diff options
21 files changed, 253 insertions, 253 deletions
diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py index 906c916b27..42d69645ac 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/model_ops_test.py @@ -98,7 +98,7 @@ class ModelOpsTest(test_util.TensorFlowTestCase): self._seed = 123 def testCreate(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree = tree_ensemble_config.trees.add() _append_to_leaf(tree.nodes.add().leaf, 0, -0.4) @@ -204,10 +204,10 @@ class ModelOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(result.eval(), [[0.5, -0.2], [0, 1.0]]) def testRestore(self): - # Calling self.test_session() without a graph specified results in + # Calling self.cached_session() without a graph specified results in # TensorFlowTestCase caching the session and returning the same one # every time. In this test, we need to create two different sessions - # which is why we also create a graph and pass it to self.test_session() + # which is why we also create a graph and pass it to self.cached_session() # to ensure no caching occurs under the hood. save_path = os.path.join(self.get_temp_dir(), "restore-test") with ops.Graph().as_default() as graph: @@ -311,7 +311,7 @@ class ModelOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(result.eval(), [[-1.1], [-1.1]]) def testUsedHandlers(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_config.growing_metadata.used_handler_ids.append(1) tree_ensemble_config.growing_metadata.used_handler_ids.append(5) diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py index bef42fdf7f..4278a30ba9 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/prediction_ops_test.py @@ -201,7 +201,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): reduce_dim=reduce_dim) def testEmptyEnsemble(self): - with self.test_session(): + with self.cached_session(): # Empty tree ensenble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() @@ -224,7 +224,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testBiasEnsembleSingleClass(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree = tree_ensemble_config.trees.add() tree_ensemble_config.tree_metadata.add().is_finalized = True @@ -252,7 +252,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testBiasEnsembleMultiClass(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree = tree_ensemble_config.trees.add() tree_ensemble_config.tree_metadata.add().is_finalized = True @@ -282,7 +282,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testFullEnsembleSingleClass(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree. tree1 = tree_ensemble_config.trees.add() @@ -378,7 +378,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testFullEnsembleWithMultidimensionalSparseSingleClass(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree. tree1 = tree_ensemble_config.trees.add() @@ -466,7 +466,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testExcludeNonFinalTree(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree. tree1 = tree_ensemble_config.trees.add() @@ -513,7 +513,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testIncludeNonFinalTree(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree. tree1 = tree_ensemble_config.trees.add() @@ -564,7 +564,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): def testMetadataMissing(self): # Sometimes we want to do prediction on trees that are not added to ensemble # (for example in - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree. tree1 = tree_ensemble_config.trees.add() @@ -612,7 +612,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): # For TREE_PER_CLASS strategy, predictions size is num_classes-1 def testFullEnsembleMultiClassTreePerClassStrategy(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree only for second class. tree1 = tree_ensemble_config.trees.add() @@ -663,7 +663,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): # This test is when leafs have SPARSE weights stored (class id and # contribution). def testFullEnsembleMultiNotClassTreePerClassStrategySparseVector(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree only for second class. tree1 = tree_ensemble_config.trees.add() @@ -713,7 +713,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): # will have the size of the number of classes. # This test is when leafs have DENSE weights stored (weight for each class) def testFullEnsembleMultiNotClassTreePerClassStrategyDenseVector(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Bias tree only for second class. tree1 = tree_ensemble_config.trees.add() @@ -760,7 +760,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([[], []], dropout_info.eval()) def testDropout(self): - with self.test_session(): + with self.cached_session(): # Empty tree ensenble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Add 1000 trees with some weights. @@ -823,7 +823,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): # This is for normal non-batch mode where ensemble does not contain the tree # that is being built currently. num_trees = 10 - with self.test_session(): + with self.cached_session(): # Empty tree ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Add 10 trees with some weights. @@ -891,7 +891,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): # This is batch mode where ensemble already contains the tree that we are # building. This tree should never be dropped. num_trees = 10 - with self.test_session(): + with self.cached_session(): # Empty tree ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Add 10 trees with some weights. @@ -959,7 +959,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): dropout_info_center[0][num_dropped_center - 1]) def testDropoutSeed(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Add 10 trees with some weights. for i in range(0, 999): @@ -1032,7 +1032,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): len(dropout_info_4.eval()[0]) + 1, len(dropout_info_1.eval()[0])) def testDropOutZeroProb(self): - with self.test_session(): + with self.cached_session(): # Empty tree ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Add 1000 trees with some weights. @@ -1075,7 +1075,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllClose(result.eval(), result_no_dropout.eval()) def testAveragingAllTrees(self): - with self.test_session(): + with self.cached_session(): # Empty tree ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() adjusted_tree_ensemble_config = ( @@ -1139,7 +1139,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(dropout_info.eval(), pattern_dropout_info.eval()) def testAveragingSomeTrees(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() adjusted_tree_ensemble_config = ( tree_config_pb2.DecisionTreeEnsembleConfig()) @@ -1220,7 +1220,7 @@ class PredictionOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual(dropout_info_2.eval(), pattern_dropout_info.eval()) def testAverageMoreThanNumTreesExist(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() adjusted_tree_ensemble_config = ( tree_config_pb2.DecisionTreeEnsembleConfig()) @@ -1309,7 +1309,7 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase): self._sparse_int_shape1 = np.array([2, 2]) def testEnsembleEmpty(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1329,7 +1329,7 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([0, 0], result.eval()) def testTreeNonFinalized(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Depth 3 tree. tree1 = tree_ensemble_config.trees.add() @@ -1364,7 +1364,7 @@ class PartitionExamplesOpsTest(test_util.TensorFlowTestCase): self.assertAllEqual([5, 3], result.eval()) def testTreeFinalized(self): - with self.test_session(): + with self.cached_session(): tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() # Depth 3 tree. tree1 = tree_ensemble_config.trees.add() diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py index cddb159f82..848c42b686 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/quantile_ops_test.py @@ -77,7 +77,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): example_weights = constant_op.constant( [10, 1, 1, 1, 1, 1], dtype=dtypes.float32) - with self.test_session(): + with self.cached_session(): config = self._gen_config(0.33, 3) dense_buckets, sparse_buckets = quantile_ops.quantile_buckets( [dense_float_tensor_0], [sparse_indices_0, sparse_indices_m], @@ -107,7 +107,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): """ num_quantiles = 3 - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = quantile_ops.QuantileAccumulator( init_stamp_token=0, num_quantiles=num_quantiles, epsilon=0.001, name="q1") @@ -119,7 +119,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): column=input_column, example_weights=weights) - with self.test_session() as sess: + with self.cached_session() as sess: for i in range(1, 23): # start = 1, 2, 4, 7, 11, 16 ... (see comment above) start = int((i * (i-1) / 2) + 1) @@ -127,7 +127,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): {input_column: range(start, start+i), weights: [1] * i}) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1)) are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1)) buckets, are_ready_flush = (sess.run( @@ -142,7 +142,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): num_quantiles = 3 # set generate_quantiles to True since the test will generate fewer # boundaries otherwise. - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = quantile_ops.QuantileAccumulator( init_stamp_token=0, num_quantiles=num_quantiles, epsilon=0.001, name="q1", generate_quantiles=True) @@ -154,7 +154,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): column=input_column, example_weights=weights) - with self.test_session() as sess: + with self.cached_session() as sess: # This input is generated by integer in the range [2030, 2060] # but represented by with float16 precision. Integers <= 2048 are # exactly represented, whereas numbers > 2048 are rounded; and hence @@ -174,7 +174,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): {input_column: inputs, weights: [1] * len(inputs)}) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1)) are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1)) buckets, are_ready_flush = (sess.run( @@ -189,7 +189,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): # set generate_quantiles to True since the test will generate fewer # boundaries otherwise. - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = quantile_ops.QuantileAccumulator( init_stamp_token=0, num_quantiles=num_quantiles, epsilon=0.001, name="q1", generate_quantiles=True) @@ -201,12 +201,12 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): column=input_column, example_weights=weights) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(update, {input_column: inputs, weights: [1] * len(inputs)}) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1)) are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1)) buckets, are_ready_flush = (sess.run( @@ -265,7 +265,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): [9900 9901 .. 9999] All the batches have 1 for all the example weights. """ - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = quantile_ops.QuantileAccumulator( init_stamp_token=0, num_quantiles=3, epsilon=0.01, name="q1") resources.initialize_resources(resources.shared_resources()).run() @@ -275,7 +275,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): stamp_token=0, column=dense_placeholder, example_weights=weight_placeholder) - with self.test_session() as sess: + with self.cached_session() as sess: for i in range(100): dense_float = np.linspace( i * 100, (i + 1) * 100 - 1, num=100).reshape(-1, 1) @@ -284,7 +284,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): weight_placeholder: np.ones(shape=(100, 1), dtype=np.float32) }) - with self.test_session() as sess: + with self.cached_session() as sess: sess.run(accumulator.flush(stamp_token=0, next_stamp_token=1)) are_ready_flush, buckets = (accumulator.get_buckets(stamp_token=1)) buckets, are_ready_flush = (sess.run([buckets, are_ready_flush])) @@ -301,7 +301,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): [9900 9901 .. 9999] All the batches have 1 for all the example weights. """ - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = quantile_ops.QuantileAccumulator( init_stamp_token=0, num_quantiles=3, epsilon=0.01, name="q1") accumulator_2 = quantile_ops.QuantileAccumulator( @@ -313,7 +313,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): stamp_token=0, column=dense_placeholder, example_weights=weight_placeholder) - with self.test_session() as sess: + with self.cached_session() as sess: for i in range(100): dense_float = np.linspace( i * 100, (i + 1) * 100 - 1, num=100).reshape(-1, 1) @@ -322,7 +322,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): weight_placeholder: np.ones(shape=(100, 1), dtype=np.float32) }) - with self.test_session() as sess: + with self.cached_session() as sess: summary = sess.run( accumulator.flush_summary(stamp_token=0, next_stamp_token=1)) sess.run( @@ -438,7 +438,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): [1] * (int(math.pow(2, 16)) + 1), dtype=dtypes.float32) config = self._gen_config(0.1, 10) - with self.test_session(): + with self.cached_session(): dense_buckets, _ = quantile_ops.quantile_buckets( [dense_float_tensor_0], [], [], [], example_weights=example_weights, @@ -464,7 +464,7 @@ class QuantileBucketsOpTest(test_util.TensorFlowTestCase): config = self._gen_config(0.1, 10) - with self.test_session(): + with self.cached_session(): dense_buckets, _ = quantile_ops.quantile_buckets( [dense_float_tensor_0], [], [], [], example_weights=example_weights, @@ -533,7 +533,7 @@ class QuantilesOpTest(test_util.TensorFlowTestCase): self._sparse_thresholds_m = [1, 2, 1000] def testDenseFeaturesOnly(self): - with self.test_session(): + with self.cached_session(): dense_quantiles, _ = quantile_ops.quantiles( [self._dense_float_tensor_0, self._dense_float_tensor_1], [], [self._dense_thresholds_0, self._dense_thresholds_1], [], []) @@ -546,7 +546,7 @@ class QuantilesOpTest(test_util.TensorFlowTestCase): dense_quantiles[1].eval()) def testSparseFeaturesOnly(self): - with self.test_session(): + with self.cached_session(): _, sparse_quantiles = quantile_ops.quantiles([], [ self._sparse_values_0, self._sparse_values_1, self._sparse_values_2, self._sparse_values_m @@ -571,7 +571,7 @@ class QuantilesOpTest(test_util.TensorFlowTestCase): sparse_quantiles[3].eval()) def testDenseAndSparseFeatures(self): - with self.test_session(): + with self.cached_session(): dense_quantiles, sparse_quantiles = quantile_ops.quantiles( [self._dense_float_tensor_0, self._dense_float_tensor_1], [ self._sparse_values_0, self._sparse_values_1, @@ -602,14 +602,14 @@ class QuantilesOpTest(test_util.TensorFlowTestCase): sparse_quantiles[3].eval()) def testBucketizeWithInputBoundaries(self): - with self.test_session(): + with self.cached_session(): buckets = quantile_ops.bucketize_with_input_boundaries( input=[1, 2, 3, 4, 5], boundaries=[3]) self.assertAllEqual([0, 0, 1, 1, 1], buckets.eval()) def testBucketizeWithInputBoundaries2(self): - with self.test_session(): + with self.cached_session(): boundaries = constant_op.constant([3], dtype=dtypes.float32) buckets = quantile_ops.bucketize_with_input_boundaries( input=[1, 2, 3, 4, 5], @@ -617,7 +617,7 @@ class QuantilesOpTest(test_util.TensorFlowTestCase): self.assertAllEqual([0, 0, 1, 1, 1], buckets.eval()) def testBucketizeWithInputBoundaries3(self): - with self.test_session(): + with self.cached_session(): b = array_ops.placeholder(dtypes.float32) buckets = quantile_ops.bucketize_with_input_boundaries( input=[1, 2, 3, 4, 5], diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py index 2589504762..5e62bad672 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/split_handler_ops_test.py @@ -33,7 +33,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeDenseSplit(self): """Tests split handler op.""" - with self.test_session() as sess: + with self.cached_session() as sess: # The data looks like the following after dividing by number of steps (2). # Gradients | Partition | Dense Quantile | # (1.2, 0.2) | 0 | 0 | @@ -111,7 +111,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeMulticlassDenseSplit(self): """Tests split handler op.""" - with self.test_session() as sess: + with self.cached_session() as sess: partition_ids = array_ops.constant([0, 0, 1], dtype=dtypes.int32) bucket_ids = array_ops.constant( [[0, 0], [1, 0], [1, 0]], dtype=dtypes.int64) @@ -153,7 +153,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeDenseSplitEmptyInputs(self): """Tests empty inputs op.""" - with self.test_session() as sess: + with self.cached_session() as sess: partition_ids = array_ops.constant([], dtype=dtypes.int32) bucket_ids = array_ops.constant([[]], dtype=dtypes.int64) gradients = array_ops.constant([]) @@ -183,7 +183,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeSparseSplit(self): """Tests split handler op.""" - with self.test_session() as sess: + with self.cached_session() as sess: # The data looks like the following after dividing by number of steps (2). # Gradients | Partition | bucket ID | # (0.9, 0.39) | 0 | -1 | @@ -274,7 +274,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeSparseSplitAllEmptyDimensions(self): """Tests split handler op when all dimensions have only bias bucket id.""" - with self.test_session() as sess: + with self.cached_session() as sess: # The data looks like the following after dividing by number of steps (2). # Gradients | Partition | Dimension | bucket ID | # (0.9, 0.39) | 0 | 0 | -1 | @@ -307,7 +307,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeSparseMultidimensionalSplit(self): """Tests split handler op.""" - with self.test_session() as sess: + with self.cached_session() as sess: # Num of steps is 2. # The feature column is three dimensional. # First dimension has bias bucket only, the second has bias bucket and @@ -408,7 +408,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): """Tests default direction is stable when no sparsity.""" random.seed(1123) for _ in range(50): - with self.test_session() as sess: + with self.cached_session() as sess: grad = random.random() hessian = random.random() # The data looks like the following (divide by the num of steps 2). @@ -465,7 +465,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeMulticlassSparseSplit(self): """Tests split handler op.""" - with self.test_session() as sess: + with self.cached_session() as sess: partition_ids = array_ops.constant([0, 0, 0, 1, 1], dtype=dtypes.int32) bucket_ids = array_ops.constant( [[-1, 0], [0, 0], [1, 0], [-1, 0], [1, 0]], dtype=dtypes.int64) @@ -514,7 +514,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeCategoricalEqualitySplit(self): """Tests split handler op for categorical equality split.""" - with self.test_session() as sess: + with self.cached_session() as sess: # The data looks like the following after dividing by number of steps (2). # Gradients | Partition | Feature ID | # (0.9, 0.39) | 0 | -1 | @@ -608,7 +608,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): def