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Diffstat (limited to 'src/backends/backendsCommon/test/LayerTests.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/LayerTests.cpp | 2684 |
1 files changed, 0 insertions, 2684 deletions
diff --git a/src/backends/backendsCommon/test/LayerTests.cpp b/src/backends/backendsCommon/test/LayerTests.cpp index 5fd8f3e641..2d71e60ca4 100644 --- a/src/backends/backendsCommon/test/LayerTests.cpp +++ b/src/backends/backendsCommon/test/LayerTests.cpp @@ -2789,1927 +2789,6 @@ LayerTestResult<float,3> ConcatTest( return ret; } -LayerTestResult<float,4> AdditionTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int batchSize = 2; - unsigned int channels = 2; - 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 = MakeTensor<float, 4>(inputTensorInfo1, std::vector<float>( - { - 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, - })); - - auto input2 = MakeTensor<float, 4>(inputTensorInfo2, std::vector<float>( - { - 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, - })); - - LayerTestResult<float,4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<float, 4>(outputTensorInfo, std::vector<float>( - { - 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, - })); - - 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, 5> Addition5dTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int depth = 2; - unsigned int batchSize = 2; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - - armnn::TensorInfo inputTensorInfo1, inputTensorInfo2; - armnn::TensorInfo outputTensorInfo; - - unsigned int shape[] = {depth, batchSize, channels, height, width}; - - inputTensorInfo1 = armnn::TensorInfo(5, shape, armnn::DataType::Float32); - inputTensorInfo2 = armnn::TensorInfo(5, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(5, shape, armnn::DataType::Float32); - - - auto input1 = MakeTensor<float, 5>(inputTensorInfo1, std::vector<float>( - { - 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, - - })); - - auto input2 = MakeTensor<float, 5>(inputTensorInfo2, std::vector<float>( - { - 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, - })); - - LayerTestResult<float, 5> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<float, 5>(outputTensorInfo, std::vector<float>( - { - 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, - })); - - 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][0]); - CopyDataToITensorHandle(inputHandle2.get(), &input2[0][0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0][0], outputHandle.get()); - - return ret; -} - -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<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; -} - -namespace { -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T, 4> DivisionTestHelper( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const unsigned int shape0[4], - const std::vector<T>& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<T> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<T> & outValues, - float outScale, - int32_t outOffset) -{ - armnn::TensorInfo inputTensorInfo0(4, shape0, ArmnnType); - armnn::TensorInfo inputTensorInfo1(4, shape1, ArmnnType); - armnn::TensorInfo outputTensorInfo(4, outShape, ArmnnType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::DivisionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateDivision(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult<float,4> DivisionByZeroTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 1.f, 1.f, 1.f, 1.f, 0.f, 0.f, 0.f, 0.f, - -1.f, -1.f, -1.f, -1.f, 5.f, 5.f, 5.f, 5.f }); - - std::vector<float> input1({ - 0.f, 0.f, -0.f, -0.f, 0.f, 0.f, -0.f, -0.f, - 0.f, 0.f, -0.f, -0.f, 5.f, 5.f, 5.f, 5.f }); - - std::vector<float> output({ - INFINITY, INFINITY, -INFINITY, -INFINITY, NAN, NAN, -NAN, -NAN, - -INFINITY, -INFINITY, INFINITY, INFINITY, 1, 1, 1, 1 }); - - return DivisionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 1.0f, 0); -} - -LayerTestResult<float,4> DivisionTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<float> input1({ - 1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector<float> output({ - 2, 2, 2, 2, 1.5, 1.5, 1.5, 1.5, - 1, 1, 1, 1, 1.25, 1.25, 1.25, 1.25 }); - - - return DivisionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 1.0f, 0); -} - -LayerTestResult<float, 4> DivisionBroadcast1ElementTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 2 }); - - std::vector<float> output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - - return DivisionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> DivisionBroadcast1DVectorTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<float> input0({ - 1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<float> input1({ 1, 2 }); - - std::vector<float> output({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<uint8_t,4> DivisionUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<uint8_t> input0({2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<uint8_t> input1({1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector<uint8_t> output({8, 8, 8, 8, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5}); - - - return DivisionTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 0.25f, 0); -} - -LayerTestResult<uint8_t, 4> DivisionBroadcast1ElementUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<uint8_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<uint8_t> input1({ 2 }); - - std::vector<uint8_t> output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - return DivisionTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<uint8_t, 4> DivisionBroadcast1DVectorUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<uint8_t> input0({1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<uint8_t> input1({ 1, 2 }); - - std::vector<uint8_t> output({1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<int16_t,4> DivisionInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape[] = { 2, 2, 2, 2 }; - - std::vector<int16_t> input0({2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<int16_t> input1({1, 1, 1, 1, 2, 2, 2, 2, - 4, 4, 4, 4, 4, 4, 4, 4 }); - - std::vector<int16_t> output({8, 8, 8, 8, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5}); - - - return DivisionTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape, input0, 1.0f, 0, - shape, input1, 1.0f, 0, - shape, output, 0.25f, 0); -} - -LayerTestResult<int16_t, 4> DivisionBroadcast1ElementInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<int16_t> input0({ 2, 4, 6, 8, 10, 12, 14, 16}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<int16_t> input1({ 2 }); - - std::vector<int16_t> output({ 1, 2, 3, 4, 5, 6, 7, 8}); - - return DivisionTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<int16_t, 4> DivisionBroadcast1DVectorInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<int16_t> input0({1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<int16_t> input1({ 1, 2 }); - - std::vector<int16_t> output({1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - return DivisionTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -template<typename DescriptorType> -std::unique_ptr<armnn::IWorkload> CreateWorkload( - const armnn::IWorkloadFactory& workloadFactory, - const armnn::WorkloadInfo& info, - const DescriptorType& descriptor) -{ - return CreateWorkload(workloadFactory, info, descriptor); -}; - -template<> -std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::MaximumQueueDescriptor>( - const armnn::IWorkloadFactory& workloadFactory, - const armnn::WorkloadInfo& info, - const armnn::MaximumQueueDescriptor& descriptor) -{ - return workloadFactory.CreateMaximum(descriptor, info); -} - -template<> -std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::MinimumQueueDescriptor>( - const armnn::IWorkloadFactory& workloadFactory, - const armnn::WorkloadInfo& info, - const armnn::MinimumQueueDescriptor& descriptor) -{ - return workloadFactory.CreateMinimum(descriptor, info); -} - -template<> -std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::EqualQueueDescriptor>( - const armnn::IWorkloadFactory& workloadFactory, - const armnn::WorkloadInfo& info, - const armnn::EqualQueueDescriptor& descriptor) -{ - return workloadFactory.CreateEqual(descriptor, info); -} - -template<> -std::unique_ptr<armnn::IWorkload> CreateWorkload<armnn::GreaterQueueDescriptor>( - const armnn::IWorkloadFactory& workloadFactory, - const armnn::WorkloadInfo& info, - const armnn::GreaterQueueDescriptor& descriptor) -{ - return workloadFactory.CreateGreater(descriptor, info); -} - -namespace { - -template <typename Descriptor, - armnn::DataType ArmnnTypeInput, - armnn::DataType ArmnnTypeOutput, - typename TInput = armnn::ResolveType<ArmnnTypeInput>, - typename TOutput = armnn::ResolveType<ArmnnTypeOutput>> -LayerTestResult<TOutput, 4> ElementwiseTestHelper( - armnn::IWorkloadFactory & workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager, - const unsigned int shape0[4], std::vector<TInput> values0, - const unsigned int shape1[4], std::vector<TInput> values1, - const unsigned int outShape[4], std::vector<TOutput> outValues, - float qScale = 0.0f, int qOffset = 0) -{ - const uint32_t dimensionCount = 4; - armnn::TensorInfo inputTensorInfo0{dimensionCount, shape0, ArmnnTypeInput}; - armnn::TensorInfo inputTensorInfo1{dimensionCount, shape1, ArmnnTypeInput}; - armnn::TensorInfo outputTensorInfo{dimensionCount, outShape, ArmnnTypeOutput}; - - auto input0 = MakeTensor<TInput, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<TInput, 4>(inputTensorInfo1, values1); - - if (armnn::IsQuantizedType<TInput>()) - { - inputTensorInfo0.SetQuantizationScale(qScale); - inputTensorInfo0.SetQuantizationOffset(qOffset); - - inputTensorInfo1.SetQuantizationScale(qScale); - inputTensorInfo1.SetQuantizationOffset(qOffset); - - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - LayerTestResult<TOutput,4> ret(outputTensorInfo); - - if(ArmnnTypeOutput == armnn::DataType::Boolean) - { - ret.compareBoolean = true; - } - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - Descriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - auto workload = CreateWorkload<Descriptor>(workloadFactory, info, data); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workload->PostAllocationConfigure(); - ExecuteWorkload(*workload, memoryManager); - - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - - ret.outputExpected = MakeTensor<TOutput, 4>(outputTensorInfo, outValues); - return ret; -} - -template <typename Descriptor, armnn::DataType ArmnnT, typename T = armnn::ResolveType<ArmnnT>> -LayerTestResult<T, 4> ElementwiseTestHelper( - armnn::IWorkloadFactory & workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager, - const unsigned int shape0[4], std::vector<T> values0, - const unsigned int shape1[4], std::vector<T> values1, - const unsigned int outShape[4], std::vector<T> outValues, - float qScale = 0.0f, int qOffset = 0) -{ - return ElementwiseTestHelper<Descriptor, ArmnnT, ArmnnT> - (workloadFactory, - memoryManager, - shape0, - values0, - shape1, - values1, - outShape, - outValues, - qScale, - qOffset); -} -} - -LayerTestResult<uint8_t, 4> EqualSimpleTest(armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ 1, 1, 1, 1, 5, 5, 5, 5, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<float> input1({ 1, 1, 1, 1, 3, 3, 3, 3, - 5, 5, 5, 5, 4, 4, 4, 4 }); - - std::vector<uint8_t> output({ 1, 1, 1, 1, 0, 0, 0, 0, - 0, 0, 0, 0, 1, 1, 1, 1 }); - - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::Float32, armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult<uint8_t, 4> EqualBroadcast1ElementTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 1 }); - - std::vector<uint8_t> output({ 1, 0, 0, 0, 0, 0, 0, 0}); - - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::Float32, armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<uint8_t, 4> EqualBroadcast1DVectorTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<float> input1({ 1, 2, 3}); - - std::vector<uint8_t> output({ 1, 1, 1, 0, 0, 0, - 0, 0, 0, 0, 0, 0 }); - - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, armnn::DataType::Float32, armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<uint8_t, 4> EqualUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape[] = { 2, 2, 2, 2 }; - - // See dequantized values to the right. - std::vector<uint8_t> input0({ 1, 1, 1, 1, 6, 6, 6, 6, - 3, 3, 3, 3, 7, 7, 7, 7 }); - - std::vector<uint8_t> input1({ 2, 2, 2, 2, 6, 6, 6, 6, - 3, 3, 3, 3, 5, 5, 5, 5 }); - - std::vector<uint8_t> output({ 0, 0, 0, 0, 1, 1, 1, 1, - 1, 1, 1, 1, 0, 0, 0, 0 }); - - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, - armnn::DataType::QuantisedAsymm8, - armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> EqualBroadcast1ElementUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<uint8_t> input1({ 1 }); - - std::vector<uint8_t> output({ 1, 0, 0, 0, 0, 0, - 0, 0, 0, 0, 0, 0 }); - - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, - armnn::DataType::QuantisedAsymm8, - armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> EqualBroadcast1DVectorUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<uint8_t> input1({ 1, 1, 3}); - - std::vector<uint8_t> output({ 1, 0, 1, 0, 0, 0, - 0, 0, 0, 0, 0, 0 }); - - return ElementwiseTestHelper<armnn::EqualQueueDescriptor, - armnn::DataType::QuantisedAsymm8, - armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> GreaterSimpleTest(armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ 1, 1, 1, 1, 5, 5, 5, 5, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<float> input1({ 1, 1, 1, 1, 3, 3, 3, 3, - 5, 5, 5, 5, 4, 4, 4, 4 }); - - std::vector<uint8_t> output({ 0, 0, 0, 0, 1, 1, 1, 1, - 0, 0, 0, 0, 0, 0, 0, 0 }); - - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::Float32, armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult<uint8_t, 4> GreaterBroadcast1ElementTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 1 }); - - std::vector<uint8_t> output({ 0, 1, 1, 1, 1, 1, 1, 1}); - - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::Float32, armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<uint8_t, 4> GreaterBroadcast1DVectorTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<float> input0({ 1, 2.9f, 2.1f, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<float> input1({ 1, 3, 2}); - - std::vector<uint8_t> output({ 