| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "RefLayerSupport.hpp" |
| #include "RefBackendId.hpp" |
| |
| #include <DataLayoutIndexed.hpp> |
| #include <InternalTypes.hpp> |
| #include <LayerSupportCommon.hpp> |
| |
| #include <armnn/Types.hpp> |
| #include <armnn/Descriptors.hpp> |
| |
| #include <backendsCommon/BackendRegistry.hpp> |
| #include <backendsCommon/LayerSupportRules.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <boost/core/ignore_unused.hpp> |
| |
| #include <vector> |
| #include <algorithm> |
| #include <array> |
| |
| using namespace boost; |
| |
| namespace armnn |
| { |
| |
| namespace |
| { |
| |
| template<typename Float32Func, typename Uint8Func, typename ... Params> |
| bool IsSupportedForDataTypeRef(Optional<std::string&> reasonIfUnsupported, |
| DataType dataType, |
| Float32Func floatFuncPtr, |
| Uint8Func uint8FuncPtr, |
| Params&&... params) |
| { |
| return IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| dataType, |
| &FalseFunc<Params...>, |
| floatFuncPtr, |
| uint8FuncPtr, |
| &FalseFunc<Params...>, |
| &FalseFunc<Params...>, |
| std::forward<Params>(params)...); |
| } |
| |
| } // anonymous namespace |
| |
| namespace |
| { |
| |
| std::string CreateIncorrectDimensionsErrorMsg(unsigned int expected, |
| unsigned int actual, |
| std::string& layerStr, |
| std::string& tensorName) |
| { |
| std::string errorMsg = "Reference " + layerStr + ": Expected " + std::to_string(expected) + " dimensions but got" + |
| " " + std::to_string(actual) + " dimensions instead, for the '" + tensorName + "' tensor."; |
| |
| return errorMsg; |
| } |
| |
| } // anonymous namespace |
| |
| bool RefLayerSupport::IsAbsSupported(const TensorInfo& input, const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference abs: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference abs: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference abs: input and output types not matching"); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference abs: input and output shapes have different number of total elements"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsActivationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ActivationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported types. |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference activation: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference activation: output type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference activation: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(ShapesAreSameRank(input, output), reasonIfUnsupported, |
| "Reference activation: input and output shapes are of different rank."); |
| |
| |
| struct ActivationFunctionSupported : public Rule |
| { |
| ActivationFunctionSupported(const ActivationDescriptor& desc) |
| { |
| switch(desc.m_Function) |
| { |
| case ActivationFunction::Abs: |
| case ActivationFunction::BoundedReLu: |
| case ActivationFunction::LeakyReLu: |
| case ActivationFunction::Linear: |
| case ActivationFunction::ReLu: |
| case ActivationFunction::Sigmoid: |
| case ActivationFunction::SoftReLu: |
| case ActivationFunction::Sqrt: |
| case ActivationFunction::Square: |
| case ActivationFunction::TanH: |
| { |
| m_Res = true; |
| break; |
| } |
| default: |
| { |
| m_Res = false; |
| break; |
| } |
| } |
| } |
| }; |
| |
| // Function is supported |
| supported &= CheckSupportRule(ActivationFunctionSupported(descriptor), reasonIfUnsupported, |
| "Reference activation: function not supported."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsAdditionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference addition: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference addition: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference addition: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference addition: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference addition: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference addition: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsArgMinMaxSupported(const armnn::TensorInfo &input, const armnn::TensorInfo &output, |
| const armnn::ArgMinMaxDescriptor &descriptor, |
| armnn::Optional<std::string &> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| |
| std::array<DataType, 3> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| bool supported = true; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference ArgMinMax: input is not a supported type."); |
| supported &= CheckSupportRule(TypeIs(output, DataType::Signed32), reasonIfUnsupported, |
| "Reference ArgMinMax: output type not supported"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsBatchNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& mean, |
| const TensorInfo& variance, |
| const TensorInfo& beta, |
| const TensorInfo& gamma, |
| const BatchNormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| |
| std::array<DataType, 4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| bool supported = true; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference batch normalization: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference batch normalization: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference batch normalization: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(mean, supportedTypes), reasonIfUnsupported, |
| "Reference batch normalization: mean is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(variance, supportedTypes), reasonIfUnsupported, |
| "Reference batch normalization: variance is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(beta, supportedTypes), reasonIfUnsupported, |
| "Reference batch normalization: beta is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(gamma, supportedTypes), reasonIfUnsupported, |
| "Reference batch normalization: gamma is not a supported type."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsBatchToSpaceNdSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const BatchToSpaceNdDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| |
| bool supported = true; |
| |
| std::string batchToSpaceNdLayerStr = "batchToSpaceNd"; |
| std::string inputTensorStr = "input"; |
| std::string outputTensorStr = "output"; |
| |
| // Define supported types. |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference BatchToSpaceNd: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference BatchToSpaceNd: output type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference BatchToSpaceNd: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(TensorNumDimensionsAreCorrect(output, 4), |
| reasonIfUnsupported, |
| CreateIncorrectDimensionsErrorMsg(4, |
| output.GetNumDimensions(), |
| batchToSpaceNdLayerStr, |
| outputTensorStr).data()); |
| |
| supported &= CheckSupportRule(TensorNumDimensionsAreCorrect(input, 4), |
| reasonIfUnsupported, |
| CreateIncorrectDimensionsErrorMsg(4, |
| input.GetNumDimensions(), |
| batchToSpaceNdLayerStr, |
| inputTensorStr).data()); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsConcatSupported(const std::vector<const TensorInfo*> inputs, |
