| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "CpuTensorHandle.hpp" |
| #include "WorkloadFactory.hpp" |
| |
| |
| #include <Layer.hpp> |
| #include <LayersFwd.hpp> |
| |
| #include <armnn/Types.hpp> |
| #include <armnn/LayerSupport.hpp> |
| #include <armnn/ILayerSupport.hpp> |
| |
| #include <backendsCommon/BackendRegistry.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| #include <backendsCommon/IBackendInternal.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <boost/cast.hpp> |
| #include <boost/iterator/transform_iterator.hpp> |
| |
| #include <cstring> |
| #include <sstream> |
| |
| namespace armnn |
| { |
| |
| namespace |
| { |
| |
| const TensorInfo OverrideDataType(const TensorInfo& info, Optional<DataType> type) |
| { |
| if (!type) |
| { |
| return info; |
| } |
| |
| return TensorInfo(info.GetShape(), type.value(), info.GetQuantizationScale(), info.GetQuantizationOffset()); |
| } |
| |
| } // anonymous namespace |
| |
| bool IWorkloadFactory::IsLayerSupported(const BackendId& backendId, |
| const IConnectableLayer& connectableLayer, |
| Optional<DataType> dataType, |
| std::string& outReasonIfUnsupported) |
| { |
| Optional<std::string&> reason = outReasonIfUnsupported; |
| bool result; |
| const Layer& layer = *(boost::polymorphic_downcast<const Layer*>(&connectableLayer)); |
| |
| auto const& backendRegistry = BackendRegistryInstance(); |
| if (!backendRegistry.IsBackendRegistered(backendId)) |
| { |
| std::stringstream ss; |
| ss << connectableLayer.GetName() << " is not supported on " << backendId |
| << " because this backend is not registered."; |
| |
| outReasonIfUnsupported = ss.str(); |
| return false; |
| } |
| |
| auto backendFactory = backendRegistry.GetFactory(backendId); |
| auto backendObject = backendFactory(); |
| auto layerSupportObject = backendObject->GetLayerSupport(); |
| |
| switch(layer.GetType()) |
| { |
| case LayerType::Activation: |
| { |
| auto cLayer = boost::polymorphic_downcast<const ActivationLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsActivationSupported( |
| OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Addition: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsAdditionSupported( |
| OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::BatchNormalization: |
| { |
| auto cLayer = boost::polymorphic_downcast<const BatchNormalizationLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| const TensorInfo& mean = cLayer->m_Mean->GetTensorInfo(); |
| const TensorInfo& var = cLayer->m_Variance->GetTensorInfo(); |
| const TensorInfo& beta = cLayer->m_Beta->GetTensorInfo(); |
| const TensorInfo& gamma = cLayer->m_Gamma->GetTensorInfo(); |
| result = layerSupportObject->IsBatchNormalizationSupported( |
| OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| OverrideDataType(mean, dataType), |
| OverrideDataType(var, dataType), |
| OverrideDataType(beta, dataType), |
| OverrideDataType(gamma, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::BatchToSpaceNd: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| auto cLayer = boost::polymorphic_downcast<const BatchToSpaceNdLayer*>(&layer); |
| |
| result = layerSupportObject->IsBatchToSpaceNdSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Constant: |
| { |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsConstantSupported(OverrideDataType(output, dataType), reason); |
| break; |
| } |
| case LayerType::ConvertFp16ToFp32: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsConvertFp16ToFp32Supported(input, output, reason); |
| break; |
| } |
| case LayerType::ConvertFp32ToFp16: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsConvertFp32ToFp16Supported(input, output, reason); |
| break; |
| } |
| case LayerType::Convolution2d: |
| { |
| auto cLayer = boost::polymorphic_downcast<const Convolution2dLayer*>(&layer); |
| |
| const TensorInfo input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(), |
| dataType); |
| const TensorInfo output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType); |
| BOOST_ASSERT(cLayer->m_Weight.get() != nullptr); |
| |
| const Convolution2dDescriptor& descriptor = cLayer->GetParameters(); |
| |
| // Construct optional biases object based on the value of m_BiasEnabled |
| Optional<TensorInfo> biases; |
| if (descriptor.m_BiasEnabled) |
| { |
| biases = |
| OverrideDataType(cLayer->m_Bias->GetTensorInfo(), GetBiasTypeFromWeightsType(dataType)); |
| } |
| |
| result = layerSupportObject->IsConvolution2dSupported( |
| input, |
| output, |
| descriptor, |
| OverrideDataType(cLayer->m_Weight->GetTensorInfo(), dataType), |
| biases, |
| reason); |
| break; |
| } |
| case LayerType::Debug: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsDebugSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::DepthwiseConvolution2d: |
| { |