0, 0, 1, 1, 1, 1, - 1, 1, 1, 1, 1, 1 }); - - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, armnn::DataType::Float32, armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<uint8_t, 4> GreaterUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape[] = { 2, 2, 2, 2 }; - - // See dequantized values to the right. - std::vector<uint8_t> input0({ 1, 1, 1, 1, 6, 6, 6, 6, - 3, 3, 3, 3, 5, 5, 5, 5 }); - - std::vector<uint8_t> input1({ 2, 2, 2, 2, 6, 6, 6, 6, - 2, 2, 2, 2, 5, 5, 5, 5 }); - - std::vector<uint8_t> output({ 0, 0, 0, 0, 0, 0, 0, 0, - 1, 1, 1, 1, 0, 0, 0, 0 }); - - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, - armnn::DataType::QuantisedAsymm8, - armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> GreaterBroadcast1ElementUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<uint8_t> input1({ 1 }); - - std::vector<uint8_t> output({ 0, 1, 1, 1, 1, 1, - 1, 1, 1, 1, 1, 1 }); - - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, - armnn::DataType::QuantisedAsymm8, - armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> GreaterBroadcast1DVectorUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<uint8_t> input1({ 1, 1, 3}); - - std::vector<uint8_t> output({ 0, 1, 0, 1, 1, 1, - 1, 1, 1, 1, 1, 1 }); - - return ElementwiseTestHelper<armnn::GreaterQueueDescriptor, - armnn::DataType::QuantisedAsymm8, - armnn::DataType::Boolean>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<float, 4> MaximumSimpleTest(armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ 1, 1, 1, 1, 5, 5, 5, 5, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<float> input1({ 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<float> output({ 2, 2, 2, 2, 5, 5, 5, 5, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::Float32>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult<float, 4> MaximumBroadcast1ElementTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 2 }); - - std::vector<float> output({ 2, 2, 3, 4, 5, 6, 7, 8}); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::Float32>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<float, 4> MaximumBroadcast1DVectorTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<float> input1({ 1, 2, 3}); - - std::vector<float> output({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::Float32>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<uint8_t, 4> MaximumUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape[] = { 2, 2, 2, 2 }; - - // See dequantized values to the right. - std::vector<uint8_t> input0({ 1, 1, 1, 1, 6, 6, 6, 6, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<uint8_t> input1({ 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<uint8_t> output({ 2, 2, 2, 2, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> MaximumBroadcast1ElementUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<uint8_t> input1({2}); - - std::vector<uint8_t> output({ 2, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> MaximumBroadcast1DVectorUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<uint8_t> input1({ 1, 10, 3}); - - std::vector<uint8_t> output({ 1, 10, 3, 4, 10, 6, - 7, 10, 9, 10, 11, 12 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MaximumInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape[] = { 2, 2, 2, 2 }; - - std::vector<int16_t> input0({ 1, 1, 1, 1, 6, 6, 6, 6, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<int16_t> input1({ 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<int16_t> output({ 2, 2, 2, 2, 6, 6, 6, 6, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedSymm16>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MaximumBroadcast1ElementInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<int16_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<int16_t> input1({2}); - - std::vector<int16_t> output({ 2, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedSymm16>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MaximumBroadcast1DVectorInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<int16_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<int16_t> input1({ 1, 10, 3}); - - std::vector<int16_t> output({ 1, 10, 3, 4, 10, 6, - 7, 10, 9, 10, 11, 12 }); - - return ElementwiseTestHelper<armnn::MaximumQueueDescriptor, armnn::DataType::QuantisedSymm16>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<float, 4> MinimumBroadcast1ElementTest1( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 2 }); - - std::vector<float> output({ 1, 2, 2, 2, 2, 2, 2, 2}); - - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::Float32>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - - -LayerTestResult<float, 4> MinimumBroadcast1ElementTest2( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 6, 3, 2, 8, 9, 1, 10}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 5 }); - - std::vector<float> output({ 1, 5, 3, 2, 5, 5, 1, 5}); - - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::Float32>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<uint8_t, 4> MinimumBroadcast1DVectorUint8Test( - armnn::IWorkloadFactory & workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr & memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0({ 1, 2, 3, 3, 2, 1, - 7, 1, 2, 3, 4, 5 }); - - std::vector<uint8_t> input1({ 1, 2, 3}); - - std::vector<uint8_t> output({ 1, 2, 3, 1, 2, 1, - 1, 1, 2, 1, 2, 3 }); - - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::QuantisedAsymm8>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MinimumInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape[] = { 2, 2, 2, 2 }; - - std::vector<int16_t> input0({ 1, 1, 1, 1, 6, 6, 6, 6, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<int16_t> input1({ 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<int16_t> output({ 1, 1, 1, 1, 3, 3, 3, 3, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::QuantisedSymm16>( - workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MinimumBroadcast1ElementInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<int16_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<int16_t> input1({2}); - - std::vector<int16_t> output({ 1, 2, 2, 2, 2, 2, - 2, 2, 2, 2, 2, 2 }); - - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::QuantisedSymm16>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MinimumBroadcast1DVectorInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<int16_t> input0({ 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 }); - - std::vector<int16_t> input1({ 1, 10, 3}); - - std::vector<int16_t> output({ 1, 2, 3, 1, 5, 3, - 1, 8, 3, 1, 10, 3 }); - - return ElementwiseTestHelper<armnn::MinimumQueueDescriptor, armnn::DataType::QuantisedSymm16>( - workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output, - 1.0f, - 0); -} - -namespace { -template<std::size_t NumDims> -LayerTestResult<float,NumDims> MultiplicationTestHelper( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const unsigned int shape0[NumDims], - const std::vector<float> & values0, - const unsigned int shape1[NumDims], - const std::vector<float> & values1, - const unsigned int outShape[NumDims], - const std::vector<float> & outValues) -{ - armnn::TensorInfo inputTensorInfo0{NumDims, shape0, armnn::DataType::Float32}; - armnn::TensorInfo inputTensorInfo1{NumDims, shape1, armnn::DataType::Float32}; - armnn::TensorInfo outputTensorInfo{NumDims, outShape, armnn::DataType::Float32}; - - auto input0 = MakeTensor<float, NumDims>(inputTensorInfo0, values0); - auto input1 = MakeTensor<float, NumDims>(inputTensorInfo1, values1); - - LayerTestResult<float,NumDims> ret(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), input0.origin()); - CopyDataToITensorHandle(inputHandle1.get(), input1.origin()); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get()); - - ret.outputExpected = MakeTensor<float, NumDims>(outputTensorInfo, outValues); - return ret; -} -} // anonymous namespace - - -LayerTestResult<float,4> MultiplicationTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 2; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - - unsigned int shape[] = { batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 1, 1, 1, 1, 2, 2, 2, 2, - 3, 3, 3, 3, 4, 4, 4, 4 }); - - std::vector<float> input1({ - 2, 2, 2, 2, 3, 3, 3, 3, - 4, 4, 4, 4, 5, 5, 5, 5 }); - - std::vector<float> output({ - 2, 2, 2, 2, 6, 6, 6, 6, - 12, 12, 12, 12, 20, 20, 20, 20 }); - - return MultiplicationTestHelper<4>(workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult<float,5> Multiplication5dTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int width = 3; - const unsigned int height = 2; - const unsigned int channelCount = 2; - const unsigned int batchSize = 2; - const unsigned int depth = 2; - - unsigned int shape[] = { depth, batchSize, channelCount, height, width }; - - std::vector<float> input0({ - 1.80f, 0.20f, 2.30f, 1.30f, 2.10f, 1.00f, - 2.60f, 0.60f, 2.10f, 2.30f, 2.30f, 2.00f, - - 2.50f, 1.00f, 2.90f, 3.10f, 1.50f, 2.40f, - 2.80f, 1.10f, 1.00f, 3.20f, 1.00f, 2.30f, - - - 0.30f, 2.20f, 1.00f, 0.20f, 1.60f, 1.40f, - 0.80f, 3.20f, 0.10f, 0.10f, 3.10f, 2.10f, - - 1.50f, 2.40f, 1.40f, 0.70f, 2.40f, 1.40f, - 1.60f, 1.20f, 1.90f, 0.80f, 0.00f, 0.10f, - }); - - std::vector<float> input1({ - 0.70f, 1.00f, 2.90f, 2.20f, 3.10f, 2.80f, - 1.80f, 2.00f, 0.50f, 2.30f, 1.20f, 2.70f, - - 2.40f, 0.20f, 3.20f, 1.60f, 0.20f, 2.50f, - 2.30f, 0.70f, 2.70f, 1.80f, 2.90f, 2.70f, - - - 3.20f, 3.20f, 0.70f, 1.90f, 2.70f, 2.50f, - 2.40f, 0.90f, 2.30f, 1.80f, 2.50f, 2.00f, - - 1.60f, 2.20f, 1.60f, 2.00f, 0.30f, 3.20f, - 0.40f, 3.00f, 2.60f, 0.30f, 