diff options
Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp | 218 |
1 files changed, 218 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp index 66a26cc41d..c719472711 100644 --- a/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/UnidirectionalSequenceLstmTestImpl.cpp @@ -17,6 +17,190 @@ namespace { template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 3> +UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const std::vector<T>& input, + const std::vector<T>& outputExpected, + const armnn::TensorShape& inputShape, + const armnn::TensorShape& outputExpectedShape, + float qScale = 0.0f, + int32_t qOffset = 0, + armnn::DataType constantDataType = armnn::DataType::Float32) +{ + IgnoreUnused(memoryManager); + unsigned int batchSize = armnn::numeric_cast<unsigned int>(inputShape[1]); + unsigned int inputSize = armnn::numeric_cast<unsigned int>(inputShape[2]); + unsigned int outputSize = armnn::numeric_cast<unsigned int>(outputExpectedShape[2]); + unsigned numUnits = outputSize; + + armnn::TensorInfo inputTensorInfo({1, batchSize , inputSize}, ArmnnType, qScale, qOffset ); + armnn::TensorInfo cellStateInTensorInfo({batchSize , numUnits}, ArmnnType, qScale, qOffset); + armnn::TensorInfo outputStateInTensorInfo({batchSize , outputSize}, ArmnnType, qScale, qOffset); + + armnn::TensorInfo outputTensorInfo({1, batchSize, outputSize}, ArmnnType, qScale, qOffset); + + std::vector<T> inputVector; + inputVector.assign(input.data(), input.data() + (batchSize * inputSize)); + + std::vector<T> cellStateInVector(batchSize * numUnits, T()); + std::vector<T> outputStateInVector(batchSize * outputSize, T()); + + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); + + std::vector<T> outputVector; + outputVector.assign(outputExpected.data(), outputExpected.data() + (batchSize * outputSize)); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> cellStateInHandle = + tensorHandleFactory.CreateTensorHandle(cellStateInTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputStateInHandle = + tensorHandleFactory.CreateTensorHandle(outputStateInTensorInfo); + + std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); + + armnn::UnidirectionalSequenceLstmQueueDescriptor data; + armnn::WorkloadInfo info; + + AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); + AddInputToWorkload(data, info, outputStateInTensorInfo, outputStateInHandle.get()); + AddInputToWorkload(data, info, cellStateInTensorInfo, cellStateInHandle.get()); + + AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); + + armnn::TensorInfo tensorInfo4({numUnits}, constantDataType , qScale, qOffset); + armnn::TensorInfo tensorInfo8({numUnits, 2}, constantDataType, qScale, qOffset); + armnn::TensorInfo tensorInfo16({numUnits, 4}, constantDataType, qScale, qOffset); + + std::vector<float> inputToInputWeights = {-0.45018822f, -0.02338299f, -0.0870589f, + -0.34550029f, 0.04266912f, -0.15680569f, + -0.34856534f, 0.43890524f}; + + std::vector<float> inputToForgetWeights = { 0.09701663f, 0.20334584f, -0.50592935f, + -0.31343272f, -0.40032279f, 0.44781327f, + 0.01387155f, -0.35593212f}; + + std::vector<float> inputToCellWeights = { -0.50013041f, 0.1370284f, 0.11810488f, 0.2013163f, + -0.20583314f, 0.44344562f, 0.22077113f, + -0.29909778f}; + + std::vector<float> inputToOutputWeights = { -0.25065863f, -0.28290087f, 0.04613829f, + 0.40525138f, 0.44272184f, 0.03897077f, + -0.1556896f, 0.19487578f}; + + std::vector<float> recurrentToInputWeights = {-0.0063535f, -0.2042388f, 0.31454784f, + -0.35746509f, 0.28902304f, 0.08183324f, + -0.16555229f, 0.02286911f, -0.13566875f, + 0.03034258f, 0.48091322f, -0.12528998f, + 0.24077177f, -0.51332325f, -0.33502164f, + 0.10629296f}; + + std::vector<float> recurrentToForgetWeights = { -0.48684245f, -0.06655136f, 0.42224967f, + 0.2112639f, 0.27654213f, 0.20864892f, + -0.07646349f, 0.45877004f, 0.00141793f, + -0.14609534f, 0.36447752f, 0.09196436f, + 0.28053468f, 0.01560611f, -0.20127171f, + -0.01140004f}; + + std::vector<float> recurrentToCellWeights = { -0.3407414f, 0.24443203f, -0.2078532f, + 0.26320225f, 0.05695659f, -0.00123841f, + -0.4744786f, -0.35869038f, -0.06418842f, + -0.13502428f, -0.501764f, 0.22830659f, + -0.46367589f, 0.26016325f, -0.03894562f, + -0.16368064f}; + + std::vector<float> recurrentToOutputWeights = { 0.43385774f, -0.17194885f, 0.2718237f, + 0.09215671f, 0.24107647f, -0.39835793f, + 0.18212086f, 0.01301402f, 0.48572797f, + -0.50656658f, 0.20047462f, -0.20607421f, + -0.51818722f, -0.15390486f, 0.0468148f, + 0.39922136f}; + + std::vector<float> cellToInputWeights = {0., 0., 0., 0.}; + + std::vector<float> inputGateBias = {0., 0., 0., 0.}; + + std::vector<float> forgetGateBias = {1., 1., 1., 1.}; + + std::vector<float> cellBias = {0., 0., 0., 0.}; + + std::vector<float> outputGateBias = {0., 0., 0., 0.}; + + armnn::ScopedTensorHandle inputToInputWeightsTensor(tensorInfo8); + armnn::ScopedTensorHandle inputToForgetWeightsTensor(tensorInfo8); + armnn::ScopedTensorHandle inputToCellWeightsTensor(tensorInfo8); + armnn::ScopedTensorHandle inputToOutputWeightsTensor(tensorInfo8); + armnn::ScopedTensorHandle recurrentToInputWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToForgetWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToCellWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle recurrentToOutputWeightsTensor(tensorInfo16); + armnn::ScopedTensorHandle cellToInputWeightsTensor(tensorInfo4); + armnn::ScopedTensorHandle inputGateBiasTensor(tensorInfo4); + armnn::ScopedTensorHandle forgetGateBiasTensor(tensorInfo4); + armnn::ScopedTensorHandle cellBiasTensor(tensorInfo4); + armnn::ScopedTensorHandle outputGateBiasTensor(tensorInfo4); + + AllocateAndCopyDataToITensorHandle(&inputToInputWeightsTensor, inputToInputWeights.data()); + AllocateAndCopyDataToITensorHandle(&inputToForgetWeightsTensor, inputToForgetWeights.data()); + AllocateAndCopyDataToITensorHandle(&inputToCellWeightsTensor, inputToCellWeights.data()); + AllocateAndCopyDataToITensorHandle(&inputToOutputWeightsTensor, inputToOutputWeights.data()); + AllocateAndCopyDataToITensorHandle(&recurrentToInputWeightsTensor, recurrentToInputWeights.data()); + AllocateAndCopyDataToITensorHandle(&recurrentToForgetWeightsTensor, recurrentToForgetWeights.data()); + AllocateAndCopyDataToITensorHandle(&recurrentToCellWeightsTensor, recurrentToCellWeights.data()); + AllocateAndCopyDataToITensorHandle(&recurrentToOutputWeightsTensor, recurrentToOutputWeights.data()); + AllocateAndCopyDataToITensorHandle(&cellToInputWeightsTensor, cellToInputWeights.data()); + AllocateAndCopyDataToITensorHandle(&inputGateBiasTensor, inputGateBias.data()); + AllocateAndCopyDataToITensorHandle(&forgetGateBiasTensor, forgetGateBias.data()); + AllocateAndCopyDataToITensorHandle(&cellBiasTensor, cellBias.data()); + AllocateAndCopyDataToITensorHandle(&outputGateBiasTensor, outputGateBias.data()); + + data.m_InputToInputWeights = &inputToInputWeightsTensor; + data.m_InputToForgetWeights = &inputToForgetWeightsTensor; + data.m_InputToCellWeights = &inputToCellWeightsTensor; + data.m_InputToOutputWeights = &inputToOutputWeightsTensor; + data.m_RecurrentToInputWeights = &recurrentToInputWeightsTensor; + data.m_RecurrentToForgetWeights = &recurrentToForgetWeightsTensor; + data.m_RecurrentToCellWeights = &recurrentToCellWeightsTensor; + data.m_RecurrentToOutputWeights = &recurrentToOutputWeightsTensor; + data.m_InputGateBias = &inputGateBiasTensor; + data.m_ForgetGateBias = &forgetGateBiasTensor; + data.m_CellBias = &cellBiasTensor; + data.m_OutputGateBias = &outputGateBiasTensor; + + // Flags to set test configuration + data.m_Parameters.m_ActivationFunc = 4; + data.m_Parameters.m_CifgEnabled = false; + data.m_Parameters.m_PeepholeEnabled = false; + data.m_Parameters.m_ProjectionEnabled = false; + data.m_Parameters.m_ClippingThresCell = 10; + data.m_Parameters.m_ClippingThresProj = 0; + data.m_Parameters.m_TimeMajor = true; + + std::unique_ptr<armnn::IWorkload> workload + = workloadFactory.CreateWorkload(armnn::LayerType::UnidirectionalSequenceLstm, data, info); + inputHandle->Allocate(); + outputStateInHandle->Allocate(); + cellStateInHandle->Allocate(); + + outputHandle->Allocate(); + + CopyDataToITensorHandle(inputHandle.get(), inputVector.data()); + CopyDataToITensorHandle(outputStateInHandle.get(), outputStateInVector.data()); + CopyDataToITensorHandle(cellStateInHandle.get(), cellStateInVector.data()); + + workload->Execute(); + + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); + + return LayerTestResult<T, 3>(actualOutput, + outputVector, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 3> UnidirectionalSequenceLstmLayerFloat32TestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -369,6 +553,40 @@ UnidirectionalSequenceLstmLayerFloat32TimeMajorTestImpl( } // anonymous namespace +LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32TimeMajorSingleBatchTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) +{ + armnn::TensorInfo inputDesc({1, 2, 2}, armnn::DataType::Float32); + std::vector<float> input = {2., 3., 3., 4.}; + + armnn::TensorInfo outputDesc({1, 2, 4}, armnn::DataType::Float32); + std::vector<float> expectedOutput = + {-0.02973187f, 0.1229473f, 0.20885126f, -0.15358765f, + -0.0185422f, 0.11281417f, 0.24466537f, -0.1826292f}; + + return UnidirectionalSequenceLstmTimeMajorSingleBatchTestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, + input, expectedOutput, inputDesc.GetShape(), outputDesc.GetShape()); +} + +LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32BatchMajorSingleBatchTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory) { + armnn::TensorInfo inputInfo({3, 1, 3}, armnn::DataType::Float32); + std::vector<float> input = { 1., 2., 3., 4., 5., 4., 3., 2., 1. }; + + armnn::TensorInfo outputInfo({3, 1, 4}, armnn::DataType::Float32); + std::vector<float> expectedOutput = { -0.0714901f, -0.162117f, -0.175168f, -0.0232934f, + -0.0424661f, -0.231802f, -0.513374f, -0.00680323f, + -0.0668735f, 0.204078f, -0.42765f, -0.0312321f }; + return UnidirectionalSequenceLstmLayerFloat32TestImpl<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, + input, expectedOutput, inputInfo.GetShape(), outputInfo.GetShape()); +} + LayerTestResult<float, 3> UnidirectionalSequenceLstmLayerFloat32Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |