.evaluate(v1)) self.assertEqual(64., self.evaluate(v2)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDependencyLoop(self): # Note: this test creates garbage during eager execution because it # purposefully creates a reference cycle. @@ -939,7 +939,7 @@ class CheckpointingTests(test.TestCase): self.assertAllEqual([3., 1., 4.], self.evaluate(first_load.v)) self.assertAllEqual([1., 1., 2., 3.], self.evaluate(second_load.v)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testRestoreOnAssign(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -989,7 +989,7 @@ class CheckpointingTests(test.TestCase): saver.save(checkpoint_prefix) self.assertEqual(before_ops, graph.get_operations()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCheckpointCleanup(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -1009,7 +1009,7 @@ class CheckpointingTests(test.TestCase): expected_filenames, os.listdir(checkpoint_directory)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testCheckpointCleanupChangingVarList(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -1132,7 +1132,7 @@ class CheckpointingTests(test.TestCase): beta1_power, _ = optimizer._get_beta_accumulators() self.assertAllEqual(3., self.evaluate(beta1_power)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_sequential(self): model = sequential.Sequential() checkpoint = checkpointable_utils.Checkpoint(model=model) @@ -1164,7 +1164,7 @@ class CheckpointingTests(test.TestCase): self.assertAllEqual([1., 2., 3., 4., 5.], self.evaluate(deferred_second_dense.bias)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_initialize_if_not_restoring(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") @@ -1257,7 +1257,7 @@ class _ManualScope(checkpointable.Checkpointable): class TemplateTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore(self): def _templated(): @@ -1308,7 +1308,7 @@ class TemplateTests(test.TestCase): self.assertAllEqual([13.], self.evaluate(var_plus_one)) self.assertAllEqual([14.], self.evaluate(var2)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_checkpointable_save_restore_nested(self): def _inner_template(): @@ -1409,7 +1409,7 @@ class CheckpointCompatibilityTests(test.TestCase): sess=session, save_path=checkpoint_prefix, global_step=root.optimizer_step) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testLoadFromNameBasedSaver(self): """Save a name-based checkpoint, load it using the object-based API.""" with test_util.device(use_gpu=True): @@ -1471,7 +1471,7 @@ class CheckpointCompatibilityTests(test.TestCase): class PythonMetadataTests(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveLoad(self): checkpoint_directory = self.get_temp_dir() checkpoint_prefix = os.path.join(checkpoint_directory, "ckpt") diff --git a/tensorflow/python/training/learning_rate_decay_test.py b/tensorflow/python/training/learning_rate_decay_test.py index efcf47edda5..4f3cf01822c 100644 --- a/tensorflow/python/training/learning_rate_decay_test.py +++ b/tensorflow/python/training/learning_rate_decay_test.py @@ -31,7 +31,7 @@ from tensorflow.python.training import learning_rate_decay class LRDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testContinuous(self): self.evaluate(variables.global_variables_initializer()) step = 5 @@ -39,7 +39,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): expected = .05 * 0.96**(5.0 / 10.0) self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStaircase(self): if context.executing_eagerly(): step = resource_variable_ops.ResourceVariable(0) @@ -80,7 +80,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): expected = .1 * 0.96 ** (100 // 3) self.assertAllClose(decayed_lr.eval(), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPiecewiseConstant(self): x = resource_variable_ops.ResourceVariable(-999) decayed_lr = learning_rate_decay.piecewise_constant( @@ -100,7 +100,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): self.evaluate(x.assign(999)) self.assertAllClose(self.evaluate(decayed_lr), 0.001, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testPiecewiseConstantEdgeCases(self): x_int = resource_variable_ops.ResourceVariable( 0, dtype=variables.dtypes.int32) @@ -147,7 +147,7 @@ class LRDecayTest(test_util.TensorFlowTestCase): class LinearDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testHalfWay(self): step = 5 lr = 0.05 @@ -156,7 +156,7 @@ class LinearDecayTest(test_util.TensorFlowTestCase): expected = lr * 0.5 self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEnd(self): step = 10 lr = 0.05 @@ -165,7 +165,7 @@ class LinearDecayTest(test_util.TensorFlowTestCase): expected = end_lr self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testHalfWayWithEnd(self): step = 5 lr = 0.05 @@ -174,7 +174,7 @@ class LinearDecayTest(test_util.TensorFlowTestCase): expected = (lr + end_lr) * 0.5 self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBeyondEnd(self): step = 15 lr = 0.05 @@ -183,7 +183,7 @@ class LinearDecayTest(test_util.TensorFlowTestCase): expected = end_lr self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBeyondEndWithCycle(self): step = 15 lr = 0.05 @@ -196,7 +196,7 @@ class LinearDecayTest(test_util.TensorFlowTestCase): class SqrtDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testHalfWay(self): step = 5 lr = 0.05 @@ -207,7 +207,7 @@ class SqrtDecayTest(test_util.TensorFlowTestCase): expected = lr * 0.5**power self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testEnd(self): step = 10 lr = 0.05 @@ -218,7 +218,7 @@ class SqrtDecayTest(test_util.TensorFlowTestCase): expected = end_lr self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testHalfWayWithEnd(self): step = 5 lr = 0.05 @@ -229,7 +229,7 @@ class SqrtDecayTest(test_util.TensorFlowTestCase): expected = (lr - end_lr) * 0.5**power + end_lr self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBeyondEnd(self): step = 15 lr = 0.05 @@ -240,7 +240,7 @@ class SqrtDecayTest(test_util.TensorFlowTestCase): expected = end_lr self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBeyondEndWithCycle(self): step = 15 lr = 0.05 @@ -254,7 +254,7 @@ class SqrtDecayTest(test_util.TensorFlowTestCase): class PolynomialDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBeginWithCycle(self): lr = 0.001 decay_steps = 10 @@ -267,7 +267,7 @@ class PolynomialDecayTest(test_util.TensorFlowTestCase): class ExponentialDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDecay(self): initial_lr = 0.1 k = 10 @@ -282,7 +282,7 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase): self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) self.evaluate(step.assign_add(1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStaircase(self): initial_lr = 0.1 k = 10 @@ -300,7 +300,7 @@ class ExponentialDecayTest(test_util.TensorFlowTestCase): class InverseDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDecay(self): initial_lr = 0.1 k = 10 @@ -315,7 +315,7 @@ class InverseDecayTest(test_util.TensorFlowTestCase): self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) self.evaluate(step.assign_add(1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testStaircase(self): initial_lr = 0.1 k = 10 @@ -339,7 +339,7 @@ class CosineDecayTest(test_util.TensorFlowTestCase): decay = 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 @@ -349,7 +349,7 @@ class CosineDecayTest(test_util.TensorFlowTestCase): expected = self.np_cosine_decay(step, num_training_steps) self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 @@ -375,7 +375,7 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): decay = fac * 0.5 * (1.0 + math.cos(math.pi * completed_fraction)) return (1.0 - alpha) * decay + alpha - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDecay(self): num_training_steps = 1000 initial_lr = 1.0 @@ -385,7 +385,7 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): expected = self.np_cosine_decay_restarts(step, num_training_steps) self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testAlpha(self): num_training_steps = 1000 initial_lr = 1.0 @@ -397,7 +397,7 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): step, num_training_steps, alpha=alpha) self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMMul(self): num_training_steps = 1000 initial_lr = 1.0 @@ -409,7 +409,7 @@ class CosineDecayRestartsTest(test_util.TensorFlowTestCase): step, num_training_steps, m_mul=m_mul) self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testTMul(self): num_training_steps = 1000 initial_lr = 1.0 @@ -436,7 +436,7 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): cosine_decayed = 0.5 * (1.0 + math.cos(math.pi * fraction)) return (alpha + linear_decayed) * cosine_decayed + beta - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 @@ -446,7 +446,7 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): expected = self.np_linear_cosine_decay(step, num_training_steps) self.assertAllClose(self.evaluate(decayed_lr), expected, 1e-6) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNonDefaultDecay(self): num_training_steps = 1000 initial_lr = 1.0 @@ -465,7 +465,7 @@ class LinearCosineDecayTest(test_util.TensorFlowTestCase): class NoisyLinearCosineDecayTest(test_util.TensorFlowTestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 @@ -476,7 +476,7 @@ class NoisyLinearCosineDecayTest(test_util.TensorFlowTestCase): # Cannot be deterministically tested self.evaluate(decayed_lr) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNonDefaultNoisyLinearCosine(self): num_training_steps = 1000 initial_lr = 1.0 diff --git a/tensorflow/python/training/optimizer_test.py b/tensorflow/python/training/optimizer_test.py index 0cab6410e83..dfe9176beaf 100644 --- a/tensorflow/python/training/optimizer_test.py +++ b/tensorflow/python/training/optimizer_test.py @@ -34,7 +34,7 @@ from tensorflow.python.training import gradient_descent class OptimizerTest(test.TestCase): - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testBasic(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -112,7 +112,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([3.0 - 3 * 3 * 42.0, 4.0 - 3 * 3 * (-42.0)], var1.eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoVariables(self): for dtype in [dtypes.half, dtypes.float32, dtypes.float64]: # pylint: disable=cell-var-from-loop @@ -127,7 +127,7 @@ class OptimizerTest(test.TestCase): with self.assertRaisesRegexp(ValueError, 'No.