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Diffstat (limited to 'src/backends/backendsCommon/WorkloadFactory.cpp')
| -rw-r--r-- | src/backends/backendsCommon/WorkloadFactory.cpp | 612 |
1 files changed, 612 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/WorkloadFactory.cpp b/src/backends/backendsCommon/WorkloadFactory.cpp new file mode 100644 index 0000000000..83a20e8675 --- /dev/null +++ b/src/backends/backendsCommon/WorkloadFactory.cpp @@ -0,0 +1,612 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "CpuTensorHandle.hpp" + +#include <Layer.hpp> +#include <LayersFwd.hpp> + +#include <armnn/Types.hpp> +#include <armnn/LayerSupport.hpp> + +#include <backendsCommon/LayerSupportRegistry.hpp> +#include <backendsCommon/WorkloadFactory.hpp> + +#include <boost/cast.hpp> +#include <boost/iterator/transform_iterator.hpp> + +#include <cstring> + +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()); +} + +Optional<DataType> GetBiasTypeFromWeightsType(Optional<DataType> weightsType) +{ + if (!weightsType) + { + return weightsType; + } + + switch(weightsType.value()) + { + case DataType::Float16: + case DataType::Float32: + return weightsType; + case DataType::QuantisedAsymm8: + return DataType::Signed32; + default: + BOOST_ASSERT_MSG(false, "GetBiasTypeFromWeightsType(): Unsupported data type."); + } + return EmptyOptional(); +} + +} // 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& layerSupportRegistry = LayerSupportRegistryInstance(); + auto layerSupportFactory = layerSupportRegistry.GetFactory(backendId); + auto layerSupportObject = layerSupportFactory(EmptyInitializer()); + + 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::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::MemCopy: + { + // MemCopy supported for CpuRef, CpuAcc and GpuAcc backends, + // (also treat Undefined as CpuRef to avoid breaking lots of Unit tests). + result = backendId == Compute::CpuRef || backendId == Compute::Undefined + || backendId == Compute::CpuAcc || backendId == Compute::GpuAcc; + reason.value() = "Unsupported backend type"; + 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::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: + { + 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::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); + + // Optional parameters + const TensorInfo* inputToInputWeights = nullptr; + const TensorInfo* recurrentToInputWeights = nullptr; + const TensorInfo* cellToInputWeights = nullptr; + const TensorInfo* inputGateBias = nullptr; + const TensorInfo* projectionWeights = nullptr; + const TensorInfo* projectionBias = nullptr; + const TensorInfo* cellToForgetWeights = nullptr; + const TensorInfo* cellToOutputWeights = nullptr; + + TensorInfo optInputToInputWeights; + TensorInfo optRecurrentToInputWeights; + TensorInfo optCellToInputWeights; + TensorInfo optInputGateBias; + TensorInfo optProjectionWeights; + TensorInfo optProjectionBias; + TensorInfo optCellToForgetWeights; + TensorInfo optCellToOutputWeights; + + if(!descriptor.m_CifgEnabled) + { + optInputToInputWeights = + OverrideDataType(cLayer->m_CifgParameters.m_InputToInputWeights->GetTensorInfo(), dataType); + inputToInputWeights = &optInputToInputWeights; + + optRecurrentToInputWeights = + OverrideDataType(cLayer->m_CifgParameters.m_RecurrentToInputWeights->GetTensorInfo(), dataType); + recurrentToInputWeights = &optRecurrentToInputWeights; + if (cLayer->m_CifgParameters.m_CellToInputWeights != nullptr) + { + optCellToInputWeights = + OverrideDataType(cLayer->m_CifgParameters.m_CellToInputWeights->GetTensorInfo(), dataType); + cellToInputWeights = &optCellToInputWeights; + } + optInputGateBias = + OverrideDataType(cLayer->m_CifgParameters.m_InputGateBias->GetTensorInfo(), dataType); + inputGateBias = &optInputGateBias; + } + + if(descriptor.m_ProjectionEnabled) + { + optProjectionWeights = + OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionWeights->GetTensorInfo(), dataType); + projectionWeights = &optProjectionWeights; + if (cLayer->m_ProjectionParameters.m_ProjectionBias != nullptr) + { + optProjectionBias = + OverrideDataType(cLayer->m_ProjectionParameters.m_ProjectionBias->GetTensorInfo(), dataType); + projectionBias = &optProjectionBias; + } + } + + if(descriptor.m_PeepholeEnabled) + { + optCellToForgetWeights = + OverrideDataType(cLayer->m_PeepholeParameters.m_CellToForgetWeights->GetTensorInfo(), dataType); + cellToForgetWeights = &optCellToForgetWeights; + optCellToOutputWeights = + OverrideDataType(cLayer->m_PeepholeParameters.m_CellToOutputWeights->GetTensorInfo(), dataType); + cellToOutputWeights = &optCellToOutputWeights; + } + + result = layerSupportObject->IsLstmSupported( + input, + outputStateIn, + cellStateIn, + scratchBuffer, + outputStateOut, + cellStateOut, + output, + descriptor, + inputToForgetWeights, + inputToCellWeights, + inputToOutputWeights, + recurrentToForgetWeights, + recurrentToCellWeights, + recurrentToOutputWeights, + forgetGateBias, + cellBias, + outputGateBias, + inputToInputWeights, + recurrentToInputWeights, + cellToInputWeights, + inputGateBias, + projectionWeights, + projectionBias, + cellToForgetWeights, + cellToOutputWeights, + reason); + break; + } + case LayerType::Merger: + { + auto cLayer = boost::polymorphic_downcast<const MergerLayer*>(&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); + + result = layerSupportObject->IsMergerSupported(inputPtrs, 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::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: + { + const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); + result = layerSupportObject->IsReshapeSupported(OverrideDataType(input, dataType), reason); + break; + } + case LayerType::ResizeBilinear: + { + const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); + result = layerSupportObject->IsResizeBilinearSupported(OverrideDataType(input, 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::Splitter: + { + auto cLayer = boost::polymorphic_downcast<const SplitterLayer*>(&layer); + const TensorInfo& input = layer.GetInputSlot(0).GetConnection()->GetTensorInfo(); + result = layerSupportObject->IsSplitterSupported(OverrideDataType(input, 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::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; + } + 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); +} + +} |