| /* Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
| http://www.apache.org/licenses/LICENSE-2.0 |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| ==============================================================================*/ |
| #include "tensorflow/core/kernels/data/parallel_interleave_dataset_op.h" |
| |
| #include "tensorflow/core/kernels/data/dataset_test_base.h" |
| |
| namespace tensorflow { |
| namespace data { |
| namespace { |
| |
| constexpr char kNodeName[] = "parallel_interleave_dataset"; |
| constexpr int kOpVersion = 2; |
| |
| class ParallelInterleaveDatasetOpTest : public DatasetOpsTestBase { |
| protected: |
| // Creates `TensorSliceDataset` variant tensor from the input vector of |
| // tensors. |
| Status CreateTensorSliceDatasetTensor( |
| std::vector<Tensor> *const tensor_vector, Tensor *dataset_tensor) { |
| DatasetBase *tensor_slice_dataset; |
| TF_RETURN_IF_ERROR(CreateTensorSliceDataset( |
| "tensor_slice_node", tensor_vector, &tensor_slice_dataset)); |
| TF_RETURN_IF_ERROR( |
| StoreDatasetInVariantTensor(tensor_slice_dataset, dataset_tensor)); |
| return Status::OK(); |
| } |
| |
| // Creates a new `ParallelInterleaveDataset` op kernel |
| Status CreateParallelInterleaveDatasetKernel( |
| const FunctionDefHelper::AttrValueWrapper &func, |
| const DataTypeVector &output_types, |
| const std::vector<PartialTensorShape> &output_shapes, bool sloppy, |
| std::unique_ptr<OpKernel> *op_kernel) { |
| name_utils::OpNameParams params; |
| params.op_version = kOpVersion; |
| NodeDef node_def = test::function::NDef( |
| kNodeName, |
| name_utils::OpName(ParallelInterleaveDatasetOp::kDatasetType, params), |
| {ParallelInterleaveDatasetOp::kInputDataset, |
| ParallelInterleaveDatasetOp::kCycleLength, |
| ParallelInterleaveDatasetOp::kBlockLength, |
| ParallelInterleaveDatasetOp::kNumParallelCalls}, |
| {{ParallelInterleaveDatasetOp::kFunc, func}, |
| {ParallelInterleaveDatasetOp::kTarguments, {}}, |
| {ParallelInterleaveDatasetOp::kOutputTypes, output_types}, |
| {ParallelInterleaveDatasetOp::kOutputShapes, output_shapes}, |
| {ParallelInterleaveDatasetOp::kSloppy, sloppy}}); |
| TF_RETURN_IF_ERROR(CreateOpKernel(node_def, op_kernel)); |
| return Status::OK(); |
| } |
| |
| // Creates a new `ParallelInterleaveDataset` op kernel context. |
| Status CreateInterleaveDatasetContext( |
| OpKernel *const op_kernel, |
| gtl::InlinedVector<TensorValue, 4> *const inputs, |
| std::unique_ptr<OpKernelContext> *context) { |
| TF_RETURN_IF_ERROR(CheckOpKernelInput(*op_kernel, *inputs)); |
| TF_RETURN_IF_ERROR(CreateOpKernelContext(op_kernel, inputs, context)); |
| return Status::OK(); |
| } |
| }; |
| |
| struct TestCase { |
| std::vector<Tensor> input_tensors; |
| FunctionDefHelper::AttrValueWrapper func; |
| std::vector<FunctionDef> func_lib; |
| Tensor cycle_length; |
| Tensor block_length; |
| Tensor num_parallel_calls; |
| bool sloppy; |
| std::vector<Tensor> expected_outputs; |
| DataTypeVector expected_output_dtypes; |
| std::vector<PartialTensorShape> expected_output_shapes; |
| int64 expected_cardinality; |
| std::vector<int> breakpoints; |
| }; |
| |
| template <typename T> |
| std::vector<Tensor> ConvertToTensorVec(std::vector<T> values) { |
| std::vector<Tensor> tensors; |
| tensors.reserve(values.size()); |
| for (auto &value : values) { |
| tensors.emplace_back(CreateTensor<T>(TensorShape({1}), {value})); |
| } |
| return tensors; |
| } |
| |
| FunctionDefHelper::AttrValueWrapper MakeTensorSliceDatasetFunc( |
| const DataTypeVector &output_types, |
| const std::vector<PartialTensorShape> &output_shapes) { |
| return FunctionDefHelper::FunctionRef( |
| /*name*/ "MakeTensorSliceDataset", |
| /*attrs*/ {{"Toutput_types", output_types}, |
| {"output_shapes", output_shapes}}); |