testMakeMulticlassCategoricalEqualitySplit(self): """Tests split handler op for categorical equality split in multiclass.""" - with self.test_session() as sess: + with self.cached_session() as sess: gradients = array_ops.constant([[1.8, 3.5], [2.4, 1.0], [0.4, 4.0], [9.0, 3.1], [3.0, 0.8]]) @@ -655,7 +655,7 @@ class SplitHandlerOpsTest(test_util.TensorFlowTestCase): self.assertEqual(1, split_node.feature_id) def testMakeCategoricalEqualitySplitEmptyInput(self): - with self.test_session() as sess: + with self.cached_session() as sess: gradients = [] hessians = [] partition_ids = [] diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py index 978bf530cd..05ce0884cc 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/stats_accumulator_ops_test.py @@ -29,7 +29,7 @@ class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase): """Tests for scalar gradients and hessians accumulator.""" def testSimpleAcculumator(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.scalar(), @@ -57,7 +57,7 @@ class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(2, 3, 0)], [0.3, 0.4]) def testMultidimensionalAcculumator(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.scalar(), @@ -86,7 +86,7 @@ class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(2, 3, 1)], [0.1, 0.2]) def testDropStaleUpdate(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.scalar(), @@ -118,7 +118,7 @@ class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(2, 3, 0)], [0.3, 0.4]) def testSerialize(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.scalar(), @@ -159,7 +159,7 @@ class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase): self.assertEqual(0, stamp_token) def testDeserialize(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.scalar(), @@ -196,7 +196,7 @@ class StatsAccumulatorScalarTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(4, 6, 2)], [0.5, 0.7]) def testMakeSummary(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.scalar(), @@ -218,7 +218,7 @@ class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase): """Tests for tensor gradients and hessians accumulator.""" def testSimpleAcculumator(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.TensorShape([2]), @@ -256,7 +256,7 @@ class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(2, 3, 0)][1], [[0.05, 0.06], [0.07, 0.08]]) def testMultidimensionalAcculumator(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.TensorShape([2]), @@ -294,7 +294,7 @@ class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(2, 3, 1)][1], [[0.05, 0.06], [0.07, 0.08]]) def testDropStaleUpdate(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.TensorShape([2]), @@ -331,7 +331,7 @@ class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(2, 3, 0)][1], [[0.05, 0.06], [0.07, 0.08]]) def testSerialize(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.TensorShape([2]), @@ -381,7 +381,7 @@ class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase): self.assertAllEqual(result_1[2, 3, 0][1], result_2[2, 3, 0][1]) def testDeserialize(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.TensorShape([2]), @@ -425,7 +425,7 @@ class StatsAccumulatorTensorTest(test_util.TensorFlowTestCase): self.assertAllClose(result[(4, 5, 0)][1], [[0.07, 0.08], [0.09, 0.10]]) def testMakeSummary(self): - with self.test_session() as sess: + with self.cached_session() as sess: accumulator = stats_accumulator_ops.StatsAccumulator( stamp_token=0, gradient_shape=tensor_shape.TensorShape([2]), diff --git a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py index 572717e216..278dc1f756 100644 --- a/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py +++ b/tensorflow/contrib/boosted_trees/python/kernel_tests/training_ops_test.py @@ -146,7 +146,7 @@ class CenterTreeEnsembleBiasOpTest(test_util.TensorFlowTestCase): def testCenterBias(self): """Tests bias centering for multiple iterations.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -297,7 +297,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEmptyEnsemble(self): """Test growing an empty ensemble.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -516,7 +516,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowExistingEnsembleTreeNotFinalized(self): """Test growing an existing ensemble with the last tree not finalized.""" - with self.test_session() as session: + with self.cached_session() as session: # Create existing ensemble with one root split tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge(""" @@ -707,7 +707,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowExistingEnsembleTreeFinalized(self): """Test growing an existing ensemble with the last tree finalized.""" - with self.test_session() as session: + with self.cached_session() as session: # Create existing ensemble with one root split tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge(""" @@ -890,7 +890,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsemblePrePrune(self): """Test growing an ensemble with pre-pruning.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -957,7 +957,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsemblePostPruneNone(self): """Test growing an empty ensemble.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1065,7 +1065,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsemblePostPruneAll(self): """Test growing an ensemble with post-pruning.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1216,7 +1216,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsemblePostPrunePartial(self): """Test growing an ensemble with post-pruning.""" - with self.test_session() as session: + with self.cached_session() as session: # Create empty ensemble. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() tree_ensemble_handle = model_ops.tree_ensemble_variable( @@ -1419,7 +1419,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowEnsembleTreeLayerByLayer(self): """Test growing an existing ensemble with the last tree not finalized.""" - with self.test_session() as session: + with self.cached_session() as session: # Create existing ensemble with one root split tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge(""" @@ -1799,7 +1799,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowExistingEnsembleTreeFinalizedWithDropout(self): """Test growing an existing ensemble with the last tree finalized.""" - with self.test_session() as session: + with self.cached_session() as session: # Create existing ensemble with one root split and one bias tree. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge(""" @@ -1924,7 +1924,7 @@ class GrowTreeEnsembleOpTest(test_util.TensorFlowTestCase): def testGrowExistingEnsembleTreeWithFeatureSelectionUsedHandlers(self): """Test growing a tree with feature selection.""" - with self.test_session() as session: + with self.cached_session() as session: # Create existing ensemble with one root split and one bias tree. tree_ensemble_config = tree_config_pb2.DecisionTreeEnsembleConfig() text_format.Merge(""" diff --git a/tensorflow/contrib/framework/python/ops/arg_scope_test.py b/tensorflow/contrib/framework/python/ops/arg_scope_test.py index bcafc1a328..0e6c6f0e2f 100644 --- a/tensorflow/contrib/framework/python/ops/arg_scope_test.py +++ b/tensorflow/contrib/framework/python/ops/arg_scope_test.py @@ -52,7 +52,7 @@ def _key_op(op): class ArgScopeTest(test.TestCase): def testEmptyArgScope(self): - with self.test_session(): + with self.cached_session(): with arg_scope([]) as sc: self.assertEqual(sc, {}) @@ -60,7 +60,7 @@ class ArgScopeTest(test.TestCase): func1_kwargs = {'a': 1, 'b': None, 'c': [1]} key_op = _key_op(func1) func1_scope = {key_op: func1_kwargs.copy()} - with self.test_session(): + with self.cached_session(): with arg_scope([func1], a=1, b=None, c=[1]) as sc1: self.assertEqual(sc1, func1_scope) with arg_scope({}) as sc2: @@ -86,7 +86,7 @@ class ArgScopeTest(test.TestCase): func1_kwargs = {'a': 1, 'b': None, 'c': [1]} key_op = _key_op(func1) current_scope = {key_op: func1_kwargs.copy()} - with self.test_session(): + with self.cached_session(): with arg_scope([func1], a=1, b=None, c=[1]) as scope: self.assertDictEqual(scope, current_scope) @@ -102,7 +102,7 @@ class ArgScopeTest(test.TestCase): key(func1): func1_kwargs.copy(), key(func2): func2_kwargs.copy() } - with self.test_session(): + with self.cached_session(): with arg_scope([func1], a=1, b=None, c=[1]): with arg_scope([func2], b=2, d=[2]) as scope: self.assertDictEqual(scope, current_scope) @@ -111,7 +111,7 @@ class ArgScopeTest(test.TestCase): func1_kwargs = {'a': 1, 'b': None, 'c': [1]} key_op = _key_op(func1) current_scope = {key_op: func1_kwargs.copy()} - with self.test_session(): + with self.cached_session(): with arg_scope([func1], a=1, b=None, c=[1]) as scope1: pass with arg_scope(scope1) as scope: @@ -126,7 +126,7 @@ class ArgScopeTest(test.TestCase): key(func1): func1_kwargs.copy(), key(func2): func2_kwargs.copy() } - with self.test_session(): + with self.cached_session(): with arg_scope([func1], a=1, b=None, c=[1]) as scope1: with arg_scope([func2], b=2, d=[2]) as scope2: pass @@ -140,7 +140,7 @@ class ArgScopeTest(test.TestCase): def testSimpleArgScope(self): func1_args = (0,) func1_kwargs = {'a': 1, 'b': None, 'c': [1]} - with self.test_session(): + with self.cached_session(): with arg_scope([func1], a=1, b=None, c=[1]): args, kwargs = func1(0) self.assertTupleEqual(args, func1_args) @@ -149,7 +149,7 @@ class ArgScopeTest(test.TestCase): def testSimpleArgScopeWithTuple(self): func1_args = (0,) func1_kwargs = {'a': 1, 'b': None, 'c': [1]} - with self.test_session(): + with