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Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp')
-rw-r--r-- | src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp | 617 |
1 files changed, 617 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp new file mode 100644 index 0000000000..c6d3982f92 --- /dev/null +++ b/src/backends/backendsCommon/test/layerTests/AdditionTestImpl.cpp @@ -0,0 +1,617 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "AdditionTestImpl.hpp" + +#include "ElementwiseTestImpl.hpp" + +template<> +std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::AdditionQueueDescriptor>( + const armnn::IWorkloadFactory& workloadFactory, + const armnn::WorkloadInfo& info, + const armnn::AdditionQueueDescriptor& descriptor) +{ + return workloadFactory.CreateAddition(descriptor, info); +} + +LayerTestResult<float,4> AdditionTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + unsigned int batchSize = 2u; + unsigned int channels = 2u; + unsigned int height = 2u; + unsigned int width = 3u; + + unsigned int shape[] = { batchSize, channels, height, width }; + + std::vector<float> input1 = + { + 0.0f, 2.0f, 1.0f, + 0.2f, 1.0f, 2.0f, + + 1.0f, 2.0f, 1.0f, + 0.2f, 1.0f, 2.0f, + + 0.0f, 2.0f, 1.0f, + 4.2f, 1.0f, 2.0f, + + 0.0f, 0.0f, 1.0f, + 0.2f, 1.0f, 2.0f, + }; + + std::vector<float> input2 = + { + 1.0f, 2.0f, 1.0f, + 0.0f, 1.0f, 2.0f, + + 1.0f, 2.0f, -2.0f, + 0.2f, 1.0f, 2.0f, + + 0.0f, 2.0f, 1.0f, + 4.2f, 0.0f, -3.0f, + + 0.0f, 0.0f, 1.0f, + 0.7f, 1.0f, 5.0f, + }; + + + std::vector<float> output + { + 1.0f, 4.0f, 2.0f, + 0.2f, 2.0f, 4.0f, + + 2.0f, 4.0f, -1.0f, + 0.4f, 2.0f, 4.0f, + + 0.0f, 4.0f, 2.0f, + 8.4f, 1.0f, -1.0f, + + 0.0f, 0.0f, 2.0f, + 0.9f, 2.0f, 7.0f, + }; + + return ElementwiseTestHelper<4, armnn::AdditionQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape, + input1, + shape, + input2, + shape, + output); +} + +LayerTestResult<float, 5> Addition5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + unsigned int depth = 2u; + unsigned int batchSize = 2u; + unsigned int channels = 2u; + unsigned int height = 2u; + unsigned int width = 3u; + + unsigned int shape[] = { depth, batchSize, channels, height, width }; + + std::vector<float> input1 = + { + 2.6f, 4.0f, 4.4f, 2.7f, 4.6f, 2.8f, + 2.3f, 1.9f, 3.4f, 2.9f, 2.2f, 4.5f, + + 2.8f, 1.9f, 2.3f, 2.6f, 4.7f, 3.5f, + 0.4f, 1.5f, 2.1f, 0.7f, 5.0f, 1.1f, + + + 1.0f, 2.7f, 0.0f, 0.6f, 0.8f, 0.9f, + 1.0f, 2.6f, 0.4f, 3.8f, 0.4f, 0.8f, + + 0.5f, 4.3f, 3.1f, 4.4f, 0.7f, 1.4f, + 0.4f, 4.4f, 0.7f, 0.6f, 4.7f, 1.2f, + + }; + + std::vector<float> input2 = + { + 4.4f, 3.0f, 1.0f, 0.0f, 3.9f, 3.1f, + 1.7f, 2.9f, 1.3f, 0.4f, 0.4f, 4.3f, + + 4.5f, 0.2f, 2.2f, 4.1f, 3.9f, 3.0f, + 0.1f, 2.5f, 4.1f, 4.6f, 1.5f, 0.0f, + + + 0.5f, 4.9f, 2.5f, 1.5f, 3.4f, 4.5f, + 2.0f, 3.0f, 4.9f, 1.6f, 2.4f, 3.4f, + + 3.6f, 1.8f, 1.3f, 2.6f, 2.1f, 4.8f, + 2.0f, 4.3f, 4.0f, 0.2f, 0.6f, 4.4f, + }; + + std::vector<float> output = + { + 7.0f, 7.0f, 5.4f, 2.7f, 8.5f, 5.9f, + 4.0f, 4.8f, 4.7f, 3.3f, 2.6f, 8.8f, + + 7.3f, 2.1f, 4.5f, 6.7f, 8.6f, 6.5f, + 0.5f, 4.0f, 6.2f, 5.3f, 6.5f, 1.1f, + + + 1.5f, 7.6f, 2.5f, 2.1f, 4.2f, 5.4f, + 3.0f, 5.6f, 5.3f, 5.4f, 2.8f, 4.2f, + + 4.1f, 6.1f, 4.4f, 7.0f, 2.8f, 6.2f, + 2.4f, 8.7f, 4.7f, 0.8f, 5.3f, 5.6f, + }; + + return ElementwiseTestHelper<5, armnn::AdditionQueueDescriptor, armnn::DataType::Float32>( + workloadFactory, + memoryManager, + shape, + input1, + shape, + input2, + shape, + output); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 4> AdditionBroadcastTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float qScale, + int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 1}, ArmnnType); + armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 2, 3}, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo1.SetQuantizationScale(qScale); + inputTensorInfo1.SetQuantizationOffset(qOffset); + inputTensorInfo2.SetQuantizationScale(qScale); + inputTensorInfo2.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, + { + 0.0f, + 1.0f, + + 2.0f, + 3.0f, + + 4.0f, + 5.0f, + })); + + auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, + { + 0.5f, 1.5f, 2.5f, + 3.5f, 4.5f, 5.5f, + })); + + LayerTestResult<T,4> ret(outputTensorInfo); + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, + { + 0.5f, 1.5f, 2.5f, + 4.5f, 5.5f, 6.5f, + + 2.5f, 3.5f, 4.5f, + 6.5f, 7.5f, 8.5f, + + 4.5f, 5.5f, 6.5f, + 8.5f, 9.5f, 10.5f, + })); + + std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + + workload->PostAllocationConfigure(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 4> AdditionBroadcast1ElementTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + float qScale, + int32_t qOffset) +{ + armnn::TensorInfo inputTensorInfo1 = armnn::TensorInfo({1, 3, 2, 3}, ArmnnType); + armnn::TensorInfo inputTensorInfo2 = armnn::TensorInfo({1, 1, 1, 1}, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnn::TensorInfo({1, 3, 2, 3}, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo1.SetQuantizationScale(qScale); + inputTensorInfo1.SetQuantizationOffset(qOffset); + inputTensorInfo2.SetQuantizationScale(qScale); + inputTensorInfo2.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + auto input1 = MakeTensor<T, 4>(inputTensorInfo1, QuantizedVector<T>(qScale, qOffset, + { + 0.0f, 1.0f, 2.0f, + 3.0f, 4.0f, 5.0f, + 6.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 11.0f, + 12.0f, 13.0f, 14.0f, + 15.0f, 16.0f, 17.0f, + })); + + auto input2 = MakeTensor<T, 4>(inputTensorInfo2, QuantizedVector<T>(qScale, qOffset, + { + 0.5f, + })); + + LayerTestResult<T,4> ret(outputTensorInfo); + ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, QuantizedVector<T>(qScale, qOffset, + { + 0.5f, 1.5f, 2.5f, + 3.5f, 4.5f, 5.5f, + 6.5f, 7.5f, 8.5f, + 9.5f, 10.5f, 11.5f, + 12.5f, 13.5f, 14.5f, + 15.5f, 16.5f, 17.5f, + })); + + std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + + workload->PostAllocationConfigure(); + workload->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + + return ret; +} + +LayerTestResult<float, 4> AdditionBroadcastTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + return AdditionBroadcastTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); +} + +LayerTestResult<uint8_t, 4> AdditionBroadcastUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + return AdditionBroadcastTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 2.f, 0); +} + +LayerTestResult<int16_t, 4> AdditionBroadcastInt16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + return AdditionBroadcastTestImpl<armnn::DataType::QuantisedSymm16>( + workloadFactory, memoryManager, 2.f, 0); +} + +LayerTestResult<float, 4> AdditionBroadcast1ElementTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + return AdditionBroadcast1ElementTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, 0.0f, 0); +} + +LayerTestResult<uint8_t, 4> AdditionBroadcast1ElementUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + return AdditionBroadcast1ElementTestImpl<armnn::DataType::QuantisedAsymm8>( + workloadFactory, memoryManager, 0.1333333f, 128); +} + +LayerTestResult<int16_t, 4> AdditionBroadcast1ElementInt16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + return AdditionBroadcast1ElementTestImpl<armnn::DataType::QuantisedSymm16>( + workloadFactory, memoryManager, 0.1333333f, 0); +} + +LayerTestResult<uint8_t, 4> AdditionUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + const unsigned int shape0[] = { 1, 2, 2, 3 }; + const unsigned int shape1[] = { 1, 2, 2, 3 }; + + std::vector<uint8_t> input0( + { + 63, 35, 77, 70, 56, 112, // 420, 224, 518, 469, 371, 763 + 203, 28, 252, 168, 245, 91 // 1400, 175, 1743, 