TosaRefLayerSupport Class Reference

#include <TosaRefLayerSupport.hpp>

Inheritance diagram for TosaRefLayerSupport:

Collaboration diagram for TosaRefLayerSupport:

Public Member Functions
bool	IsLayerSupported (const LayerType &type, const std::vector< TensorInfo > &infos, const BaseDescriptor &descriptor, const Optional< LstmInputParamsInfo > &lstmParamsInfo, const Optional< QuantizedLstmInputParamsInfo > &, Optional< std::string & > reasonIfUnsupported) const override
	Default implementation of the ILayerSupport interface, Backends should implement this as a switch statement for each of their LayerTypes calling their specific backend implementation of IsXXXLayerSupported. More...

Additional Inherited Members
Protected Member Functions inherited from ILayerSupport
	ILayerSupport ()
virtual	~ILayerSupport ()

Detailed Description

Definition at line 12 of file TosaRefLayerSupport.hpp.

Member Function Documentation

IsLayerSupported()

bool IsLayerSupported ( const LayerType &  type, const std::vector< TensorInfo > &  infos, const BaseDescriptor &  descriptor, const Optional< LstmInputParamsInfo > &  lstmParamsInfo, const Optional< QuantizedLstmInputParamsInfo > &  quantizedLstmParamsInfo, Optional< std::string & >  reasonIfUnsupported  ) const

overridevirtual

Default implementation of the ILayerSupport interface, Backends should implement this as a switch statement for each of their LayerTypes calling their specific backend implementation of IsXXXLayerSupported.

Reimplemented from ILayerSupport.

Definition at line 21 of file TosaRefLayerSupport.cpp.

{
    IgnoreUnused(lstmParamsInfo);
    IgnoreUnused(quantizedLstmInputParamsInfo);
    IgnoreUnused(reasonIfUnsupported);
 
    std::vector<const TensorInfo*> inputInfos;
    std::vector<const TensorInfo*> outputInfos;
 
    switch (type)
    {
        case LayerType::Activation:
            inputInfos.push_back(&infos[0]);
            outputInfos.push_back(&infos[1]);
            break;
        case LayerType::Input:
        case LayerType::Output:
            return true;
        case LayerType::Addition:
        case LayerType::BatchMatMul:
        case LayerType::Multiplication:
        case LayerType::Subtraction:
        case LayerType::ElementwiseBinary:
            // Setup inputs and outputs
            inputInfos.push_back(&infos[0]);
            inputInfos.push_back(&infos[1]);
            outputInfos.push_back(&infos[2]);
            break;
        case LayerType::Concat:
            for (unsigned int i = 0; i < infos.size() - 1; ++i)
            {
                inputInfos.push_back(&infos[i]);
            }
            outputInfos.push_back(&infos.back());
            break;
        case LayerType::Constant:
            outputInfos.push_back(&infos[0]);
            break;
        case LayerType::Convolution2d:
        {
            inputInfos.push_back(&infos[0]); // input
            outputInfos.push_back(&infos[1]); // output
            inputInfos.push_back(&infos[2]); // weights
 
            auto conv2dDesc = PolymorphicDowncast<const Convolution2dDescriptor*>(&descriptor);
            if(conv2dDesc->m_BiasEnabled)
            {
                inputInfos.push_back(&infos[3]); // bias
            }
            break;
        }
        case LayerType::DepthwiseConvolution2d:
        {
            inputInfos.push_back(&infos[0]); // input
            outputInfos.push_back(&infos[1]); // output
            inputInfos.push_back(&infos[2]); // weights
 
