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Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/ReduceSumTestImpl.cpp')
-rw-r--r-- | src/backends/backendsCommon/test/layerTests/ReduceSumTestImpl.cpp | 344 |
1 files changed, 344 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/ReduceSumTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ReduceSumTestImpl.cpp new file mode 100644 index 0000000000..4edbd1108a --- /dev/null +++ b/src/backends/backendsCommon/test/layerTests/ReduceSumTestImpl.cpp @@ -0,0 +1,344 @@ +// +// Copyright © 2020 Samsung Electronics Co Ltd and Contributors. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ReduceSumTestImpl.hpp" + +#include <backendsCommon/test/DataTypeUtils.hpp> +#include <backendsCommon/test/TensorCopyUtils.hpp> +#include <backendsCommon/test/WorkloadTestUtils.hpp> + +#include <test/TensorHelpers.hpp> + +namespace +{ + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<float, 4> ReduceTestCommon( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::TensorInfo inputTensorInfo, + const armnn::TensorInfo outputTensorInfo, + const std::vector<float>& inputData, + const std::vector<float>& outputData, + const std::vector<int32_t> vAxis, + const armnn::ReduceOperation reduceOperation) +{ + IgnoreUnused(memoryManager); + auto inputTensor = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>(inputData, inputTensorInfo)); + + LayerTestResult<float, 4> result(outputTensorInfo); + result.outputExpected = MakeTensor<float, 4>(outputTensorInfo, outputData); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::ReduceQueueDescriptor descriptor; + std::vector<uint32_t> updated_idx; + uint32_t resolvedAxis = 0; + for (uint32_t i = 0; i < vAxis.size(); ++i) + { + if (vAxis[i] < 0) + { + resolvedAxis = inputTensorInfo.GetNumDimensions() + static_cast<uint32_t>(vAxis[i]); + } else + { + resolvedAxis = static_cast<uint32_t>(vAxis[i]); + } + + updated_idx.push_back(resolvedAxis); + } + + descriptor.m_Parameters.m_vAxis = updated_idx; + descriptor.m_Parameters.m_ReduceOperation = reduceOperation; + armnn::WorkloadInfo info; + + AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateReduce(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), inputTensor.origin()); + + workload->Execute(); + + CopyDataFromITensorHandle(result.output.origin(), outputHandle.get()); + + return result; +} + +} // namespace + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<float, 4> ReduceSumSimpleTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + const armnn::TensorShape inputShape{ 1, 1, 1, 5 }; + const armnn::TensorShape outputShape{ 1, 1, 1, 1}; + + armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(1.0f); + inputTensorInfo.SetQuantizationOffset(0); + } + + armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); + + std::vector<float> inputValues({ 5.0f, 2.0f, 8.0f, 10.0f, 9.0f }); + std::vector<float> outputValues({ 34.0f }); + + return ReduceTestCommon<ArmnnType>(workloadFactory, + memoryManager, + tensorHandleFactory, + inputTensorInfo, + outputTensorInfo, + inputValues, + outputValues, + { -1 }, + armnn::ReduceOperation::Sum); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<float, 4> ReduceSumSingleAxisTest1( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + const armnn::TensorShape inputShape{ 1, 3, 2, 4 }; + const armnn::TensorShape outputShape{ 1, 1, 2, 4}; + + armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(1.0f); + inputTensorInfo.SetQuantizationOffset(0); + } + + armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); + + std::vector<float> inputValues({ 1.0f, 2.0f, 3.0f, 4.0f, + 5.0f, 6.0f, 7.0f, 8.0f, + + 10.0f, 20.0f, 30.0f, 40.0f, + 50.0f, 60.0f, 70.0f, 80.0f, + + 100.0f, 200.0f, 300.0f, 400.0f, + 500.0f, 600.0f, 700.0f, 800.0f }); + std::vector<float> outputValues({ 111.0f, 222.0f, 333.0f, 444.0f, + 555.0f, 666.0f, 777.0f, 888.0f }); + + return ReduceTestCommon<ArmnnType>(workloadFactory, + memoryManager, + tensorHandleFactory, + inputTensorInfo, + outputTensorInfo, + inputValues, + outputValues, + { 