self.cached_session(): with arg_scope((func1,), a=1, b=None, c=[1]): args, kwargs = func1(0) self.assertTupleEqual(args, func1_args) @@ -240,7 +240,7 @@ class ArgScopeTest(test.TestCase): def testAddArgScopeRaceCondition(self): func4_kwargs = ('a', 'b', 'c', 'd', 'e', 'f', 'g', 'h') for i in range(4): - # redefine the function with different args + # redefine the function with different args @add_arg_scope def func4(a=1, b=2, c=3, d=4, e=5, f=6, g=7, h=8): pass diff --git a/tensorflow/contrib/framework/python/ops/checkpoint_ops_test.py b/tensorflow/contrib/framework/python/ops/checkpoint_ops_test.py index b7b9f5c59e..4036c87b6d 100644 --- a/tensorflow/contrib/framework/python/ops/checkpoint_ops_test.py +++ b/tensorflow/contrib/framework/python/ops/checkpoint_ops_test.py @@ -50,7 +50,7 @@ class LoadMulticlassBiasTest(test.TestCase): bias = variables.Variable( array_ops.reshape(flat_data, (num, dim)), name='bias') save = saver.Saver([bias]) - with self.test_session() as sess: + with self.cached_session() as sess: variables.global_variables_initializer().run() self.bundle_file = os.path.join(test.get_temp_dir(), 'bias_checkpoint') save.save(sess, self.bundle_file) @@ -90,7 +90,7 @@ class LoadMulticlassBiasTest(test.TestCase): initializer=bias_loading_initializer, partitioner=partitioned_variables.fixed_size_partitioner(3)) - with self.test_session(): + with self.cached_session(): variables.global_variables_initializer().run() self.assertAllClose(expected_remapped_bias_vector, remapped_bias_vector.as_tensor().eval()) @@ -109,7 +109,7 @@ class LoadVariableSlotTest(test.TestCase): accum = variables.Variable( array_ops.reshape(flat_data, (num, dim)), name='accum') save = saver.Saver([accum]) - with self.test_session() as sess: + with self.cached_session() as sess: variables.global_variables_initializer().run() self.bundle_file = os.path.join(test.get_temp_dir(), 'accum_checkpoint') save.save(sess, self.bundle_file) @@ -179,7 +179,7 @@ class LoadVariableSlotTest(test.TestCase): shape=[2, 1], initializer=variable_slot_initializer_part_1) - with self.test_session(): + with self.cached_session(): variables.global_variables_initializer().run() self.assertAllClose(expected_remapped_accum_vector_part_0, remapped_accum_vector_part_0.eval()) diff --git a/tensorflow/contrib/framework/python/ops/prettyprint_ops_test.py b/tensorflow/contrib/framework/python/ops/prettyprint_ops_test.py index 50bcbe625d..c104c51fef 100644 --- a/tensorflow/contrib/framework/python/ops/prettyprint_ops_test.py +++ b/tensorflow/contrib/framework/python/ops/prettyprint_ops_test.py @@ -34,7 +34,7 @@ class PrettyPrintOpsTest(test.TestCase): def testPrintTensorPassthrough(self): a = constant_op.constant([1]) a = prettyprint_ops.print_op(a) - with self.test_session(): + with self.cached_session(): self.assertEqual(a.eval(), constant_op.constant([1]).eval()) def testPrintSparseTensorPassthrough(self): @@ -43,7 +43,7 @@ class PrettyPrintOpsTest(test.TestCase): b = sparse_tensor.SparseTensor( indices=[[0, 0], [1, 2]], values=[1, 2], dense_shape=[3, 4]) a = prettyprint_ops.print_op(a) - with self.test_session(): + with self.cached_session(): self.assertAllEqual( sparse_ops.sparse_tensor_to_dense(a).eval(), sparse_ops.sparse_tensor_to_dense(b).eval()) @@ -54,13 +54,13 @@ class PrettyPrintOpsTest(test.TestCase): a = a.write(1, 1) a = a.write(0, 0) a = prettyprint_ops.print_op(a) - with self.test_session(): + with self.cached_session(): self.assertAllEqual(a.stack().eval(), constant_op.constant([0, 1]).eval()) def testPrintVariable(self): a = variables.Variable(1.0) a = prettyprint_ops.print_op(a) - with self.test_session(): + with self.cached_session(): variables.global_variables_initializer().run() a.eval() diff --git a/tensorflow/contrib/framework/python/ops/sort_ops_test.py b/tensorflow/contrib/framework/python/ops/sort_ops_test.py index a8fb94b245..791b32cd1e 100644 --- a/tensorflow/contrib/framework/python/ops/sort_ops_test.py +++ b/tensorflow/contrib/framework/python/ops/sort_ops_test.py @@ -48,7 +48,7 @@ class SortTest(test.TestCase): sort_axis = np.random.choice(rank) if negative_axis: sort_axis = -1 - sort_axis - with self.test_session(): + with self.cached_session(): self.assertAllEqual( np.sort(arr, axis=sort_axis), sort_ops.sort(constant_op.constant(arr), axis=sort_axis).eval()) @@ -60,7 +60,7 @@ class SortTest(test.TestCase): shape = [np.random.randint(1, 4) for _ in range(rank)] arr = np.random.random(shape) sort_axis = np.random.choice(rank) - with self.test_session(): + with self.cached_session(): self.assertAllEqual( np.sort(arr, axis=sort_axis), sort_ops.sort(constant_op.constant(arr), axis=sort_axis).eval()) @@ -73,7 +73,7 @@ class SortTest(test.TestCase): scalar = array_ops.zeros(zeros_length_1) sort = sort_ops.sort(scalar) - with self.test_session(): + with self.cached_session(): with self.assertRaises(errors.InvalidArgumentError): sort.eval() @@ -84,7 +84,7 @@ class SortTest(test.TestCase): def testDescending(self): arr = np.random.random((10, 5, 5)) - with self.test_session(): + with self.cached_session(): self.assertAllEqual( np.sort(arr, axis=0)[::-1], sort_ops.sort( @@ -111,7 +111,7 @@ class SortTest(test.TestCase): def testArgsort_1d(self): arr = np.random.random(42) - with self.test_session(): + with self.cached_session(): self.assertAllEqual( np.sort(arr), array_ops.gather(arr, sort_ops.argsort(arr)).eval()) @@ -119,7 +119,7 @@ class SortTest(test.TestCase): def testArgsort(self): arr = np.random.random((5, 6, 7, 8)) for axis in range(4): - with self.test_session(): + with self.cached_session(): self.assertAllEqual( np.argsort(arr, axis=axis), sort_ops.argsort(arr, axis=axis).eval()) diff --git a/tensorflow/contrib/framework/python/ops/variables_test.py b/tensorflow/contrib/framework/python/ops/variables_test.py index 3c44630a51..f9b0efd1da 100644 --- a/tensorflow/contrib/framework/python/ops/variables_test.py +++ b/tensorflow/contrib/framework/python/ops/variables_test.py @@ -45,7 +45,7 @@ from tensorflow.python.training import saver as saver_lib class LocalVariableTest(test.TestCase): def test_local_variable(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEquals([], variables_lib.local_variables()) value0 = 42 variables_lib2.local_variable(value0) @@ -58,7 +58,7 @@ class LocalVariableTest(test.TestCase): self.assertAllEqual(set([value0, value1]), set(sess.run(variables))) def testLocalVariableNameAndShape(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.local_variable([1, 1, 1, 1, 1], name='a') self.assertEquals(a.op.name, 'A/a') @@ -66,21 +66,21 @@ class LocalVariableTest(test.TestCase): self.assertListEqual([a], variables_lib2.get_local_variables()) def testLocalVariableNotInAllVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.local_variable(0) self.assertFalse(a in variables_lib.global_variables()) self.assertTrue(a in variables_lib.local_variables()) def testLocalVariableNotInVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.local_variable(0) self.assertFalse(a in variables_lib2.get_variables_to_restore()) self.assertTrue(a in variables_lib.local_variables()) def testGetVariablesDontReturnsTransients(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): variables_lib2.local_variable(0) with variable_scope.variable_scope('B'): @@ -89,7 +89,7 @@ class LocalVariableTest(test.TestCase): self.assertEquals([], variables_lib2.get_variables('B')) def testGetLocalVariablesReturnsTransients(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.local_variable(0) with variable_scope.variable_scope('B'): @@ -98,7 +98,7 @@ class LocalVariableTest(test.TestCase): self.assertEquals([b], variables_lib2.get_local_variables('B')) def testInitializedVariableValue(self): - with self.test_session() as sess: + with self.cached_session() as sess: a = variables_lib2.local_variable([0, 0, 0, 0, 0], name='a') sess.run(variables_lib.local_variables_initializer()) self.assertAllEqual(a.eval(), [0] * 5) @@ -114,7 +114,7 @@ class LocalVariableTest(test.TestCase): class GlobalVariableTest(test.TestCase): def test_global_variable(self): - with self.test_session() as sess: + with self.cached_session() as sess: self.assertEquals([], variables_lib.global_variables()) value0 = 42 variables_lib2.global_variable(value0) @@ -129,7 +129,7 @@ class GlobalVariableTest(test.TestCase): self.assertAllEqual(set([value0, value1]), set(sess.run(variables))) def testVariableNameAndShape(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.global_variable([1, 1, 1, 1, 1], name='a') self.assertEquals(a.op.name, 'A/a') @@ -137,21 +137,21 @@ class GlobalVariableTest(test.TestCase): self.assertListEqual([a], variables_lib.global_variables()) def testGlobalVariableNotInLocalVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.global_variable(0) self.assertFalse(a in variables_lib.local_variables()) self.assertTrue(a in variables_lib.global_variables()) def testGlobalVariableInVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.global_variable(0) self.assertFalse(a in variables_lib.local_variables()) self.assertTrue(a in variables_lib2.get_variables_to_restore()) def testGetVariablesReturnsThem(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.global_variable(0) with variable_scope.variable_scope('B'): @@ -160,7 +160,7 @@ class GlobalVariableTest(test.TestCase): self.assertEquals([b], variables_lib2.get_variables('B')) def testGetLocalVariablesDontReturnsThem(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): variables_lib2.global_variable(0) with variable_scope.variable_scope('B'): @@ -169,7 +169,7 @@ class GlobalVariableTest(test.TestCase): self.assertEquals([], variables_lib2.get_local_variables('B')) def testInitializedVariableValue(self): - with self.test_session() as sess: + with self.cached_session() as sess: a = variables_lib2.global_variable([0, 0, 0, 0, 0], name='a') sess.run(variables_lib.global_variables_initializer()) self.assertAllEqual(a.eval(), [0] * 5) @@ -249,7 +249,7 @@ class GlobalStepTest(test.TestCase): class VariablesTest(test.TestCase): def testCreateVariable(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) self.assertEquals(a.op.name, 'A/a') @@ -259,7 +259,7 @@ class VariablesTest(test.TestCase): self.assertFalse(a in variables_lib.local_variables()) def testGetVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) with variable_scope.variable_scope('B'): @@ -269,7 +269,7 @@ class VariablesTest(test.TestCase): self.assertEquals([b], variables_lib2.get_variables('B')) def testGetVariablesWithScope(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A') as var_scope: a = variables_lib2.variable('a', [5]) b = variables_lib2.variable('b', [5]) @@ -277,7 +277,7 @@ class VariablesTest(test.TestCase): set([a, b]), set(variables_lib2.get_variables(var_scope))) def testGetVariablesSuffix(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) with variable_scope.variable_scope('A'): @@ -286,13 +286,13 @@ class VariablesTest(test.TestCase): self.assertEquals([b], variables_lib2.get_variables(suffix='b')) def testGetVariableWithSingleVar(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('parent'): a = variables_lib2.variable('child', [5]) self.assertEquals(a, variables_lib2.get_unique_variable('parent/child')) def testGetVariableWithDistractors(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('parent'): a = variables_lib2.variable('child', [5]) with variable_scope.variable_scope('child'): @@ -302,13 +302,13 @@ class VariablesTest(test.TestCase): def testGetVariableThrowsExceptionWithNoMatch(self): var_name = 'cant_find_me' - with self.test_session(): + with self.cached_session(): with self.assertRaises(ValueError): variables_lib2.get_unique_variable(var_name) def testGetThrowsExceptionWithChildrenButNoMatch(self): var_name = 'parent/child' - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope(var_name): variables_lib2.variable('grandchild1', [7]) variables_lib2.variable('grandchild2', [9]) @@ -316,7 +316,7 @@ class VariablesTest(test.TestCase): variables_lib2.get_unique_variable(var_name) def testGetVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) with variable_scope.variable_scope('B'): @@ -324,7 +324,7 @@ class VariablesTest(test.TestCase): self.assertEquals([a, b], variables_lib2.get_variables_to_restore()) def testIncludeGetVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) with variable_scope.variable_scope('B'): @@ -333,7 +333,7 @@ class VariablesTest(test.TestCase): self.assertEquals([a], variables_lib2.get_variables_to_restore(['A'])) def testExcludeGetVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) with variable_scope.variable_scope('B'): @@ -343,7 +343,7 @@ class VariablesTest(test.TestCase): [a], variables_lib2.get_variables_to_restore(exclude=['B'])) def testWrongIncludeGetVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) with variable_scope.variable_scope('B'): @@ -352,7 +352,7 @@ class VariablesTest(test.TestCase): self.assertEquals([], variables_lib2.get_variables_to_restore(['a'])) def testGetMixedVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) b = variables_lib2.variable('b', [5]) @@ -365,7 +365,7 @@ class VariablesTest(test.TestCase): variables_lib2.get_variables_to_restore(include=['A/a', 'B/c'])) def testExcludeGetMixedVariablesToRestore(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) b = variables_lib2.variable('b', [5]) @@ -378,7 +378,7 @@ class VariablesTest(test.TestCase): variables_lib2.get_variables_to_restore(exclude=['A/a', 'B/c'])) def testReuseVariable(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', []) with variable_scope.variable_scope('A', reuse=True): @@ -387,14 +387,14 @@ class VariablesTest(test.TestCase): self.assertListEqual([a], variables_lib2.get_variables()) def testVariableWithRegularizer(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [], regularizer=nn_ops.l2_loss) loss = ops.get_collection(ops.GraphKeys.REGULARIZATION_LOSSES)[0] self.assertDeviceEqual(loss.device, a.device) def testVariableWithRegularizerColocate(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable( 'a', [], device='gpu:0', regularizer=nn_ops.l2_loss) @@ -402,7 +402,7 @@ class VariablesTest(test.TestCase): self.assertDeviceEqual(loss.device, a.device) def testVariableWithDevice(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [], device='cpu:0') b = variables_lib2.variable('b', [], device='cpu:1') @@ -410,7 +410,7 @@ class VariablesTest(test.TestCase): self.assertDeviceEqual(b.device, 'cpu:1') def testVariableWithDeviceFromScope(self): - with self.test_session(): + with self.cached_session(): with ops.device('/cpu:0'): a = variables_lib2.variable('a', []) b = variables_lib2.variable('b', [], device='cpu:1') @@ -428,7 +428,7 @@ class VariablesTest(test.TestCase): self.counter += 1 return 'cpu:%d' % self.counter - with self.test_session(): + with self.cached_session(): with arg_scope([variables_lib2.variable], device=DevFn()): a = variables_lib2.variable('a', []) b = variables_lib2.variable('b', []) @@ -453,7 +453,7 @@ class VariablesTest(test.TestCase): self.assertDeviceEqual(e.initial_value.device, 'cpu:99') def testVariableWithReplicaDeviceSetter(self): - with self.test_session(): + with self.cached_session(): with ops.device(device_setter.replica_device_setter(ps_tasks=2)): a = variables_lib2.variable('a', []) b = variables_lib2.variable('b', []) @@ -570,7 +570,7 @@ class VariablesTest(test.TestCase): class ModelVariablesTest(test.TestCase): def testNameAndShape(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.model_variable('a', [5]) self.assertEquals(a.op.name, 'A/a') @@ -578,7 +578,7 @@ class ModelVariablesTest(test.TestCase): self.assertListEqual([a], variables_lib2.get_model_variables('A')) def testNotInLocalVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.model_variable('a', [5]) self.assertTrue(a in variables_lib.global_variables()) @@ -586,7 +586,7 @@ class ModelVariablesTest(test.TestCase): self.assertFalse(a in variables_lib.local_variables()) def testGetVariablesReturns(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.model_variable('a', [5]) with variable_scope.variable_scope('B'): @@ -595,7 +595,7 @@ class ModelVariablesTest(test.TestCase): self.assertEquals([b], variables_lib2.get_variables('B')) def testGetModelVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.model_variable('a', [5]) with variable_scope.variable_scope('B'): @@ -604,7 +604,7 @@ class ModelVariablesTest(test.TestCase): self.assertEquals([b], variables_lib2.get_model_variables('B')) def testGetTrainableVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): variables_lib2.local_variable([5]) a = variables_lib.Variable([5]) @@ -615,7 +615,7 @@ class ModelVariablesTest(test.TestCase): self.assertEquals([b], variables_lib2.get_trainable_variables('B')) def testGetLocalVariables(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): _ = variables_lib2.model_variable('a', [5]) with variable_scope.variable_scope('B'): @@ -624,7 +624,7 @@ class ModelVariablesTest(test.TestCase): self.assertEquals([], variables_lib2.get_local_variables('B')) def testInitializedVariableValue(self): - with self.test_session() as sess: + with self.cached_session() as sess: a = variables_lib2.model_variable( 'a', [5], initializer=init_ops.ones_initializer()) sess.run(variables_lib.global_variables_initializer()) @@ -670,14 +670,14 @@ class ModelVariablesTest(test.TestCase): class GetVariablesCollections(test.TestCase): def testVariableCollection(self): - with self.test_session(): + with self.cached_session(): a = variables_lib2.variable('a', [], collections='A') b = variables_lib2.variable('b', [], collections='B') self.assertEquals(a, ops.get_collection('A')[0]) self.assertEquals(b, ops.get_collection('B')[0]) def testVariableCollections(self): - with self.test_session(): + with self.cached_session(): a = variables_lib2.variable('a', [], collections=['A', 'C']) b = variables_lib2.variable('b', [], collections=['B', 'C']) self.assertEquals(a, ops.get_collection('A')[0]) @@ -685,14 +685,14 @@ class GetVariablesCollections(test.TestCase): self.assertListEqual([a, b], ops.get_collection('C')) def testVariableCollectionsWithArgScope(self): - with self.test_session(): + with self.cached_session(): with arg_scope([variables_lib2.variable], collections='A'): a = variables_lib2.variable('a', []) b = variables_lib2.variable('b', []) self.assertListEqual([a, b], ops.get_collection('A')) def testVariableCollectionsWithArgScopeNested(self): - with self.test_session(): + with self.cached_session(): with arg_scope([variables_lib2.variable], collections='A'): a = variables_lib2.variable('a', []) with arg_scope([variables_lib2.variable], collections='B'): @@ -701,7 +701,7 @@ class GetVariablesCollections(test.TestCase): self.assertEquals(b, ops.get_collection('B')[0]) def testVariableCollectionsWithArgScopeNonNested(self): - with self.test_session(): + with self.cached_session(): with arg_scope([variables_lib2.variable], collections='A'): a = variables_lib2.variable('a', []) with arg_scope([variables_lib2.variable], collections='B'): @@ -711,7 +711,7 @@ class GetVariablesCollections(test.TestCase): self.assertListEqual([b], ops.get_collection('B')) def testVariableRestoreWithArgScopeNested(self): - with self.test_session(): + with self.cached_session(): a = variables_lib2.variable('a', []) with arg_scope( [variables_lib2.variable], trainable=False, collections=['A', 'B']): @@ -726,7 +726,7 @@ class GetVariablesCollections(test.TestCase): class GetVariablesBySuffixTest(test.TestCase): def testGetVariableGivenNameScoped(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) b = variables_lib2.variable('b', [5]) @@ -734,7 +734,7 @@ class GetVariablesBySuffixTest(test.TestCase): self.assertEquals([b], variables_lib2.get_variables_by_suffix('b')) def testGetVariableWithScope(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) fooa = variables_lib2.variable('fooa', [5]) @@ -748,7 +748,7 @@ class GetVariablesBySuffixTest(test.TestCase): self.assertEquals([a, fooa], matched_variables) def testGetVariableWithoutScope(self): - with self.test_session(): + with self.cached_session(): a = variables_lib2.variable('a', [5]) fooa = variables_lib2.variable('fooa', [5]) b_a = variables_lib2.variable('B/a', [5]) @@ -761,7 +761,7 @@ class GetVariablesBySuffixTest(test.TestCase): class GetVariablesByNameTest(test.TestCase): def testGetVariableGivenNameScoped(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) b = variables_lib2.variable('b', [5]) @@ -769,7 +769,7 @@ class GetVariablesByNameTest(test.TestCase): self.assertEquals([b], variables_lib2.get_variables_by_name('b')) def testGetVariableWithScope(self): - with self.test_session(): + with self.cached_session(): with variable_scope.variable_scope('A'): a = variables_lib2.variable('a', [5]) fooa = variables_lib2.variable('fooa', [5]) @@ -785,7 +785,7 @@ class GetVariablesByNameTest(test.TestCase): self.assertEquals([a], matched_variables) def testGetVariableWithoutScope(self): - with self.test_session(): + with self.cached_session(): a = variables_lib2.variable('a', [5]) fooa = variables_lib2.variable('fooa', [5]) b_a = variables_lib2.variable('B/a', [5]) @@ -818,7 +818,7 @@ class AssignFromValuesTest(test.TestCase): init_value0 = np.asarray([1.0, 3.0, 9.0]).reshape((1, 3, 1)) init_value1 = np.asarray([2.0, 4.0, 6.0, 8.0]).reshape((2, 1, 2)) - with self.test_session() as sess: + with self.cached_session() as sess: initializer = init_ops.truncated_normal_initializer(stddev=.1) var0 = variables_lib2.variable( 'my_var0', shape=[1, 3, 1], initializer=initializer) @@ -844,7 +844,7 @@ class AssignFromValuesTest(test.TestCase): init_value0 = np.asarray([1.0, 3.0, 9.0]).reshape((1, 3, 1)) init_value1 = np.asarray([2.0, 4.0, 6.0, 8.0]).reshape((2, 1, 2)) - with self.test_session() as sess: + with self.cached_session() as sess: initializer = init_ops.truncated_normal_initializer(stddev=.1) with variable_scope.variable_scope('my_model/my_layer0'): @@ -879,7 +879,7 @@ class AssignFromValuesFnTest(test.TestCase): init_value0 = np.asarray([1.0, 3.0, 9.0]).reshape((1, 3, 1)) init_value1 = np.asarray([2.0, 4.0, 6.0, 8.0]).reshape((2, 1, 2)) - with self.test_session() as sess: + with self.cached_session() as sess: initializer = init_ops.truncated_normal_initializer(stddev=.1) var0 = variables_lib2.variable( 'my_var0', shape=[1, 3, 1], initializer=initializer) @@ -904,7 +904,7 @@ class AssignFromValuesFnTest(test.TestCase): init_value0 = np.asarray([1.0, 3.0, 9.0]).reshape((1, 3, 1)) init_value1 = np.asarray([2.0, 4.0, 6.0, 8.0]).reshape((2, 1, 2)) - with self.test_session() as sess: + with self.cached_session() as sess: initializer = init_ops.truncated_normal_initializer(stddev=.1) with variable_scope.variable_scope('my_model/my_layer0'): @@ -968,7 +968,7 @@ class AssignFromCheckpointTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[]) @@ -998,7 +998,7 @@ class AssignFromCheckpointTest(test.TestCase): init_value1 = np.array([20.0]) # Partitioned into 1 part, edge case. var_names_to_values = {'var0': init_value0, 'var1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) # var0 and var1 are PartitionedVariables. @@ -1039,7 +1039,7 @@ class AssignFromCheckpointTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session(): + with self.cached_session(): model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[]) @@ -1062,7 +1062,7 @@ class AssignFromCheckpointTest(test.TestCase): var_names_to_values = {'layer0/v0': init_value0, 'layer1/v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) with variable_scope.variable_scope('my_model/my_layer0'): @@ -1123,7 +1123,7 @@ class AssignFromCheckpointFnTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[]) @@ -1154,7 +1154,7 @@ class AssignFromCheckpointFnTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[2, 1]) @@ -1183,7 +1183,7 @@ class AssignFromCheckpointFnTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[2, 1]) @@ -1213,7 +1213,7 @@ class AssignFromCheckpointFnTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[]) @@ -1241,7 +1241,7 @@ class AssignFromCheckpointFnTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('v0', shape=[]) @@ -1272,7 +1272,7 @@ class AssignFromCheckpointFnTest(test.TestCase): init_value1 = 20.0 var_names_to_values = {'v0': init_value0, 'v1': init_value1} - with self.test_session() as sess: + with self.cached_session() as sess: model_path = self.create_checkpoint_from_values(var_names_to_values, model_dir) var0 = variables_lib2.variable('my_var0', shape=[]) @@ -1299,7 +1299,7 @@ class ZeroInitializerOpTest(test.TestCase): def _testZeroInitializer(self, shape, initializer, use_init): var = variables_lib.Variable(initializer) var_zero = variables_lib2.zero_initializer(var) - with self.test_session() as sess: + with self.cached_session() as sess: with self.assertRaisesOpError('Attempting to use uninitialized value'): var.eval() if use_init: @@ -1324,7 +1324,7 @@ class ZeroVarInitializerOpTest(test.TestCase): var = resource_variable_ops.ResourceVariable(initializer) var_zero = variables_lib2.zero_initializer(var) - with self.test_session() as sess: + with self.cached_session() as sess: with self.assertRaisesOpError('Error while reading resource variable'): var.eval() if use_init: diff --git a/tensorflow/examples/adding_an_op/cuda_op_test.py b/tensorflow/examples/adding_an_op/cuda_op_test.py index 07390bc3bf..a9aaa81e3f 100644 --- a/tensorflow/examples/adding_an_op/cuda_op_test.py +++ b/tensorflow/examples/adding_an_op/cuda_op_test.py @@ -26,7 +26,7 @@ class AddOneTest(tf.test.TestCase): def test(self): if tf.test.is_built_with_cuda(): - with self.test_session(): + with self.cached_session(): result = cuda_op.add_one([5, 4, 3, 2, 1]) self.assertAllEqual(result.eval(), [6, 5, 4, 3, 2]) diff --git a/tensorflow/examples/adding_an_op/fact_test.py b/tensorflow/examples/adding_an_op/fact_test.py index f7f17e5180..11163e7ba5 100644 --- a/tensorflow/examples/adding_an_op/fact_test.py +++ b/tensorflow/examples/adding_an_op/fact_test.py @@ -24,7 +24,7 @@ import tensorflow as tf class FactTest(tf.test.TestCase): def test(self): - with self.test_session(): + with self.cached_session(): print(tf.user_ops.my_fact().eval()) diff --git a/tensorflow/examples/adding_an_op/zero_out_1_test.py b/tensorflow/examples/adding_an_op/zero_out_1_test.py index fac486100d..342d3a020c 100644 --- a/tensorflow/examples/adding_an_op/zero_out_1_test.py +++ b/tensorflow/examples/adding_an_op/zero_out_1_test.py @@ -28,7 +28,7 @@ from tensorflow.examples.adding_an_op import zero_out_op_1 class ZeroOut1Test(tf.test.TestCase): def test(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_1.zero_out([5, 4, 3, 2, 1]) self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0]) diff --git a/tensorflow/examples/adding_an_op/zero_out_2_test.py b/tensorflow/examples/adding_an_op/zero_out_2_test.py index 217bbbcffa..4504597817 100644 --- a/tensorflow/examples/adding_an_op/zero_out_2_test.py +++ b/tensorflow/examples/adding_an_op/zero_out_2_test.py @@ -29,17 +29,17 @@ from tensorflow.examples.adding_an_op import zero_out_op_2 class ZeroOut2Test(tf.test.TestCase): def test(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_2.zero_out([5, 4, 3, 2, 1]) self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0]) def test_2d(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_2.zero_out([[6, 5, 4], [3, 2, 1]]) self.assertAllEqual(result.eval(), [[6, 0, 0], [0, 0, 0]]) def test_grad(self): - with self.test_session(): + with self.cached_session(): shape = (5,) x = tf.constant([5, 4, 3, 2, 1], dtype=tf.float32) y = zero_out_op_2.zero_out(x) @@ -47,7 +47,7 @@ class ZeroOut2Test(tf.test.TestCase): self.assertLess(err, 1e-4) def test_grad_2d(self): - with self.test_session(): + with self.cached_session(): shape = (2, 3) x = tf.constant([[6, 5, 4], [3, 2, 1]], dtype=tf.float32) y = zero_out_op_2.zero_out(x) diff --git a/tensorflow/examples/adding_an_op/zero_out_3_test.py b/tensorflow/examples/adding_an_op/zero_out_3_test.py index 01280caf49..15d62495aa 100644 --- a/tensorflow/examples/adding_an_op/zero_out_3_test.py +++ b/tensorflow/examples/adding_an_op/zero_out_3_test.py @@ -26,23 +26,23 @@ from tensorflow.examples.adding_an_op import zero_out_op_3 class ZeroOut3Test(tf.test.TestCase): def test(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_3.zero_out([5, 4, 3, 2, 1]) self.assertAllEqual(result.eval(), [5, 0, 0, 0, 0]) def testAttr(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_3.zero_out([5, 4, 3, 2, 1], preserve_index=3) self.assertAllEqual(result.eval(), [0, 0, 0, 2, 0]) def testNegative(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_3.zero_out([5, 4, 3, 2, 1], preserve_index=-1) with