1155, 1694, 616 + }); + + std::vector<uint8_t> input1( + { + 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 + 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 + }); + + std::vector<uint8_t> output( + { + 81, 39, 249, 255, 228, 255, // 546, 252, 1722, 2065(clamped), 1575, 2212(clamped) + 255, 186, 255, 186, 255, 214, // 2261(clamped), 1281, 2163(clamped), 1281, 2408(clamped), 1477 + }); + + return ElementwiseTestHelper<4, armnn::AdditionQueueDescriptor, armnn::DataType::QuantisedAsymm8>( + workloadFactory, + memoryManager, + shape0, + input0, + 7.0f, + 3, + shape1, + input1, + 7.0f, + 3, + shape0, + output, + 7.0f, + 3); +} + +LayerTestResult<int16_t, 4> AdditionInt16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + const unsigned int shape0[] = { 1, 2, 2, 3 }; + const unsigned int shape1[] = { 1, 2, 2, 3 }; + + std::vector<int16_t> input0 = + { + 63, 35, 77, 70, 56, 112, // 441, 245, 539, 490, 392, 184 + 203, 28, 252, 168, 245, 91 // 1421, 196, 1764, 1176, 1715, 637 + }; + + std::vector<int16_t> input1 = + { + 21, 7, 175, 231, 175, 210, // 126, 28, 1204, 1596, 1204, 1449 + 126, 161, 63, 21, 105, 126 // 861, 1106, 420, 126, 714, 861 + }; + + std::vector<int16_t> output = + { + 84, 42, 252, 301, 231, 322, // 588, 294, 1764, 2107(clamped), 1617, 2254(clamped) + 329, 189, 315, 189, 350, 217, // 2303(clamped), 1323, 2205(clamped), 1323, 2450(clamped), 1519 + }; + + return ElementwiseTestHelper<4, armnn::AdditionQueueDescriptor, armnn::DataType::QuantisedSymm16>( + workloadFactory, + memoryManager, + shape0, + input0, + 7.0f, + 0, + shape1, + input1, + 7.0f, + 0, + shape0, + output, + 7.0f, + 0); +} + +LayerTestResult<float, 4> AdditionAfterMaxPoolTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + // Create Initial Tensor + // 1, 2, 3 + // 4, 5, 6 + // 7, 8, 9 + + armnn::TensorInfo poolingInputTensorInfo({ 1, 1, 3, 3}, armnn::DataType::Float32); + armnn::TensorInfo poolingOutputTensorInfo({ 1, 1, 2, 2}, armnn::DataType::Float32); + + boost::multi_array<float, 4> poolingInput = MakeTensor<float,4>(poolingInputTensorInfo, + {1, 2, 3, + 4, 5, 6, + 7, 8, 9 + }); + + std::unique_ptr<armnn::ITensorHandle> poolingInputHandle = + workloadFactory.CreateTensorHandle(poolingInputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> poolingOutputHandle = + workloadFactory.CreateTensorHandle(poolingOutputTensorInfo); + + // Apply MaxPool poolSize = 1x1, stride=2x2 + // Result = + // 1, 3 + // 7, 9 + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolHeight = 1; + descriptor.m_PoolWidth = 1; + descriptor.m_StrideX = 2; + descriptor.m_StrideY = 2; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + + armnn::Pooling2dQueueDescriptor queueDescriptor; + queueDescriptor.m_Parameters = descriptor; + armnn::WorkloadInfo workloadInfo; + AddInputToWorkload(queueDescriptor, workloadInfo, poolingInputTensorInfo, poolingInputHandle.get()); + AddOutputToWorkload(queueDescriptor, workloadInfo, poolingOutputTensorInfo, poolingOutputHandle.get()); + + // Create the MaxPool + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); + + //LayerTestResult<float, 4> result(poolingOutputTensorInfo); + auto shape( GetTensorShapeAsArray<4>(poolingOutputTensorInfo)); + boost::multi_array<float, 4> resultMaxPool; + resultMaxPool.resize(shape); + + + // Create addition with another tensor the same size + // This would be the result to apply a Conv2d with kernel ones(2) and stride 1x1 + // with the initial tensor. + // 12, 16 + // 24, 28 + + armnn::TensorInfo addInputTensorInfo({ 1,1,2,2}, armnn::DataType::Float32); + armnn::TensorInfo addOutputTensorInfo({ 1,1,2,2}, armnn::DataType::Float32); + + boost::multi_array<float, 4> addInput = MakeTensor<float,4>(addInputTensorInfo, + {12, 16, + 24, 28, + }); + + // Expected output tensor after MaxPool and