| } |
| |
| // test case 1: cycle_length = 1, block_length = 1, num_parallel_calls = 1, |
| // sloppy = false |
| TestCase TestCase1() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*sloppy*/ false, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({0, 1, 2, 3, 4, 5, 6, 7, 8}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 2: cycle_length = 2, block_length = 1, num_parallel_calls = 2, |
| // sloppy = false |
| TestCase TestCase2() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*sloppy*/ false, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({0, 3, 1, 4, 2, 5, 6, 7, 8}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 3: cycle_length = 3, block_length = 1, num_parallel_calls = 2, |
| // sloppy = true |
| TestCase TestCase3() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {3}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({0, 3, 6, 1, 4, 7, 2, 5, 8}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 4: cycle_length = 5, block_length = 1, num_parallel_calls = 4, |
| // sloppy = true |
| TestCase TestCase4() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {5}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {4}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({0, 3, 6, 1, 4, 7, 2, 5, 8}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 5: cycle_length = 2, block_length = 2, num_parallel_calls = 1, |
| // sloppy = false |
| TestCase TestCase5() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<string>(TensorShape{3, 3, 1}, |
| {"a", "b", "c", "d", "e", "f", "g", "h", "i"})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_STRING}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*sloppy*/ false, |
| /*expected_outputs*/ |
| ConvertToTensorVec<string>({"a", "b", "d", "e", "c", "f", "g", "h", "i"}), |
| /*expected_output_dtypes*/ {DT_STRING}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 6: cycle_length = 2, block_length = 3, num_parallel_calls = 2, |
| // sloppy = true |
| TestCase TestCase6() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<string>(TensorShape{3, 3, 1}, |
| {"a", "b", "c", "d", "e", "f", "g", "h", "i"})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_STRING}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {3}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<string>({"a", "b", "c", "d", "e", "f", "g", "h", "i"}), |
| /*expected_output_dtypes*/ {DT_STRING}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 7: cycle_length = 3, block_length = 2, num_parallel_calls = 2, |
| // sloppy = false |
| TestCase TestCase7() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<string>(TensorShape{3, 3, 1}, |
| {"a", "b", "c", "d", "e", "f", "g", "h", "i"})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_STRING}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {3}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*sloppy*/ false, |
| /*expected_outputs*/ |
| ConvertToTensorVec<string>({"a", "b", "d", "e", "g", "h", "c", "f", "i"}), |
| /*expected_output_dtypes*/ {DT_STRING}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 8: cycle_length = 3, block_length = 3, num_parallel_calls = 3, |
| // sloppy = true |
| TestCase TestCase8() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<string>(TensorShape{3, 3, 1}, |
| {"a", "b", "c", "d", "e", "f", "g", "h", "i"})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_STRING}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {3}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {3}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {3}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<string>({"a", "b", "c", "d", "e", "f", "g", "h", "i"}), |
| /*expected_output_dtypes*/ {DT_STRING}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 9: cycle_length = 4, block_length = 4, num_parallel_calls = 4, |
| // sloppy = true |
| TestCase TestCase9() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<string>(TensorShape{3, 3, 1}, |
| {"a", "b", "c", "d", "e", "f", "g", "h", "i"})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_STRING}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {4}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {4}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {4}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<string>({"a", "b", "c", "d", "e", "f", "g", "h", "i"}), |
| /*expected_output_dtypes*/ {DT_STRING}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 10: cycle_length = 3, block_length = 3, |
| // num_parallel_calls = kAutotune, sloppy = true |
| TestCase TestCase10() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<string>(TensorShape{3, 3, 1}, |
| {"a", "b", "c", "d", "e", "f", "g", "h", "i"})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_STRING}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {4}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {4}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {model::kAutotune}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<string>({"a", "b", "c", "d", "e", "f", "g", "h", "i"}), |
| /*expected_output_dtypes*/ {DT_STRING}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {0, 4, 11}}; |
| } |
| |
| // test case 11: cycle_length = 0, block_length = 1, num_parallel_calls = 2, |
| // sloppy = true |
| TestCase InvalidCycleLengthTestCase() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {0}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {}}; |
| } |
| |
| // test case 12: cycle_length = 1, block_length = -1, num_parallel_calls = 2, |
| // sloppy = true |
| TestCase InvalidBlockLengthTestCase() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {-1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {2}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {}}; |
| } |
| |
| // test case 13: cycle_length = 1, block_length = 1, num_parallel_calls = -5, |
| // sloppy = true |
| TestCase InvalidNumParallelCallsTestCase() { |
| return { |
| /*input_tensors*/ |
| {CreateTensor<int64>(TensorShape{3, 3, 1}, {0, 1, 2, 3, 4, 5, 6, 7, 8})}, |
| /*func*/ |
| MakeTensorSliceDatasetFunc( |
| DataTypeVector({DT_INT64}), |
| std::vector<PartialTensorShape>({PartialTensorShape({1})})), |
| /*func_lib*/ {test::function::MakeTensorSliceDataset()}, |
| /*cycle_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*block_length*/ |
| CreateTensor<int64>(TensorShape({}), {1}), |
| /*num_parallel_calls*/ |
| CreateTensor<int64>(TensorShape({}), {-5}), |
| /*sloppy*/ true, |
| /*expected_outputs*/ |
| ConvertToTensorVec<int64>({}), |
| /*expected_output_dtypes*/ {DT_INT64}, |
| /*expected_output_shapes*/ {PartialTensorShape({1})}, |
| /*expected_cardinality*/ tensorflow::data::kUnknownCardinality, |
| /*breakpoints*/ {}}; |
| } |
| |
| class ParameterizedParallelInterleaveDatasetOpTest |
| : public ParallelInterleaveDatasetOpTest, |
| public ::testing::WithParamInterface<TestCase> {}; |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, GetNext) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| std::unique_ptr<IteratorContext> iterator_ctx; |
| TF_ASSERT_OK(CreateIteratorContext(parallel_interleave_dataset_context.get(), |
| &iterator_ctx)); |
| std::unique_ptr<IteratorBase> iterator; |
| TF_ASSERT_OK(parallel_interleave_dataset->MakeIterator( |
| iterator_ctx.get(), "Iterator", &iterator)); |
| bool end_of_sequence = false; |
| std::vector<Tensor> out_tensors; |
| while (!end_of_sequence) { |
| std::vector<Tensor> next; |
| TF_EXPECT_OK( |
| iterator->GetNext(iterator_ctx.get(), &next, &end_of_sequence)); |