1 }, + armnn::ReduceOperation::Sum); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<float, 4> ReduceSumSingleAxisTest2( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + const armnn::TensorShape inputShape{ 1, 6, 3, 4 }; + const armnn::TensorShape outputShape{ 1, 1, 3, 4}; + + armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(1.0f); + inputTensorInfo.SetQuantizationOffset(0); + } + + armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); + + std::vector<float> inputValues( {7, 8, 6, 1, + 1, 1, 8, 7, + 3, 7, 7, 7, + + 6, 8, 4, 7, + 3, 8, 7, 3, + 5, 8, 8, 8, + + + 7, 8, 2, 7, + 3, 8, 5, 6, + 8, 4, 2, 7, + + 1, 6, 7, 2, + 8, 3, 3, 1, + 7, 6, 2, 6, + + + 5, 3, 4, 8, + 7, 8, 2, 4, + 6, 6, 2, 8, + + 2, 2, 7, 2, + 5, 3, 6, 3, + 6, 1, 8, 8}); + std::vector<float> outputValues({ 28.0f, 35.0f, 30.0f, 27.0f, + 27.0f, 31.0f, 31.0f, 24.0f, + 35.0f, 32.0f, 29.0f, 44.0f}); + + return ReduceTestCommon<ArmnnType>(workloadFactory, + memoryManager, + tensorHandleFactory, + inputTensorInfo, + outputTensorInfo, + inputValues, + outputValues, + { 1 }, + armnn::ReduceOperation::Sum); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<float, 4> ReduceSumSingleAxisTest3( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + const armnn::TensorShape inputShape{ 1, 6, 3, 4 }; + const armnn::TensorShape outputShape{ 1, 6, 3, 1}; + + armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(1.0f); + inputTensorInfo.SetQuantizationOffset(0); + } + + armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); + + std::vector<float> inputValues( {7, 8, 6, 1, + 1, 1, 8, 7, + 3, 7, 7, 7, + + 6, 8, 4, 7, + 3, 8, 7, 3, + 5, 8, 8, 8, + + + 7, 8, 2, 7, + 3, 8, 5, 6, + 8, 4, 2, 7, + + 1, 6, 7, 2, + 8, 3, 3, 1, + 7, 6, 2, 6, + + + 5, 3, 4, 8, + 7, 8, 2, 4, + 6, 6, 2, 8, + + 2, 2, 7, 2, + 5, 3, 6, 3, + 6, 1, 8, 8}); + std::vector<float> outputValues({ 22.0f, 17.0f, 24.0f, + 25.0f, 21.0f, 29.0f, + + 24.0f, 22.0f, 21.0f, + 16.0f, 15.0f, 21.0f, + + 20.0f, 21.0f, 22.0f, + 13.0f, 17.0f, 23.0f}); + + return ReduceTestCommon<ArmnnType>(workloadFactory, + memoryManager, + tensorHandleFactory, + inputTensorInfo, + outputTensorInfo, + inputValues, + outputValues, + { 3 }, + armnn::ReduceOperation::Sum); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<float, 4> ReduceSumMultipleAxisTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + const armnn::TensorShape inputShape{ 1, 3, 2, 4 }; + const armnn::TensorShape outputShape{ 1, 1, 1, 4}; + + armnn::TensorInfo inputTensorInfo(inputShape, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(1.0f); + inputTensorInfo.SetQuantizationOffset(0); + } + + armnn::TensorInfo outputTensorInfo(outputShape, armnn::DataType::Float32); + + std::vector<float> inputValues({ 1.0f, 2.0f, 3.0f, 4.0f, + 5.0f, 6.0f, 7.0f, 8.0f, + + 10.0f, 20.0f, 30.0f, 40.0f, + 50.0f, 60.0f, 70.0f, 80.0f, + + 100.0f, 200.0f, 300.0f, 400.0f, + 500.0f, 600.0f, 700.0f, 800.0f }); + std::vector<float> outputValues({ 666.0f, 888.0f, 1110.0f, 1332.0f }); + + return ReduceTestCommon<ArmnnType>(workloadFactory, + memoryManager, + tensorHandleFactory, + inputTensorInfo, + outputTensorInfo, + inputValues, + outputValues, + { 1, 2 }, + armnn::ReduceOperation::Sum); +} + +// Explicit template specializations + +template LayerTestResult<float, 4> +ReduceSumSimpleTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory); + +template LayerTestResult<float, 4> +ReduceSumSingleAxisTest1<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory); + +template LayerTestResult<float, 4> +ReduceSumSingleAxisTest2<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory); + +template LayerTestResult<float, 4> +ReduceSumSingleAxisTest3<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory); + +template LayerTestResult<float, 4> +ReduceSumMultipleAxisTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory); 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