| auto cLayer = boost::polymorphic_downcast<const DepthwiseConvolution2dLayer*>(&layer); |
| const TensorInfo& input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(), |
| dataType); |
| const TensorInfo& output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType); |
| BOOST_ASSERT(cLayer->m_Weight.get() != nullptr); |
| |
| const DepthwiseConvolution2dDescriptor& descriptor = cLayer->GetParameters(); |
| |
| // Construct optional biases object based on the value of m_BiasEnabled |
| Optional<TensorInfo> biases; |
| if (descriptor.m_BiasEnabled) |
| { |
| biases = |
| OverrideDataType(cLayer->m_Bias->GetTensorInfo(), GetBiasTypeFromWeightsType(dataType)); |
| } |
| |
| result = layerSupportObject->IsDepthwiseConvolutionSupported( |
| input, |
| output, |
| descriptor, |
| OverrideDataType(cLayer->m_Weight->GetTensorInfo(), dataType), |
| biases, |
| reason); |
| break; |
| } |
| case LayerType::Dequantize: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsDequantizeSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, DataType::Float32), |
| reason); |
| break; |
| } |
| case LayerType::DetectionPostProcess: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| auto cLayer = boost::polymorphic_downcast<const DetectionPostProcessLayer*>(&layer); |
| const DetectionPostProcessDescriptor& descriptor = cLayer->GetParameters(); |
| result = layerSupportObject->IsDetectionPostProcessSupported(input0, |
| input1, |
| descriptor, |
| reason); |
| break; |
| } |
| case LayerType::Equal: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsEqualSupported(OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::FakeQuantization: |
| { |
| auto cLayer = boost::polymorphic_downcast<const FakeQuantizationLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| result = layerSupportObject->IsFakeQuantizationSupported(OverrideDataType(input, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Floor: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsFloorSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::FullyConnected: |
| { |
| auto cLayer = boost::polymorphic_downcast<const FullyConnectedLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| BOOST_ASSERT(cLayer->m_Weight.get() != nullptr); |
| |
| TensorInfo biasInfo; |
| const TensorInfo * biasInfoPtr = nullptr; |
| static const TensorInfo dummyFloat16Bias(TensorShape({1,1,1,1}), DataType::Float16); |
| static const TensorInfo dummyFloat32Bias(TensorShape({1,1,1,1}), DataType::Float32); |
| static const TensorInfo dummyQA8Bias(TensorShape({1,1,1,1}), DataType::Signed32); |
| |
| const FullyConnectedDescriptor& descriptor = cLayer->GetParameters(); |
| if (descriptor.m_BiasEnabled) |
| { |
| BOOST_ASSERT(cLayer->m_Bias.get() != nullptr); |
| biasInfo = OverrideDataType(cLayer->m_Bias->GetTensorInfo(), GetBiasTypeFromWeightsType(dataType)); |
| biasInfoPtr = &biasInfo; |
| } |
| else |
| { |
| // If biases are not enabled pass a dummy tensorinfo for the validation |
| switch(input.GetDataType()) |
| { |
| case DataType::Float16: |
| { |
| biasInfoPtr = &dummyFloat16Bias; |
| break; |
| } |
| case DataType::Float32: |
| { |
| biasInfoPtr = &dummyFloat32Bias; |
| break; |
| } |
| case DataType::QuantisedAsymm8: |
| case DataType::QuantisedSymm16: |
| { |
| biasInfoPtr = &dummyQA8Bias; |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "Unexpected bias type"); |
| } |
| } |
| } |
| |
| result = layerSupportObject->IsFullyConnectedSupported( |
| OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| OverrideDataType(cLayer->m_Weight->GetTensorInfo(), dataType), |
| *biasInfoPtr, |
| descriptor, |
| reason); |
| break; |
| } |
| case LayerType::Gather: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsGatherSupported(OverrideDataType(input0, dataType), |
| input1, |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Input: |
| { |
| const TensorInfo& input = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsInputSupported(OverrideDataType(input, dataType), reason); |
| break; |
| } |
| case LayerType::L2Normalization: |
| { |
| auto cLayer = boost::polymorphic_downcast<const L2NormalizationLayer*>(&layer); |
| const L2NormalizationDescriptor& descriptor = cLayer->GetParameters(); |
| |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsL2NormalizationSupported( |
| OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| descriptor, |
| reason); |
| break; |
| } |
| case LayerType::Lstm: |
| { |
| auto cLayer = boost::polymorphic_downcast<const LstmLayer*>(&layer); |
| const LstmDescriptor& descriptor = cLayer->GetParameters(); |
| |
| // All inputs. |
| const TensorInfo& input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(), |
| dataType); |
| const TensorInfo& outputStateIn = OverrideDataType(layer.GetInputSlot(1).GetConnection()->GetTensorInfo(), |
| dataType); |
| const TensorInfo& cellStateIn = OverrideDataType(layer.GetInputSlot(2).GetConnection()->GetTensorInfo(), |
| dataType); |
| // All outputs |
| const TensorInfo& scratchBuffer = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType); |
| const TensorInfo& outputStateOut = OverrideDataType(layer.GetOutputSlot(1).GetTensorInfo(), dataType); |
| const TensorInfo& cellStateOut = OverrideDataType(layer.GetOutputSlot(2).GetTensorInfo(), dataType); |
| const TensorInfo& output = OverrideDataType(layer.GetOutputSlot(3).GetTensorInfo(), dataType); |
| |
| // Basic parameters |
| const TensorInfo& inputToForgetWeights |
| = OverrideDataType(cLayer->m_BasicParameters.m_InputToForgetWeights->GetTensorInfo(), dataType); |
| const TensorInfo& inputToCellWeights |
| = OverrideDataType(cLayer->m_BasicParameters.m_InputToCellWeights->GetTensorInfo(), dataType); |
| const TensorInfo& inputToOutputWeights |
| = OverrideDataType(cLayer->m_BasicParameters.m_InputToOutputWeights->GetTensorInfo(), dataType); |
| const TensorInfo& recurrentToForgetWeights |
| = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToForgetWeights->GetTensorInfo(), dataType); |
| const TensorInfo& recurrentToCellWeights |
| = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToCellWeights->GetTensorInfo(), dataType); |
| const TensorInfo& recurrentToOutputWeights |
| = OverrideDataType(cLayer->m_BasicParameters.m_RecurrentToOutputWeights->GetTensorInfo(), dataType); |
| const TensorInfo& forgetGateBias |
| = OverrideDataType(cLayer->m_BasicParameters.m_ForgetGateBias->GetTensorInfo(), dataType); |
| const TensorInfo& cellBias |
| = OverrideDataType(cLayer->m_BasicParameters.m_CellBias->GetTensorInfo(), dataType); |
| const TensorInfo& outputGateBias |
| = OverrideDataType(cLayer->m_BasicParameters.m_OutputGateBias->GetTensorInfo(), dataType); |
| |
| LstmInputParamsInfo paramsInfo; |
| |
| paramsInfo.m_InputToForgetWeights = &inputToForgetWeights; |
| paramsInfo.m_InputToCellWeights = &inputToCellWeights; |
| paramsInfo.m_InputToOutputWeights = &inputToOutputWeights; |
| paramsInfo.m_RecurrentToForgetWeights = &recurrentToForgetWeights; |
| paramsInfo.m_RecurrentToCellWeights = &recurrentToCellWeights; |
| paramsInfo.m_RecurrentToOutputWeights = &recurrentToOutputWeights; |
| paramsInfo.m_ForgetGateBias = &forgetGateBias; |
| paramsInfo.m_CellBias = &cellBias; |
| paramsInfo.m_OutputGateBias = &outputGateBias; |
| |
| |
| // Optional parameters |
| TensorInfo optInputToInputWeights; |
| TensorInfo optRecurrentToInputWeights; |
| TensorInfo optCellToInputWeights; |
| TensorInfo optInputGateBias; |
| TensorInfo optProjectionWeights; |
| TensorInfo optProjectionBias; |
| TensorInfo optCellToForgetWeights; |
| TensorInfo optCellToOutputWeights; |
| TensorInfo optInputLayerNormWeights; |
| TensorInfo optForgetLayerNormWeights; |
| TensorInfo optCellLayerNormWeights; |
| TensorInfo optOutputLayerNormWeights; |
| |
| if(!descriptor.m_CifgEnabled) |
| { |
| optInputToInputWeights = |
| OverrideDataType(cLayer->m_CifgParameters.m_InputToInputWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_InputToInputWeights = &optInputToInputWeights; |
| |
| optRecurrentToInputWeights = |
| OverrideDataType(cLayer->m_CifgParameters.m_RecurrentToInputWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_RecurrentToInputWeights = &optRecurrentToInputWeights; |
| if (cLayer->m_CifgParameters.m_CellToInputWeights != nullptr) |
| { |
| optCellToInputWeights = |
| OverrideDataType(cLayer->m_CifgParameters.m_CellToInputWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_CellToInputWeights = &optCellToInputWeights; |
| } |
| optInputGateBias = |
| OverrideDataType(cLayer->m_CifgParameters.m_InputGateBias->GetTensorInfo(), dataType); |
| paramsInfo.m_InputGateBias = &optInputGateBias; |
| } |
| |
| if(descriptor.m_ProjectionEnabled) |
| { |
| optProjectionWeights = |
| OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_ProjectionWeights = &optProjectionWeights; |
| if (cLayer->m_ProjectionParameters.m_ProjectionBias != nullptr) |
| { |
| optProjectionBias = |
| OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(), dataType); |
| paramsInfo.m_ProjectionBias = &optProjectionBias; |
| } |
| } |
| |
| if(descriptor.m_PeepholeEnabled) |
| { |
| optCellToForgetWeights = |
| OverrideDataType(cLayer->m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_CellToForgetWeights = &optCellToForgetWeights; |
| optCellToOutputWeights = |
| OverrideDataType(cLayer->m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_CellToOutputWeights = &optCellToOutputWeights; |
| } |
| |
| if(descriptor.m_LayerNormEnabled) |
| { |
| optInputLayerNormWeights = OverrideDataType( |
| cLayer->m_LayerNormParameters.m_InputLayerNormWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_InputLayerNormWeights = &optInputLayerNormWeights; |