0.00f, 2.50f, - }); - - std::vector<float> output({ - 1.26f, 0.20f, 6.67f, 2.86f, 6.51f, 2.80f, - 4.68f, 1.20f, 1.05f, 5.29f, 2.76f, 5.40f, - - 6.00f, 0.20f, 9.28f, 4.96f, 0.30f, 6.00f, - 6.44f, 0.77f, 2.70f, 5.76f, 2.90f, 6.21f, - - - 0.96f, 7.04f, 0.70f, 0.38f, 4.32f, 3.50f, - 1.92f, 2.88f, 0.23f, 0.18f, 7.75f, 4.20f, - - 2.40f, 5.28f, 2.24f, 1.40f, 0.72f, 4.48f, - 0.64f, 3.60f, 4.94f, 0.24f, 0.00f, 0.25f, - }); - - return MultiplicationTestHelper<5>(workloadFactory, - memoryManager, - shape, - input0, - shape, - input1, - shape, - output); -} - -LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 2, 2, 2 }; - std::vector<float> input0({ 1, 2, 3, 4, 5, 6, 7, 8}); - - unsigned int shape1[] = { 1, 1, 1, 1 }; - std::vector<float> input1({ 2 }); - - std::vector<float> output({ 2, 4, 6, 8, 10, 12, 14, 16}); - - return MultiplicationTestHelper<4>(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<float, 4> MultiplicationBroadcast1DVectorTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int shape0[] = { 1, 3, 3, 2 }; - std::vector<float> input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12, - 13, 14, 15, 16, 17, 18}); - - unsigned int shape1[] = { 1, 1, 1, 2 }; - std::vector<float> input1({ 1, 2 }); - - std::vector<float> output({ - 1, 4, 3, 8, 5, 12, - 7, 16, 9, 20, 11, 24, - 13, 28, 15, 32, 17, 36}); - - return MultiplicationTestHelper<4>(workloadFactory, - memoryManager, - shape0, - input0, - shape1, - input1, - shape0, - output); -} - -LayerTestResult<float,4> CompareMultiplicationTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - armnn::IWorkloadFactory& refWorkloadFactory) -{ - const unsigned int width = 16; - const unsigned int height = 32; - const unsigned int channelCount = 2; - const unsigned int batchSize = 5; - - armnn::TensorInfo inputTensorInfo0; - armnn::TensorInfo inputTensorInfo1; - armnn::TensorInfo outputTensorInfo; - - constexpr unsigned int shape[] = { batchSize, channelCount, height, width }; - - inputTensorInfo0 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - inputTensorInfo1 = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); - - LayerTestResult<float,4> comparisonResult(outputTensorInfo); - - auto input0 = MakeRandomTensor<float, 4>(inputTensorInfo0, 803506992); - auto input1 = MakeRandomTensor<float, 4>(inputTensorInfo1, 54902257); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1Ref = refWorkloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - armnn::MultiplicationQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo0, inputHandle0Ref.get()); - SetWorkloadInput(refData, refInfo, 1, inputTensorInfo1, inputHandle1Ref.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreateMultiplication(refData, refInfo); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - inputHandle0Ref->Allocate(); - inputHandle1Ref->Allocate(); - outputHandleRef->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - CopyDataToITensorHandle(inputHandle0Ref.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1Ref.get(), &input1[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - workloadRef->PostAllocationConfigure(); - workloadRef->Execute(); - CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); - - return comparisonResult; -} - LayerTestResult<float,4> CompareBatchNormTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -8467,664 +6546,6 @@ LayerTestResult<uint16_t, 3> ConcatUint16Test( return ret; } -namespace -{ -template <typename T> -LayerTestResult<T, 4> AdditionQuantizeTestHelper( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const unsigned int shape0[4], - const std::vector<T>& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<T> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<T> & outValues, - float outScale, - int32_t outOffset) -{ - auto dataType = (std::is_same<T, uint8_t>::value ? - armnn::DataType::QuantisedAsymm8 : - armnn::DataType::QuantisedSymm16); - - armnn::TensorInfo inputTensorInfo0(4, shape0, dataType); - armnn::TensorInfo inputTensorInfo1(4, shape1, dataType); - armnn::TensorInfo outputTensorInfo(4, outShape, dataType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::AdditionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateAddition(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -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 AdditionQuantizeTestHelper(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 AdditionQuantizeTestHelper(workloadFactory, - memoryManager, - shape0, input0, 7.0f, 0, - shape1, input1, 7.0f, 0, - shape0, output, 7.0f, 0); -} - -namespace -{ -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T, 4> MultiplicationQuantizeTestHelper( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const unsigned int shape0[4], - const std::vector<T> & values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<T> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<T> & outValues, - float outScale, - int32_t outOffset) -{ - armnn::TensorInfo inputTensorInfo0(4, shape0, ArmnnType); - armnn::TensorInfo inputTensorInfo1(4, shape1, ArmnnType); - armnn::TensorInfo outputTensorInfo(4, outShape, ArmnnType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::MultiplicationQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateMultiplication(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult<uint8_t, 4> MultiplicationUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - unsigned int batchSize = 1; - unsigned int channels = 2; - unsigned int height = 2; - unsigned int width = 3; - const unsigned int shape[] = { batchSize, channels, height, width }; - - // See dequantized values to the right. - std::vector<uint8_t> input0({ - 62, 37, 3, 172, 13, 111, // 244, 144, 8, 684, 48, 440, - 188, 20, 73, 31, 23, 31 // 748, 76, 288, 120, 88, 120 - }); - - // See dequantized values to the right. - std::vector<uint8_t> input1({ - 126, 240, 252, 183, 121, 247, // 384, 726, 762, 555, 369, 747, - 48, 115, 151, 79, 78, 97 // 150, 351, 459, 243, 240, 297 - }); - - // See dequantized values to the right. - std::vector<uint8_t> output( - { - 64, 72, 0, 255, 8, 236, // 93696, 104544, 6096(clamped), 379620(clamped), 17712, 328680, - 77, 15, 92, 16, 10, 21, // 112200, 26676, 132192, 29160, 21120, 35640 - }); - - // Scale/offset chosen to have output values out of range. - return MultiplicationQuantizeTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape, - input0, - 4.0f, - 1, - shape, - input1, - 3.0f, - -2, - shape, - output, - 1366.255f, - -5); -} - -LayerTestResult<uint8_t, 4> MultiplicationBroadcast1ElementUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<uint8_t> input1({2}); - - std::vector<uint8_t> output({ - 2, 4, 6, 8, 10, 12, - 14, 16, 18, 20, 22, 24 - }); - - return MultiplicationQuantizeTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<uint8_t, 4> MultiplicationBroadcast1DVectorUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<uint8_t> input0({ - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<uint8_t> input1({1, 2, 3}); - - std::vector<uint8_t> output({ - 1, 4, 9, 4, 10, 18, - 7, 16, 27, 10, 22, 36 - }); - - return MultiplicationQuantizeTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MultiplicationInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape[] = { 1, 2, 2, 3 }; - - std::vector<int16_t> input0( - { - 6, 7, 8, 9, 10, 11, - 12, 13, 14, 15, 16, 17 - }); - - std::vector<int16_t> input1( - { - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<int16_t> output( - { - 6, 14, 24, 36, 50, 66, - 84, 104, 126, 150, 176, 204 - }); - - return MultiplicationQuantizeTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape, - input0, - 1.0f, - 0, - shape, - input1, - 1.0f, - 0, - shape, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MultiplicationBroadcast1ElementInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<int16_t> input0( - { - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<int16_t> input1({2}); - - std::vector<int16_t> output( - { - 2, 4, 6, 8, 10, 12, - 14, 16, 18, 20, 22, 24 - }); - - return MultiplicationQuantizeTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -LayerTestResult<int16_t, 4> MultiplicationBroadcast1DVectorInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 2, 2, 3 }; - const unsigned int shape1[] = { 1, 1, 1, 3 }; - - std::vector<int16_t> input0( - { - 1, 2, 3, 4, 5, 6, - 7, 8, 9, 10, 11, 12 - }); - - std::vector<int16_t> input1({1, 2, 3}); - - std::vector<int16_t> output( - { - 1, 4, 9, 4, 10, 18, - 7, 16, 27, 10, 22, 36 - }); - - return MultiplicationQuantizeTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, - input0, - 1.0f, - 0, - shape1, - input1, - 1.0f, - 0, - shape0, - output, - 1.0f, - 0); -} - -namespace -{ -template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T, 4> SubtractionTestHelper( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const unsigned int shape0[4], - const std::vector<T>& values0, - float scale0, - int32_t offset0, - const unsigned int shape1[4], - const std::vector<T> & values1, - float scale1, - int32_t offset1, - const unsigned int outShape[4], - const std::vector<T> & outValues, - float outScale, - int32_t outOffset) -{ - armnn::TensorInfo inputTensorInfo0(4, shape0, ArmnnType); - armnn::TensorInfo inputTensorInfo1(4, shape1, ArmnnType); - armnn::TensorInfo outputTensorInfo(4, outShape, ArmnnType); - - inputTensorInfo0.SetQuantizationScale(scale0); - inputTensorInfo0.SetQuantizationOffset(offset0); - - inputTensorInfo1.SetQuantizationScale(scale1); - inputTensorInfo1.SetQuantizationOffset(offset1); - - outputTensorInfo.SetQuantizationScale(outScale); - outputTensorInfo.SetQuantizationOffset(outOffset); - - auto input0 = MakeTensor<T, 4>(inputTensorInfo0, values0); - auto input1 = MakeTensor<T, 4>(inputTensorInfo1, values1); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outValues); - - std::unique_ptr<armnn::ITensorHandle> inputHandle0 = workloadFactory.CreateTensorHandle(inputTensorInfo0); - std::unique_ptr<armnn::ITensorHandle> inputHandle1 = workloadFactory.CreateTensorHandle(inputTensorInfo1); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::SubtractionQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo0, inputHandle0.get()); - AddInputToWorkload(data, info, inputTensorInfo1, inputHandle1.