Addition. + LayerTestResult<float,4> addRet(addOutputTensorInfo); + addRet.outputExpected = MakeTensor<float, 4>(addOutputTensorInfo, std::vector<float>( + { + 13, 19, + 31, 37 + })); + + std::unique_ptr<armnn::ITensorHandle> addInputHandle = workloadFactory.CreateTensorHandle(addInputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> addOutputHandle = workloadFactory.CreateTensorHandle(addOutputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + + // Add the output of the MaxPool and the new tensor + AddInputToWorkload(data, info, poolingOutputTensorInfo, poolingOutputHandle.get()); + AddInputToWorkload(data, info, addInputTensorInfo, addInputHandle.get()); + AddOutputToWorkload(data, info, addOutputTensorInfo, addOutputHandle.get()); + + std::unique_ptr<armnn::IWorkload> addWorkload = workloadFactory.CreateAddition(data, info); + + poolingInputHandle->Allocate(); + poolingOutputHandle->Allocate(); + addInputHandle->Allocate(); + addOutputHandle->Allocate(); + + CopyDataToITensorHandle(poolingInputHandle.get(), &poolingInput[0][0][0][0]); + CopyDataFromITensorHandle(&resultMaxPool[0][0][0][0], poolingOutputHandle.get()); + + CopyDataToITensorHandle(poolingOutputHandle.get(), &resultMaxPool[0][0][0][0]); + CopyDataToITensorHandle(addInputHandle.get(), &addInput[0][0][0][0]); + + workload->PostAllocationConfigure(); + workload->Execute(); + addWorkload->PostAllocationConfigure(); + addWorkload->Execute(); + + CopyDataFromITensorHandle(&addRet.output[0][0][0][0], addOutputHandle.get()); + + return addRet; +} + +LayerTestResult<float,4> CompareAdditionTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + armnn::IWorkloadFactory& refWorkloadFactory) +{ + unsigned int batchSize = 4; + unsigned int channels = 1; + unsigned int height = 2; + unsigned int width = 3; + + armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; + armnn::TensorInfo outputTensorInfo; + + unsigned int shape[] = {batchSize, channels, height, width}; + + inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + inputTensorInfo2 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); + + auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 1232); + auto input2 = MakeRandomTensor<float, 4>(inputTensorInfo2, 456); + + LayerTestResult<float,4> ret(outputTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr<armnn::ITensorHandle> inputHandle2 = workloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); + std::unique_ptr<armnn::ITensorHandle> inputHandle2Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo2); + std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); + + armnn::AdditionQueueDescriptor data; + armnn::WorkloadInfo info; + AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); + AddInputToWorkload(data, info, inputTensorInfo2, inputHandle2.get()); + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::AdditionQueueDescriptor refData = data; + armnn::WorkloadInfo refInfo = info; + SetWorkloadInput(refData, refInfo, 0, inputTensorInfo1, inputHandle1Ref.get()); + SetWorkloadInput(refData, refInfo, 1, inputTensorInfo2, inputHandle2Ref.get()); + SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); + std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateAddition(refData, refInfo); + + inputHandle1->Allocate(); + inputHandle2->Allocate(); + outputHandle->Allocate(); + inputHandle1Ref->Allocate(); + inputHandle2Ref->Allocate(); + outputHandleRef->Allocate(); + + CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0]); + CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); + CopyDataToITensorHandle(inputHandle2Ref.get(), &input2[0][0][0][0]); + + workload->PostAllocationConfigure(); + workload->Execute(); + workloadRef->PostAllocationConfigure(); + workloadRef->Execute(); + + CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + + return ret; +} |