            auto conv2dDesc = PolymorphicDowncast<const DepthwiseConvolution2dDescriptor*>(&descriptor);
            if(conv2dDesc->m_BiasEnabled)
            {
                inputInfos.push_back(&infos[3]); // bias
            }
            break;
        }
        case LayerType::FullyConnected:
        {
            inputInfos.push_back(&infos[0]); // input
            outputInfos.push_back(&infos[1]); // output
            inputInfos.push_back(&infos[2]); // weights
            auto fullyConnectedDesc = PolymorphicDowncast<const FullyConnectedDescriptor*>(&descriptor);
            if(fullyConnectedDesc->m_BiasEnabled)
            {
                inputInfos.push_back(&infos[3]); // bias
            }
            break;
        }
        case LayerType::ElementwiseUnary:
        case LayerType::Pad:
        case LayerType::Pooling2d:
        case LayerType::Mean:
        case LayerType::Quantize:
        case LayerType::Reduce:
        case LayerType::Reshape:
        case LayerType::Resize:
        case LayerType::Slice:
        case LayerType::Softmax:
        case LayerType::Transpose:
        {
            inputInfos.push_back(&infos[0]);
            outputInfos.push_back(&infos[1]);
            break;
        }
        case LayerType::Splitter:
        {
            inputInfos.push_back(&infos[0]);
            for (unsigned int i = 1; i < infos.size(); ++i)
            {
                outputInfos.push_back(&infos[i]);
            }
            break;
        }
        case LayerType::TransposeConvolution2d:
        {
            inputInfos.push_back(&infos[0]); // input
            outputInfos.push_back(&infos[1]); // output
            inputInfos.push_back(&infos[2]); // weights
 
            auto conv2dDesc = PolymorphicDowncast<const TransposeConvolution2dDescriptor*>(&descriptor);
            if(conv2dDesc->m_BiasEnabled)
            {
                inputInfos.push_back(&infos[3]); // bias
            }
            break;
        }
        default:
            // Default to false for all unsupported layers.
            return false;
    }
 
    auto mappings = GetTosaMapping(nullptr, type, inputInfos, outputInfos, descriptor);
    if (mappings->GetName() == "")
    {
        // There currently isn't a TOSA mapping for this layer, as the default was returned.
        return false;
    }
 
    TosaSerializationHandler handler;
 
    // Add all mappings to main block.
    auto* block = new TosaSerializationBasicBlock("main",
                                                  "main",
                                                  mappings->GetOperators(),
                                                  mappings->GetTensors(),
                                                  mappings->GetInputs(),
                                                  mappings->GetOutputs());
 
    std::vector<TosaSerializationBasicBlock*> blocks;
    blocks.emplace_back(block);
 
    // Add blocks to the main region.
    auto* region = new TosaSerializationRegion("main", blocks);
    handler.GetRegions().emplace_back(region);
 
    GraphStatus status;
    TosaReference::IModelRunner runner;
 
#if !defined(TOSA_REFERENCE_MODEL_OUTPUT)
    // There currently isn't a way to disable the output from the TOSA Reference Model, but it does have a file pointer
    // to write debug output to, so set this to /dev/null (if it exists on the system) to hide the output.
    func_debug_t funcDebug;
 
    FILE* file = fopen("/dev/null", "w");
    funcDebug.func_debug_file = (file == nullptr) ? stderr : file;
 
    runner.setFuncDebug(funcDebug);
#endif
 
    // Initialise the model runner with the TosaSerializationHandler, which runs validation on the mapping.
    status = runner.initialize(handler);
 
#if !defined(TOSA_REFERENCE_MODEL_OUTPUT)
    // Reset FuncDebug as they can persist across multiple IModelRunner instances.
    funcDebug.func_debug_file = stderr;
    runner.setFuncDebug(funcDebug);
#endif
 
    if(status == GraphStatus::TOSA_ERROR || status == GraphStatus::TOSA_UNPREDICTABLE)
    {
        return false;
    }
    else
    {
        return true;
    }
}

References armnn::Activation, armnn::Addition, armnn::BatchMatMul, armnn::Concat, armnn::Constant, armnn::Convolution2d, armnn::DepthwiseConvolution2d, armnn::ElementwiseBinary, armnn::ElementwiseUnary, armnn::FullyConnected, GetTosaMapping(), armnn::IgnoreUnused(), armnn::Input, armnn::Mean, armnn::Multiplication, armnn::Output, armnn::Pad, armnn::Pooling2d, armnn::Quantize, armnn::Reduce, armnn::Reshape, armnn::Resize, armnn::Slice, armnn::Softmax, armnn::Splitter, armnn::Subtraction, armnn::Transpose, and armnn::TransposeConvolution2d.

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The documentation for this class was generated from the following files:

• src/backends/tosaReference/TosaRefLayerSupport.hpp
• src/backends/tosaReference/TosaRefLayerSupport.cpp