| out_tensors.insert(out_tensors.end(), next.begin(), next.end()); |
| } |
| |
| TF_EXPECT_OK(ExpectEqual(out_tensors, test_case.expected_outputs, |
| /*compare_order*/ !test_case.sloppy)); |
| } |
| |
| TEST_F(ParallelInterleaveDatasetOpTest, InvalidArguments) { |
| int thread_num = 2, cpu_num = 2; |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| |
| std::vector<TestCase> test_cases({InvalidCycleLengthTestCase(), |
| InvalidBlockLengthTestCase(), |
| InvalidNumParallelCallsTestCase()}); |
| for (const auto &test_case : test_cases) { |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = |
| test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor( |
| &inputs_for_tensor_slice_dataset, &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| EXPECT_EQ(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset) |
| .code(), |
| tensorflow::error::INVALID_ARGUMENT); |
| } |
| } |
| |
| TEST_F(ParallelInterleaveDatasetOpTest, DatasetNodeName) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = TestCase1(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| EXPECT_EQ(parallel_interleave_dataset->node_name(), kNodeName); |
| } |
| |
| TEST_F(ParallelInterleaveDatasetOpTest, DatasetTypeString) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = TestCase1(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| name_utils::OpNameParams params; |
| params.op_version = kOpVersion; |
| EXPECT_EQ( |
| parallel_interleave_dataset->type_string(), |
| name_utils::OpName(ParallelInterleaveDatasetOp::kDatasetType, params)); |
| } |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, DatasetOutputDtypes) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| TF_EXPECT_OK(VerifyTypesMatch(parallel_interleave_dataset->output_dtypes(), |
| test_case.expected_output_dtypes)); |
| } |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, DatasetOutputShapes) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| TF_EXPECT_OK( |
| VerifyShapesCompatible(parallel_interleave_dataset->output_shapes(), |
| test_case.expected_output_shapes)); |
| } |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, Cardinality) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| EXPECT_EQ(parallel_interleave_dataset->Cardinality(), |
| test_case.expected_cardinality); |
| } |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, IteratorOutputDtypes) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| std::unique_ptr<IteratorContext> iterator_ctx; |
| TF_ASSERT_OK(CreateIteratorContext(parallel_interleave_dataset_context.get(), |
| &iterator_ctx)); |
| std::unique_ptr<IteratorBase> iterator; |
| TF_ASSERT_OK(parallel_interleave_dataset->MakeIterator( |
| iterator_ctx.get(), "Iterator", &iterator)); |
| |
| TF_EXPECT_OK(VerifyTypesMatch(iterator->output_dtypes(), |
| test_case.expected_output_dtypes)); |
| } |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, IteratorOutputShapes) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| std::unique_ptr<IteratorContext> iterator_ctx; |
| TF_ASSERT_OK(CreateIteratorContext(parallel_interleave_dataset_context.get(), |
| &iterator_ctx)); |
| std::unique_ptr<IteratorBase> iterator; |
| TF_ASSERT_OK(parallel_interleave_dataset->MakeIterator( |
| iterator_ctx.get(), "Iterator", &iterator)); |
| |
| TF_EXPECT_OK(VerifyShapesCompatible(iterator->output_shapes(), |
| test_case.expected_output_shapes)); |
| } |
| |
| TEST_F(ParallelInterleaveDatasetOpTest, IteratorOutputPrefix) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = TestCase1(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| std::unique_ptr<IteratorContext> iterator_ctx; |
| TF_ASSERT_OK(CreateIteratorContext(parallel_interleave_dataset_context.get(), |
| &iterator_ctx)); |
| std::unique_ptr<IteratorBase> iterator; |