*variables'): sgd_op.minimize(loss) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -145,7 +145,7 @@ class OptimizerTest(test.TestCase): # var1 has no gradient sgd_op.minimize(loss, var_list=[var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_Minimize(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -161,7 +161,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.minimize(loss, var_list=[var0, var1]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_ApplyGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -175,7 +175,7 @@ class OptimizerTest(test.TestCase): 'No gradients provided for any variable'): sgd_op.apply_gradients([(None, var0), (None, var1)]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testGradientsAsVariables(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): # Note that we name the variables uniquely here since the variables don't @@ -215,7 +215,7 @@ class OptimizerTest(test.TestCase): self.assertAllClose([-14., -13.], self.evaluate(var0)) self.assertAllClose([-6., -5.], self.evaluate(var1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testComputeGradientsWithTensors(self): x = ops.convert_to_tensor(1.0) def f(): diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index e3be7d868e5..96963f55bd0 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -171,7 +171,7 @@ class SaverTest(test.TestCase): def testBasic(self): self.basicSaveRestore(variables.Variable) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResourceBasic(self): self.basicSaveRestore(resource_variable_ops.ResourceVariable) @@ -252,7 +252,7 @@ class SaverTest(test.TestCase): self.assertAllEqual(w3.eval(), 3.0) self.assertAllEqual(w4.eval(), 4.0) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testResourceSaveRestoreCachingDevice(self): save_path = os.path.join(self.get_temp_dir(), "resource_cache") with self.test_session(graph=ops_lib.Graph()) as sess: @@ -671,7 +671,7 @@ class SaverTest(test.TestCase): save.restore(sess, save_path) self.assertAllClose([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]], var.eval()) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testSaveWithGlobalStep(self, pad_step_number=False): save_path = os.path.join(self.get_temp_dir(), "ckpt_with_global_step") global_step_int = 5 @@ -1395,7 +1395,7 @@ class KeepCheckpointEveryNHoursTest(test.TestCase): gfile.MakeDirs(test_dir) return test_dir - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes @test.mock.patch.object(saver_module, "time") def testNonSharded(self, mock_time): save_dir = self._get_test_dir("keep_checkpoint_every_n_hours") @@ -1515,7 +1515,7 @@ class SaveRestoreWithVariableNameMap(test.TestCase): self.assertEqual(10.0, self.evaluate(v0)) self.assertEqual(20.0, self.evaluate(v1)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNonReshapeResourceVariable(self): self._testNonReshape(resource_variable_ops.ResourceVariable) @@ -3021,7 +3021,7 @@ class MyModel(training.Model): class CheckpointableCompatibilityTests(test.TestCase): # TODO(allenl): Track down python3 reference cycles in these tests. - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testNotSaveableButIsCheckpointable(self): v = _OwnsAVariableSimple() saver = saver_module.Saver(var_list=[v]) @@ -3034,7 +3034,7 @@ class CheckpointableCompatibilityTests(test.TestCase): saver.restore(sess, save_path) self.assertEqual(42., self.evaluate(v.non_dep_variable)) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def testMoreComplexSaveableReturned(self): v = _OwnsMirroredVariables() saver = saver_module.Saver(var_list=[v]) diff --git a/tensorflow/python/util/serialization_test.py b/tensorflow/python/util/serialization_test.py index 5000bcfad05..9d9cac27259 100644 --- a/tensorflow/python/util/serialization_test.py +++ b/tensorflow/python/util/serialization_test.py @@ -47,7 +47,7 @@ class SerializationTests(test.TestCase): self.assertIs(round_trip[0], None) self.assertEqual(round_trip[1], 2) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_serialize_sequential(self): model = sequential.Sequential() model.add(core.Dense(4)) @@ -61,7 +61,7 @@ class SerializationTests(test.TestCase): self.assertAllEqual([1, 1], input_round_trip[0]["config"]["batch_input_shape"]) - @test_util.run_in_graph_and_eager_modes() + @test_util.run_in_graph_and_eager_modes def test_serialize_model(self): x = input_layer.Input(shape=[3]) y = core.Dense(10)(x)