| |
| optForgetLayerNormWeights = OverrideDataType( |
| cLayer->m_LayerNormParameters.m_ForgetLayerNormWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_ForgetLayerNormWeights = &optForgetLayerNormWeights; |
| |
| optCellLayerNormWeights = OverrideDataType( |
| cLayer->m_LayerNormParameters.m_CellLayerNormWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_CellLayerNormWeights = &optCellLayerNormWeights; |
| |
| optOutputLayerNormWeights = OverrideDataType( |
| cLayer->m_LayerNormParameters.m_OutputLayerNormWeights->GetTensorInfo(), dataType); |
| paramsInfo.m_OutputLayerNormWeights = &optOutputLayerNormWeights; |
| } |
| |
| result = layerSupportObject->IsLstmSupported( |
| input, |
| outputStateIn, |
| cellStateIn, |
| scratchBuffer, |
| outputStateOut, |
| cellStateOut, |
| output, |
| descriptor, |
| paramsInfo, |
| reason); |
| break; |
| } |
| case LayerType::Maximum: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsMaximumSupported(OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::MemCopy: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsMemCopySupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Merge: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsMergeSupported(OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Concat: |
| { |
| auto cLayer = boost::polymorphic_downcast<const ConcatLayer*>(&layer); |
| |
| // Get vector of all inputs. |
| auto getTensorInfo = [&dataType](const InputSlot& slot) |
| { |
| return OverrideDataType(slot.GetConnectedOutputSlot()->GetTensorInfo(), dataType); |
| }; |
| auto beginI = boost::make_transform_iterator(layer.GetInputSlots().begin(), getTensorInfo); |
| auto endI = boost::make_transform_iterator(layer.GetInputSlots().end(), getTensorInfo); |
| std::vector<TensorInfo> inputs(beginI, endI); |
| |
| auto getTensorInfoPtr = [](const TensorInfo& info) |
| { |
| return &info; |
| }; |
| auto beginPtr = boost::make_transform_iterator(inputs.begin(), getTensorInfoPtr); |
| auto endPtr = boost::make_transform_iterator(inputs.end(), getTensorInfoPtr); |
| std::vector<const TensorInfo*> inputPtrs(beginPtr, endPtr); |
| |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsConcatSupported(inputPtrs, output, cLayer->GetParameters(), reason); |
| |
| |
| break; |
| } |
| case LayerType::Multiplication: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsMultiplicationSupported( |
| OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Normalization: |
| { |
| auto cLayer = boost::polymorphic_downcast<const NormalizationLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsNormalizationSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Output: |
| { |
| const TensorInfo& output = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| result = layerSupportObject->IsOutputSupported(OverrideDataType(output, dataType), reason); |
| break; |
| } |
| case LayerType::Permute: |
| { |
| auto cLayer = boost::polymorphic_downcast<const PermuteLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsPermuteSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Pad: |
| { |
| auto cLayer = boost::polymorphic_downcast<const PadLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsPadSupported( |
| OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Pooling2d: |
| { |
| auto cLayer = boost::polymorphic_downcast<const Pooling2dLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsPooling2dSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::PreCompiled: |
| { |
| auto cLayer = boost::polymorphic_downcast<const PreCompiledLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| result = layerSupportObject->IsPreCompiledSupported(OverrideDataType(input, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Quantize: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsQuantizeSupported(input, output, reason); |
| break; |
| } |
| case LayerType::Division: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsDivisionSupported( |
| OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Reshape: |
| { |
| auto cLayer = boost::polymorphic_downcast<const ReshapeLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| result = layerSupportObject->IsReshapeSupported(OverrideDataType(input, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Resize: |
| { |
| auto cLayer = boost::polymorphic_downcast<const ResizeLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsResizeSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Rsqrt: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsRsqrtSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Softmax: |
| { |
| auto cLayer = boost::polymorphic_downcast<const SoftmaxLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsSoftmaxSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::SpaceToBatchNd: |
| { |
| auto cLayer = boost::polymorphic_downcast<const SpaceToBatchNdLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsSpaceToBatchNdSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::SpaceToDepth: |
| { |
| auto cLayer = boost::polymorphic_downcast<const SpaceToDepthLayer*>(&layer); |
| |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| |
| result = layerSupportObject->IsSpaceToDepthSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Splitter: |
| { |
| auto cLayer = boost::polymorphic_downcast<const SplitterLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| |
| // Get vector of all outputs. |
| auto getTensorInfo = [&dataType](const OutputSlot& slot) |
| { |
| return OverrideDataType(slot.GetTensorInfo(), dataType); |
| }; |
| auto beginI = boost::make_transform_iterator(layer.GetOutputSlots().begin(), getTensorInfo); |
| auto endI = boost::make_transform_iterator(layer.GetOutputSlots().end(), getTensorInfo); |
| std::vector<TensorInfo> outputs(beginI, endI); |
| |
| const std::vector<std::reference_wrapper<TensorInfo>> outputPtrs(outputs.begin(), outputs.end()); |
| |
| result = layerSupportObject->IsSplitterSupported(OverrideDataType(input, dataType), |
| outputPtrs, |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::StridedSlice: |
| { |
| auto cLayer = boost::polymorphic_downcast<const StridedSliceLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsStridedSliceSupported(OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Subtraction: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsSubtractionSupported( |
| OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Switch: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output0 = layer.GetOutputSlot(0).GetTensorInfo(); |
| const TensorInfo& output1 = layer.GetOutputSlot(1).GetTensorInfo(); |
| result = layerSupportObject->IsSwitchSupported(OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output0, dataType), |
| OverrideDataType(output1, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Mean: |
| { |
| auto cLayer = boost::polymorphic_downcast<const MeanLayer*>(&layer); |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsMeanSupported( |
| OverrideDataType(input, dataType), |
| OverrideDataType(output, dataType), |
| cLayer->GetParameters(), |
| reason); |
| break; |
| } |
| case LayerType::Minimum: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsMinimumSupported(OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::Greater: |
| { |
| const TensorInfo& input0 = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& input1 = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsGreaterSupported(OverrideDataType(input0, dataType), |
| OverrideDataType(input1, dataType), |
| OverrideDataType(output, DataType::Boolean), |
| reason); |
| break; |
| } |
| case LayerType::Prelu: |
| { |
| const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); |
| const TensorInfo& alpha = layer.GetInputSlot(1).GetConnection()->GetTensorInfo(); |
| const TensorInfo& output = layer.GetOutputSlot(0).GetTensorInfo(); |
| result = layerSupportObject->IsPreluSupported(OverrideDataType(input, dataType), |
| OverrideDataType(alpha, dataType), |
| OverrideDataType(output, dataType), |
| reason); |
| break; |
| } |
| case LayerType::TransposeConvolution2d: |
| { |
| auto cLayer = boost::polymorphic_downcast<const TransposeConvolution2dLayer*>(&layer); |
| |
| const TensorInfo input = OverrideDataType(layer.GetInputSlot(0).GetConnection()->GetTensorInfo(), |
| dataType); |
| const TensorInfo output = OverrideDataType(layer.GetOutputSlot(0).GetTensorInfo(), dataType); |
| |
| const TransposeConvolution2dDescriptor& descriptor = cLayer->GetParameters(); |
| |
| Optional<TensorInfo> biases; |
| if (descriptor.m_BiasEnabled) |
| { |
| BOOST_ASSERT(cLayer->m_Bias.get() != nullptr); |
| biases = OverrideDataType(cLayer->m_Bias->GetTensorInfo(), |
| GetBiasTypeFromWeightsType(dataType)); |
| } |
| |
| BOOST_ASSERT(cLayer->m_Weight.get() != nullptr); |
| const TensorInfo weights = OverrideDataType(cLayer->m_Weight->GetTensorInfo(), dataType); |
| |
| result = layerSupportObject->IsTransposeConvolution2dSupported(input, |
| output, |
| descriptor, |
| weights, |
| biases, |
| reason); |
| |
| break; |
| } |
| default: |
| { |
| BOOST_ASSERT_MSG(false, "WorkloadFactory did not recognise type of layer."); |
| reason.value() = "Unrecognised layer type"; |
| result = false; |
| break; |
| } |
| } |
| return result; |
| } |
| |
| bool IWorkloadFactory::IsLayerSupported(const IConnectableLayer& connectableLayer, |
| Optional<DataType> dataType, |
| std::string& outReasonIfUnsupported) |
| { |
| auto layer = boost::polymorphic_downcast<const Layer*>(&connectableLayer); |
| return IsLayerSupported(layer->GetBackendId(), connectableLayer, dataType, outReasonIfUnsupported); |
| } |
| |
| // Default Implementations |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateActivation(const ActivationQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateAddition(const AdditionQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateBatchNormalization( |
| const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateBatchToSpaceNd(const BatchToSpaceNdQueueDescriptor& descriptor, |
| const WorkloadInfo& Info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateConcat(const ConcatQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateConstant(const ConstantQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp16ToFp32(const ConvertFp16ToFp32QueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvertFp32ToFp16(const ConvertFp32ToFp16QueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateConvolution2d(const Convolution2dQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateDebug(const DebugQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateDepthwiseConvolution2d( |
| const DepthwiseConvolution2dQueueDescriptor& descriptor, const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateDequantize( |
| const DequantizeQueueDescriptor& descriptor, const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateDetectionPostProcess( |
| const DetectionPostProcessQueueDescriptor& descriptor, const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateDivision(const DivisionQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateEqual(const EqualQueueDescriptor& descriptor, |
| const WorkloadInfo& Info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateFakeQuantization(const FakeQuantizationQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateFloor(const FloorQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateFullyConnected(const FullyConnectedQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateGather(const GatherQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateGreater(const GreaterQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateL2Normalization(const L2NormalizationQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateLstm(const LstmQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMaximum(const MaximumQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMean(const MeanQueueDescriptor& descriptor, |
| const WorkloadInfo& Info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMemCopy(const MemCopyQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMerge(const MergeQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMerger(const MergerQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMinimum(const MinimumQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateMultiplication(const MultiplicationQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateNormalization(const NormalizationQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateOutput(const OutputQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreatePad(const PadQueueDescriptor& descriptor, |
| const WorkloadInfo& Info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreatePermute(const PermuteQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreatePooling2d(const Pooling2dQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreatePreCompiled(const PreCompiledQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreatePrelu(const PreluQueueDescriptor &descriptor, |
| const WorkloadInfo &info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateQuantize(const QuantizeQueueDescriptor& descriptor, |
| const WorkloadInfo& Info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateReshape(const ReshapeQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateResizeBilinear(const ResizeBilinearQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateResize(const ResizeQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateRsqrt(const RsqrtQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateSoftmax(const SoftmaxQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateSplitter(const SplitterQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateSpaceToBatchNd(const SpaceToBatchNdQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateSpaceToDepth(const SpaceToDepthQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateStridedSlice(const StridedSliceQueueDescriptor& descriptor, |
| const WorkloadInfo& Info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateSubtraction(const SubtractionQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateSwitch(const SwitchQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| std::unique_ptr<IWorkload> IWorkloadFactory::CreateTransposeConvolution2d( |
| const TransposeConvolution2dQueueDescriptor& descriptor, |
| const WorkloadInfo& info) const |
| { |
| return std::unique_ptr<IWorkload>(); |
| } |
| |
| } // namepsace armnn |
