| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "AdditionTestImpl.hpp" |
| |
| #include "ElementwiseTestImpl.hpp" |
| |
| #include <QuantizeHelper.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) |
| { |
| boost::ignore_unused(memoryManager); |
| 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, armnnUtils::QuantizedVector<T>( |
| { |
| 0.0f, |
| 1.0f, |
| |
| 2.0f, |
| 3.0f, |
| |
| 4.0f, |
| 5.0f, |
| }, |
| qScale, qOffset)); |
| |
| auto input2 = MakeTensor<T, 4>(inputTensorInfo2, armnnUtils::QuantizedVector<T>( |
| { |
| 0.5f, 1.5f, 2.5f, |
| 3.5f, 4.5f, 5.5f, |
| }, |
| qScale, qOffset)); |
| |
| LayerTestResult<T,4> ret(outputTensorInfo); |
| ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>( |
| { |
| 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, |
| }, |
| qScale, qOffset)); |
| |
| 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) |
| { |
| boost::ignore_unused(memoryManager); |
| 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, armnnUtils::QuantizedVector<T>( |
| { |
| 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, |
| }, |
| qScale, qOffset)); |
| |
| auto input2 = MakeTensor<T, 4>(inputTensorInfo2, armnnUtils::QuantizedVector<T>( |
| { |
| 0.5f, |
| }, |
| qScale, qOffset)); |
| |
| LayerTestResult<T,4> ret(outputTensorInfo); |
| ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>( |
| { |
| 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, |
| }, |
| qScale, qOffset)); |
| |
| 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) |
| { |
| boost::ignore_unused(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) |
| { |
| boost::ignore_unused(memoryManager); |
| 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; |
| } |