self.assertRaisesOpError("Need preserve_index >= 0, got -1"): result.eval() def testLarge(self): - with self.test_session(): + with self.cached_session(): result = zero_out_op_3.zero_out([5, 4, 3, 2, 1], preserve_index=17) with self.assertRaisesOpError("preserve_index out of range"): result.eval() diff --git a/tensorflow/python/kernel_tests/random/random_crop_test.py b/tensorflow/python/kernel_tests/random/random_crop_test.py index 6028be1228..8ded522320 100644 --- a/tensorflow/python/kernel_tests/random/random_crop_test.py +++ b/tensorflow/python/kernel_tests/random/random_crop_test.py @@ -30,12 +30,12 @@ class RandomCropTest(test.TestCase): # No random cropping is performed since the size is value.shape. for shape in (2, 1, 1), (2, 1, 3), (4, 5, 3): value = np.arange(0, np.prod(shape), dtype=np.int32).reshape(shape) - with self.test_session(): + with self.cached_session(): crop = random_ops.random_crop(value, shape).eval() self.assertAllEqual(crop, value) def testContains(self): - with self.test_session(): + with self.cached_session(): shape = (3, 5, 7) target = (2, 3, 4) value = np.random.randint(1000000, size=shape) @@ -57,7 +57,7 @@ class RandomCropTest(test.TestCase): single = [1, 1, 1] value = np.arange(size).reshape(shape) - with self.test_session(): + with self.cached_session(): crop = random_ops.random_crop(value, single, seed=7) counts = np.zeros(size, dtype=np.int32) for _ in range(num_samples): diff --git a/tensorflow/python/kernel_tests/random/random_gamma_test.py b/tensorflow/python/kernel_tests/random/random_gamma_test.py index aa40228dc1..d969944493 100644 --- a/tensorflow/python/kernel_tests/random/random_gamma_test.py +++ b/tensorflow/python/kernel_tests/random/random_gamma_test.py @@ -256,7 +256,7 @@ class RandomGammaTest(test.TestCase): def testPositive(self): n = int(10e3) for dt in [dtypes.float16, dtypes.float32, dtypes.float64]: - with self.test_session(): + with self.cached_session(): x = random_ops.random_gamma(shape=[n], alpha=0.001, dtype=dt, seed=0) self.assertEqual(0, math_ops.reduce_sum(math_ops.cast( math_ops.less_equal(x, 0.), dtype=dtypes.int64)).eval()) diff --git a/tensorflow/python/kernel_tests/random/random_grad_test.py b/tensorflow/python/kernel_tests/random/random_grad_test.py index c1d455b785..d89056c485 100644 --- a/tensorflow/python/kernel_tests/random/random_grad_test.py +++ b/tensorflow/python/kernel_tests/random/random_grad_test.py @@ -49,7 +49,7 @@ class AddLeadingUnitDimensionsTest(test.TestCase): x = array_ops.placeholder(dtypes.float32) num_dimensions = array_ops.placeholder(dtypes.int32) ret = random_grad.add_leading_unit_dimensions(x, num_dimensions) - with self.test_session() as sess: + with self.cached_session() as sess: ret_val = sess.run(ret, {x: np.ones([2, 2]), num_dimensions: 2}) self.assertAllEqual(ret_val.shape, [1, 1, 2, 2]) @@ -99,7 +99,7 @@ class RandomGammaGradTest(test.TestCase): alpha_val = np.ones([1, 2]) beta_val = np.ones([2, 1]) - with self.test_session() as sess: + with self.cached_session() as sess: grads_alpha_val, grads_beta_val = sess.run( [grads_alpha, grads_beta], {alpha: alpha_val, beta: beta_val, shape: [2, 1]}) diff --git a/tensorflow/python/kernel_tests/random/random_poisson_test.py b/tensorflow/python/kernel_tests/random/random_poisson_test.py index afdf71e652..15ab95cdb7 100644 --- a/tensorflow/python/kernel_tests/random/random_poisson_test.py +++ b/tensorflow/python/kernel_tests/random/random_poisson_test.py @@ -137,7 +137,7 @@ class RandomPoissonTest(test.TestCase): self.assertGreaterEqual(np.linalg.norm(diff.eval()), 1) def testZeroShape(self): - with self.test_session(): + with self.cached_session(): rnd = random_ops.random_poisson([], [], seed=12345) self.assertEqual([0], rnd.get_shape().as_list()) self.assertAllClose(np.array([], dtype=np.float32), rnd.eval()) @@ -186,7 +186,7 @@ class RandomPoissonTest(test.TestCase): def testDTypeCombinationsV2(self): """Tests random_poisson_v2() for all supported dtype combinations.""" - with self.test_session(): + with self.cached_session(): for lam_dt in _SUPPORTED_DTYPES: for out_dt in _SUPPORTED_DTYPES: random_ops.random_poisson( diff --git a/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py b/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py index b7a79f239c..0d85a072d4 100644 --- a/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py +++ b/tensorflow/python/kernel_tests/random/random_shuffle_queue_test.py @@ -46,7 +46,7 @@ class RandomShuffleQueueTest(test.TestCase): tf_logging.error("Finished: %s", self._testMethodName) def testEnqueue(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 5, dtypes_lib.float32) enqueue_op = q.enqueue((10.0,)) self.assertAllEqual(0, q.size().eval()) @@ -54,7 +54,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertAllEqual(1, q.size().eval()) def testEnqueueWithShape(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue( 10, 5, dtypes_lib.float32, shapes=tensor_shape.TensorShape([3, 2])) enqueue_correct_op = q.enqueue(([[1.0, 2.0], [3.0, 4.0], [5.0, 6.0]],)) @@ -64,7 +64,7 @@ class RandomShuffleQueueTest(test.TestCase): q.enqueue(([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],)) def testEnqueueManyWithShape(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue( 10, 5, [dtypes_lib.int32, dtypes_lib.int32], shapes=[(), (2,)]) q.enqueue_many([[1, 2, 3, 4], [[1, 1], [2, 2], [3, 3], [4, 4]]]).run() @@ -76,7 +76,7 @@ class RandomShuffleQueueTest(test.TestCase): q2.enqueue_many(([[1, 2, 3]],)) def testScalarShapes(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 10, 0, [dtypes_lib.int32, dtypes_lib.int32], shapes=[(), (1,)]) q.enqueue_many([[1, 2, 3, 4], [[5], [6], [7], [8]]]).run() @@ -93,7 +93,7 @@ class RandomShuffleQueueTest(test.TestCase): results) def testParallelEnqueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -119,7 +119,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, results) def testParallelDequeue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -143,7 +143,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, results) def testDequeue(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) elems = [10.0, 20.0, 30.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -156,7 +156,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, vals) def testEnqueueAndBlockingDequeue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(3, 0, dtypes_lib.float32) elems = [10.0, 20.0, 30.0] enqueue_ops = [q.enqueue((x,)) for x in elems] @@ -185,7 +185,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, results) def testMultiEnqueueAndDequeue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 10, 0, (dtypes_lib.int32, dtypes_lib.float32)) elems = [(5, 10.0), (10, 20.0), (15, 30.0)] @@ -202,12 +202,12 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, results) def testQueueSizeEmpty(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 5, dtypes_lib.float32) self.assertEqual(0, q.size().eval()) def testQueueSizeAfterEnqueueAndDequeue(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) enqueue_op = q.enqueue((10.0,)) dequeued_t = q.dequeue() @@ -220,7 +220,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual([0], size.eval()) def testEnqueueMany(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -234,7 +234,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems + elems, results) def testEmptyEnqueueMany(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 5, dtypes_lib.float32) empty_t = constant_op.constant( [], dtype=dtypes_lib.float32, shape=[0, 2, 3]) @@ -246,7 +246,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(0, size_t.eval()) def testEmptyDequeueMany(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, shapes=()) enqueue_op = q.enqueue((10.0,)) dequeued_t = q.dequeue_many(0) @@ -256,7 +256,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual([], dequeued_t.eval().tolist()) def testEmptyDequeueUpTo(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, shapes=()) enqueue_op = q.enqueue((10.0,)) dequeued_t = q.dequeue_up_to(0) @@ -266,7 +266,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual([], dequeued_t.eval().tolist()) def testEmptyDequeueManyWithNoShape(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) enqueue_op = q.enqueue((constant_op.constant( [10.0, 20.0], shape=(1, 2)),)) @@ -287,7 +287,7 @@ class RandomShuffleQueueTest(test.TestCase): dequeued_t.eval() def testEmptyDequeueUpToWithNoShape(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) enqueue_op = q.enqueue((constant_op.constant( [10.0, 20.0], shape=(1, 2)),)) @@ -308,7 +308,7 @@ class RandomShuffleQueueTest(test.TestCase): dequeued_t.eval() def testMultiEnqueueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 10, 0, (dtypes_lib.float32, dtypes_lib.int32)) float_elems = [10.0, 20.0, 30.0, 40.0] @@ -327,7 +327,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(expected, results) def testDequeueMany(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] enqueue_op = q.enqueue_many((elems,)) @@ -340,7 +340,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, results) def testDequeueUpToNoBlocking(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0, 100.0] enqueue_op = q.enqueue_many((elems,)) @@ -353,7 +353,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, results) def testMultiDequeueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 10, 0, (dtypes_lib.float32, dtypes_lib.int32), shapes=((), (2,))) float_elems = [ @@ -387,7 +387,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(zip(float_elems, int_elems), results) def