| TF_ASSERT_OK(parallel_interleave_dataset->MakeIterator( |
| iterator_ctx.get(), "Iterator", &iterator)); |
| name_utils::IteratorPrefixParams params; |
| params.op_version = kOpVersion; |
| EXPECT_EQ(iterator->prefix(), |
| name_utils::IteratorPrefix( |
| ParallelInterleaveDatasetOp::kDatasetType, "Iterator", params)); |
| } |
| |
| TEST_P(ParameterizedParallelInterleaveDatasetOpTest, Roundtrip) { |
| int thread_num = 2, cpu_num = 2; |
| const TestCase &test_case = GetParam(); |
| TF_ASSERT_OK(InitThreadPool(thread_num)); |
| TF_ASSERT_OK(InitFunctionLibraryRuntime(test_case.func_lib, cpu_num)); |
| |
| std::unique_ptr<OpKernel> parallel_interleave_dataset_kernel; |
| TF_ASSERT_OK(CreateParallelInterleaveDatasetKernel( |
| test_case.func, test_case.expected_output_dtypes, |
| test_case.expected_output_shapes, test_case.sloppy, |
| ¶llel_interleave_dataset_kernel)); |
| |
| Tensor tensor_slice_dataset_tensor(DT_VARIANT, TensorShape({})); |
| std::vector<Tensor> inputs_for_tensor_slice_dataset = test_case.input_tensors; |
| TF_ASSERT_OK(CreateTensorSliceDatasetTensor(&inputs_for_tensor_slice_dataset, |
| &tensor_slice_dataset_tensor)); |
| Tensor cycle_length = test_case.cycle_length; |
| Tensor block_length = test_case.block_length; |
| Tensor num_parallel_calls = test_case.num_parallel_calls; |
| gtl::InlinedVector<TensorValue, 4> inputs( |
| {TensorValue(&tensor_slice_dataset_tensor), TensorValue(&cycle_length), |
| TensorValue(&block_length), TensorValue(&num_parallel_calls)}); |
| std::unique_ptr<OpKernelContext> parallel_interleave_dataset_context; |
| TF_ASSERT_OK(CreateInterleaveDatasetContext( |
| parallel_interleave_dataset_kernel.get(), &inputs, |
| ¶llel_interleave_dataset_context)); |
| DatasetBase *parallel_interleave_dataset; |
| TF_ASSERT_OK(CreateDataset(parallel_interleave_dataset_kernel.get(), |
| parallel_interleave_dataset_context.get(), |
| ¶llel_interleave_dataset)); |
| core::ScopedUnref scoped_unref(parallel_interleave_dataset); |
| |
| std::unique_ptr<IteratorContext> iterator_ctx; |
| TF_ASSERT_OK(CreateIteratorContext(parallel_interleave_dataset_context.get(), |
| &iterator_ctx)); |
| std::unique_ptr<IteratorBase> iterator; |
| TF_ASSERT_OK(parallel_interleave_dataset->MakeIterator( |
| iterator_ctx.get(), "Iterator", &iterator)); |
| |
| std::unique_ptr<SerializationContext> serialization_ctx; |
| TF_ASSERT_OK(CreateSerializationContext(&serialization_ctx)); |
| |
| bool end_of_sequence = false; |
| std::vector<Tensor> out_tensors; |
| int cur_iteration = 0; |
| const std::vector<int> &breakpoints = test_case.breakpoints; |
| for (int breakpoint : breakpoints) { |
| VariantTensorData data; |
| VariantTensorDataWriter writer(&data); |
| TF_EXPECT_OK(iterator->Save(serialization_ctx.get(), &writer)); |
| TF_EXPECT_OK(writer.Flush()); |
| VariantTensorDataReader reader(&data); |
| TF_EXPECT_OK(RestoreIterator(iterator_ctx.get(), &reader, "Iterator", |
| *parallel_interleave_dataset, &iterator)); |
| |
| while (cur_iteration <= breakpoint) { |
| std::vector<Tensor> next; |
| TF_EXPECT_OK( |
| iterator->GetNext(iterator_ctx.get(), &next, &end_of_sequence)); |
| out_tensors.insert(out_tensors.end(), next.begin(), next.end()); |
| cur_iteration++; |
| } |
| } |
| |
| TF_EXPECT_OK(ExpectEqual(out_tensors, test_case.expected_outputs, |
| /*compare_order*/ !test_case.sloppy)); |
| } |
| |
| INSTANTIATE_TEST_SUITE_P( |
| ParallelInterleaveDatasetOpTest, |
| ParameterizedParallelInterleaveDatasetOpTest, |
| ::testing::ValuesIn(std::vector<TestCase>( |
| {TestCase1(), TestCase2(), TestCase3(), TestCase4(), TestCase5(), |
| TestCase6(), TestCase7(), TestCase8(), TestCase9(), TestCase10()}))); |
| |
| } // namespace |
| } // namespace data |
| } // namespace tensorflow |