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSubtraction(data, info); - - inputHandle0->Allocate(); - inputHandle1->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle0.get(), &input0[0][0][0][0]); - CopyDataToITensorHandle(inputHandle1.get(), &input1[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - return result; -} -} // anonymous namespace - -LayerTestResult<uint8_t, 4> SubtractionUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector<uint8_t> input0({ 10, 12, 14, 16 }); - std::vector<uint8_t> input1({ 1, 2, 1, 2 }); - std::vector<uint8_t> output({ 3, 3, 5, 5 }); - - return SubtractionTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, input0, 0.5f, 2, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<uint8_t, 4> SubtractionBroadcast1ElementUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<uint8_t> input0({ 10, 12, 14, 16 }); - std::vector<uint8_t> input1({ 2 }); - std::vector<uint8_t> output({ 5, 6, 7, 8 }); - - return SubtractionTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, input0, 0.5f, 2, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 3); -} - -LayerTestResult<uint8_t, 4> SubtractionBroadcastUint8Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 1 }; - - std::vector<uint8_t> input0({ 10, 12, 14, 16 }); - std::vector<uint8_t> input1({ 2, 1 }); - std::vector<uint8_t> output({ 8, 11, 12, 15 }); - - return SubtractionTestHelper<armnn::DataType::QuantisedAsymm8>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> SubtractionTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector<float> input0({ 1, 2, 3, 4 }); - std::vector<float> input1({ 1, -1, 0, 2 }); - std::vector<float> output({ 0, 3, 3, 2 }); - - return SubtractionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> SubtractionBroadcast1ElementTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<float> input0({ 1, 2, 3, 4 }); - std::vector<float> input1({ 10 }); - std::vector<float> output({ -9, -8, -7, -6 }); - - return SubtractionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<float, 4> SubtractionBroadcastTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 2 }; - - std::vector<float> input0({ 1, 2, 3, 4 }); - std::vector<float> input1({ 10, -5 }); - std::vector<float> output({ -9, 7, -7, 9 }); - - return SubtractionTestHelper<armnn::DataType::Float32>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<int16_t, 4> SubtractionInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 2 }; - - std::vector<int16_t> input0({ 10, 12, 14, 16 }); - std::vector<int16_t> input1({ 1, 2, 1, 2 }); - std::vector<int16_t> output({ 3, 3, 5, 5 }); - - return SubtractionTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, input0, 0.5f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<int16_t, 4> SubtractionBroadcast1ElementInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 1, 1 }; - - std::vector<int16_t> input0({ 10, 12, 14, 16 }); - std::vector<int16_t> input1({ 2 }); - std::vector<int16_t> output({ 3, 4, 5, 6 }); - - return SubtractionTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, input0, 0.5f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - -LayerTestResult<int16_t, 4> SubtractionBroadcastInt16Test( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) -{ - const unsigned int shape0[] = { 1, 1, 2, 2 }; - const unsigned int shape1[] = { 1, 1, 2, 1 }; - - std::vector<int16_t> input0({ 10, 12, 14, 16 }); - std::vector<int16_t> input1({ 2, 1 }); - std::vector<int16_t> output({ 8, 11, 12, 15 }); - - return SubtractionTestHelper<armnn::DataType::QuantisedSymm16>(workloadFactory, - memoryManager, - shape0, input0, 1.0f, 0, - shape1, input1, 1.0f, 0, - shape0, output, 1.0f, 0); -} - LayerTestResult<float, 4> BatchNormTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) @@ -10021,111 +7442,6 @@ LayerTestResult<float, 2> FullyConnectedLargeTest( return FullyConnectedLargeTestCommon<armnn::DataType::Float32>(workloadFactory, memoryManager, transposeWeights); } -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> SpaceToBatchNdSimpleFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) |