testMultiDequeueUpToNoBlocking(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 10, 0, (dtypes_lib.float32, dtypes_lib.int32), shapes=((), (2,))) float_elems = [ @@ -422,7 +422,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(zip(float_elems, int_elems), results) def testHighDimension(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.int32, ( (4, 4, 4, 4))) elems = np.array([[[[[x] * 4] * 4] * 4] * 4 for x in range(10)], np.int32) @@ -433,7 +433,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(dequeued_t.eval().tolist(), elems.tolist()) def testParallelEnqueueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 1000, 0, dtypes_lib.float32, shapes=()) elems = [10.0 * x for x in range(100)] @@ -453,7 +453,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(dequeued_t.eval(), elems * 10) def testParallelDequeueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 1000, 0, dtypes_lib.float32, shapes=()) elems = [10.0 * x for x in range(1000)] @@ -476,7 +476,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testParallelDequeueUpTo(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( 1000, 0, dtypes_lib.float32, shapes=()) elems = [10.0 * x for x in range(1000)] @@ -499,7 +499,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testParallelDequeueUpToRandomPartition(self): - with self.test_session() as sess: + with self.cached_session() as sess: dequeue_sizes = [random.randint(50, 150) for _ in xrange(10)] total_elements = sum(dequeue_sizes) q = data_flow_ops.RandomShuffleQueue( @@ -527,7 +527,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testBlockingDequeueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -554,7 +554,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testBlockingDequeueUpTo(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -581,7 +581,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testDequeueManyWithTensorParameter(self): - with self.test_session(): + with self.cached_session(): # Define a first queue that contains integer counts. dequeue_counts = [random.randint(1, 10) for _ in range(100)] count_q = data_flow_ops.RandomShuffleQueue(100, 0, dtypes_lib.int32) @@ -607,7 +607,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testDequeueUpToWithTensorParameter(self): - with self.test_session(): + with self.cached_session(): # Define a first queue that contains integer counts. dequeue_counts = [random.randint(1, 10) for _ in range(100)] count_q = data_flow_ops.RandomShuffleQueue(100, 0, dtypes_lib.int32) @@ -633,7 +633,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elems, dequeued_elems) def testDequeueFromClosedQueue(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 2, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -652,7 +652,7 @@ class RandomShuffleQueueTest(test.TestCase): dequeued_t.eval() def testBlockingDequeueFromClosedQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: min_size = 2 q = data_flow_ops.RandomShuffleQueue(10, min_size, dtypes_lib.float32) elems = [10.0, 20.0, 30.0, 40.0] @@ -690,7 +690,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(len(results), 4) def testBlockingDequeueFromClosedEmptyQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) close_op = q.close() dequeued_t = q.dequeue() @@ -715,7 +715,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(len(finished), 1) def testBlockingDequeueManyFromClosedQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -751,7 +751,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(len(progress), 2) def testBlockingDequeueUpToFromClosedQueueReturnsRemainder(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -778,7 +778,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(results, elems) def testBlockingDequeueUpToSmallerThanMinAfterDequeue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue( capacity=10, min_after_dequeue=2, @@ -811,7 +811,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(results, elems) def testBlockingDequeueManyFromClosedQueueWithElementsRemaining(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -845,7 +845,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(len(results), 4) def testBlockingDequeueManyFromClosedEmptyQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 5, dtypes_lib.float32, ((),)) close_op = q.close() dequeued_t = q.dequeue_many(4) @@ -865,7 +865,7 @@ class RandomShuffleQueueTest(test.TestCase): dequeue_thread.join() def testBlockingDequeueUpToFromClosedEmptyQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(10, 5, dtypes_lib.float32, ((),)) close_op = q.close() dequeued_t = q.dequeue_up_to(4) @@ -885,7 +885,7 @@ class RandomShuffleQueueTest(test.TestCase): dequeue_thread.join() def testEnqueueToClosedQueue(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 4, dtypes_lib.float32) enqueue_op = q.enqueue((10.0,)) close_op = q.close() @@ -898,7 +898,7 @@ class RandomShuffleQueueTest(test.TestCase): enqueue_op.run() def testEnqueueManyToClosedQueue(self): - with self.test_session(): + with self.cached_session(): q = data_flow_ops.RandomShuffleQueue(10, 5, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -912,7 +912,7 @@ class RandomShuffleQueueTest(test.TestCase): enqueue_op.run() def testBlockingEnqueueToFullQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(4, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -940,7 +940,7 @@ class RandomShuffleQueueTest(test.TestCase): thread.join() def testBlockingEnqueueManyToFullQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(4, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -974,7 +974,7 @@ class RandomShuffleQueueTest(test.TestCase): thread.join() def testBlockingEnqueueToClosedQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(4, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0, 40.0] enqueue_op = q.enqueue_many((elems,)) @@ -1019,7 +1019,7 @@ class RandomShuffleQueueTest(test.TestCase): thread1.join() def testBlockingEnqueueManyToClosedQueue(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(4, 0, dtypes_lib.float32, ((),)) elems = [10.0, 20.0, 30.0] enqueue_op = q.enqueue_many((elems,)) @@ -1067,7 +1067,7 @@ class RandomShuffleQueueTest(test.TestCase): sess.run(blocking_enqueue_op) def testSharedQueueSameSession(self): - with self.test_session(): + with self.cached_session(): q1 = data_flow_ops.RandomShuffleQueue( 1, 0, dtypes_lib.float32, ((),), shared_name="shared_queue") q1.enqueue((10.0,)).run() @@ -1104,7 +1104,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(q2_size_t.eval(), 0) def testSharedQueueSameSessionGraphSeedNone(self): - with self.test_session(): + with self.cached_session(): q1 = data_flow_ops.RandomShuffleQueue( 1, 0, @@ -1127,7 +1127,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(q2_size_t.eval(), 1) def testIncompatibleSharedQueueErrors(self): - with self.test_session(): + with self.cached_session(): q_a_1 = data_flow_ops.RandomShuffleQueue( 10, 5, dtypes_lib.float32, shared_name="q_a") q_a_2 = data_flow_ops.RandomShuffleQueue( @@ -1193,7 +1193,7 @@ class RandomShuffleQueueTest(test.TestCase): q_h_2.queue_ref.op.run() def testSelectQueue(self): - with self.test_session(): + with self.cached_session(): num_queues = 10 qlist = list() for _ in xrange(num_queues): @@ -1207,7 +1207,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertEqual(q.dequeue().eval(), 10.0) def testSelectQueueOutOfRange(self): - with self.test_session(): + with self.cached_session(): q1 = data_flow_ops.RandomShuffleQueue(10, 0, dtypes_lib.float32) q2 = data_flow_ops.RandomShuffleQueue(15, 0, dtypes_lib.float32) enq_q = data_flow_ops.RandomShuffleQueue.from_list(3, [q1, q2]) @@ -1235,7 +1235,7 @@ class RandomShuffleQueueTest(test.TestCase): sess.run(enqueue_many_op) def testResetOfBlockingOperation(self): - with self.test_session() as sess: + with self.cached_session() as sess: q_empty = data_flow_ops.RandomShuffleQueue(5, 0, dtypes_lib.float32, ( (),)) dequeue_op = q_empty.dequeue() @@ -1267,7 +1267,7 @@ class RandomShuffleQueueTest(test.TestCase): t.join() def testDequeueManyInDifferentOrders(self): - with self.test_session(): + with self.cached_session(): # Specify seeds to make the test deterministic # (https://en.wikipedia.org/wiki/Taxicab_number). q1 = data_flow_ops.RandomShuffleQueue( @@ -1301,7 +1301,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertNotEqual(results[i], results[j]) def testDequeueUpToInDifferentOrders(self): - with self.test_session(): + with self.cached_session(): # Specify seeds to make the test deterministic # (https://en.wikipedia.org/wiki/Taxicab_number). q1 = data_flow_ops.RandomShuffleQueue( @@ -1335,7 +1335,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertNotEqual(results[i], results[j]) def testDequeueInDifferentOrders(self): - with self.test_session(): + with self.cached_session(): # Specify seeds to make the test deterministic # (https://en.wikipedia.org/wiki/Taxicab_number). q1 = data_flow_ops.RandomShuffleQueue( @@ -1371,7 +1371,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertNotEqual(results[i], results[j]) def testBigEnqueueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(5, 0, dtypes_lib.int32, ((),)) elem = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] enq = q.enqueue_many((elem,)) @@ -1416,7 +1416,7 @@ class RandomShuffleQueueTest(test.TestCase): self.assertItemsEqual(elem, results) def testBigDequeueMany(self): - with self.test_session() as sess: + with self.cached_session() as sess: q = data_flow_ops.RandomShuffleQueue(2, 0, dtypes_lib.int32, ((),)) elem = np.arange(4, dtype=np.int32) enq_list = [q.enqueue((e,)) for e in elem] |