| const TensorInfo& output, |
| const ConcatDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference concatenation: output type not supported"); |
| for (const TensorInfo* input : inputs) |
| { |
| BOOST_ASSERT(input != nullptr); |
| supported &= CheckSupportRule(TypeAnyOf(*input, supportedTypes), reasonIfUnsupported, |
| "Reference concatenation: input type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(*input, output), reasonIfUnsupported, |
| "Reference concatenation: input and output types mismatched."); |
| } |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsConstantSupported(const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Signed32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| return CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference constant: output is not a supported type."); |
| } |
| |
| bool RefLayerSupport::IsConvertFp16ToFp32Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return (IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| input.GetDataType(), |
| &TrueFunc<>, |
| &FalseInputFuncF32<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>) && |
| IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| output.GetDataType(), |
| &FalseOutputFuncF16<>, |
| &TrueFunc<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>)); |
| } |
| |
| bool RefLayerSupport::IsConvertFp32ToFp16Supported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return (IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| input.GetDataType(), |
| &FalseInputFuncF16<>, |
| &TrueFunc<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>) && |
| IsSupportedForDataTypeGeneric(reasonIfUnsupported, |
| output.GetDataType(), |
| &TrueFunc<>, |
| &FalseOutputFuncF32<>, |
| &FalseFuncU8<>, |
| &FalseFuncI32<>, |
| &FalseFuncU8<>)); |
| } |
| |
| bool RefLayerSupport::IsConvolution2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Convolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported types. |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference convolution2d: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference convolution2d: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, |
| "Reference convolution2d: weights is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference convolution2d: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, weights), reasonIfUnsupported, |
| "Reference convolution2d: input and weights types mismatched."); |
| |
| if (biases.has_value()) |
| { |
| std::array<DataType,3> biasesSupportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::Signed32 |
| }; |
| supported &= CheckSupportRule(TypeAnyOf(biases.value(), biasesSupportedTypes), reasonIfUnsupported, |
| "Reference convolution2d: biases is not a supported type."); |
| } |
| ignore_unused(descriptor); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsDebugSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference debug: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference debug: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference debug: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsDepthToSpaceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthToSpaceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference DepthToSpace: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference DepthToSpace: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference DepthToSpace: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsDepthwiseConvolutionSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthwiseConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported types. |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference DepthwiseConvolution2d: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference DepthwiseConvolution2d: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, |
| "Reference DepthwiseConvolution2d: weights is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference DepthwiseConvolution2d: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, weights), reasonIfUnsupported, |
| "Reference DepthwiseConvolution2d: input and weights types mismatched."); |
| |
| if (biases.has_value()) |
| { |
| std::array<DataType,3> biasesSupportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::Signed32 |
| }; |
| supported &= CheckSupportRule(TypeAnyOf(biases.value(), biasesSupportedTypes), reasonIfUnsupported, |
| "Reference DepthwiseConvolution2d: biases is not a supported type."); |
| } |
| ignore_unused(descriptor); |
| |
| return supported; |
| |
| } |
| |
| bool RefLayerSupport::IsDequantizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,2> supportedInputTypes = { |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedInputTypes), reasonIfUnsupported, |
| "Reference dequantize: input type not supported."); |
| |
| std::array<DataType,1> supportedOutputTypes = { |
| DataType::Float32 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedOutputTypes), reasonIfUnsupported, |
| "Reference dequantize: output type not supported."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference dequantize: input and output shapes have different num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsDetectionPostProcessSupported(const armnn::TensorInfo& input0, |
| const armnn::TensorInfo& input1, |
| const armnn::DetectionPostProcessDescriptor& descriptor, |
| armnn::Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,3> supportedInputTypes = |
| { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedInputTypes), reasonIfUnsupported, |
| "Reference DetectionPostProcess: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedInputTypes), reasonIfUnsupported, |
| "Reference DetectionPostProcess: input 1 is not a supported type."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsDilatedDepthwiseConvolutionSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const DepthwiseConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsDepthwiseConvolutionSupported(input, output, descriptor, weights, biases, reasonIfUnsupported); |
| } |
| |
| bool RefLayerSupport::IsDivisionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference division: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference division: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference division: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference division: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference division: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference division: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsEqualSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference equal: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference equal: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference equal: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference equal: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsFakeQuantizationSupported(const TensorInfo& input, |
| const FakeQuantizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| std::array<DataType,1> supportedTypes = |
| { |
| DataType::Float32 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference fake quantization: input type not supported."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsFloorSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Floor: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference Floor: output type not supported."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsFullyConnectedSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TensorInfo& weights, |
| const TensorInfo& biases, |
| const FullyConnectedDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported types. |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Fully Connected: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference Fully Connected: output type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Fully Connected: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, |
| "Reference Fully Connected: weights type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, weights), reasonIfUnsupported, |
| "Reference Fully Connected: input and weight types mismatched."); |
| |
| if (descriptor.m_BiasEnabled) |
| { |
| // Defined supported types for bias |
| std::array<DataType, 3> |
| supportedBiasTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::Signed32 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(biases, supportedBiasTypes), reasonIfUnsupported, |
| "Reference Fully Connected: bias type not supported."); |
| |
| supported &= CheckSupportRule(BiasAndWeightsTypesMatch(biases, weights), reasonIfUnsupported, |
| "Reference Fully Connected: bias and weight types mismatch."); |
| |
| supported &= CheckSupportRule(BiasAndWeightsTypesCompatible(weights, supportedBiasTypes), reasonIfUnsupported, |
| "Reference Fully Connected: bias type inferred from weights is incompatible."); |
| |
| } |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsGatherSupported(const armnn::TensorInfo& input0, |
| const armnn::TensorInfo& input1, |
| const armnn::TensorInfo& output, |
| armnn::Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference Gather: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference Gather: output type not supported"); |
| |
| supported &= CheckSupportRule(TypeIs(input1, DataType::Signed32), reasonIfUnsupported, |
| "Reference Gather: indices (input1) type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference Gather: input and output types not matching"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsGreaterSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference greater: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference greater: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference greater: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference greater: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsInputSupported(const TensorInfo& input, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return true; |
| } |
| |
| bool RefLayerSupport::IsInstanceNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const InstanceNormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| // Define supported types |
| std::array<DataType, 4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16 |
| }; |
| |
| bool supported = true; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Instance Normalization: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference Instance Normalization: output type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Instance Normalization: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference Instance Normalization: input and output shapes have different " |
| "num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsL2NormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const L2NormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| // Define supported types |
| std::array<DataType, 4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| bool supported = true; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference L2normalization: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference L2normalization: output type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference L2normalization: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference L2normalization: input and output shapes have different " |
| "num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsLstmSupported(const TensorInfo& input, |
| const TensorInfo& outputStateIn, |
| const TensorInfo& cellStateIn, |
| const TensorInfo& scratchBuffer, |
| const TensorInfo& outputStateOut, |
| const TensorInfo& cellStateOut, |
| const TensorInfo& output, |
| const LstmDescriptor& descriptor, |
| const LstmInputParamsInfo& paramsInfo, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| ignore_unused(paramsInfo); |
| |
| bool supported = true; |
| |
| std::array<DataType,2> supportedTypes = { |
| DataType::Float32, |
| DataType::QuantisedSymm16 |
| }; |
| |
| // check inputs and outputs |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Lstm: input is not a supported type."); |
| supported &= CheckSupportRule(TypesAreEqual(input, outputStateIn), reasonIfUnsupported, |
| "Reference Lstm: input and outputStateIn types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, cellStateIn), reasonIfUnsupported, |
| "Reference Lstm: input and cellStateIn types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, scratchBuffer), reasonIfUnsupported, |
| "Reference Lstm: input and scratchBuffer types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, outputStateOut), reasonIfUnsupported, |
| "Reference Lstm: input and outputStateOut types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, cellStateOut), reasonIfUnsupported, |
| "Reference Lstm: input and cellStateOut types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Lstm: input and output types are mismatched"); |
| // check layer parameters |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputToForgetWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and InputToForgetWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputToCellWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and InputToCellWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputToOutputWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and InputToOutputWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetRecurrentToForgetWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and RecurrentToForgetWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetRecurrentToCellWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and RecurrentToCellWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetRecurrentToOutputWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and RecurrentToOutputWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetForgetGateBias()), reasonIfUnsupported, |
| "Reference Lstm: input and ForgetGateBias types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellBias()), reasonIfUnsupported, |
| "Reference Lstm: input and CellBias types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetOutputGateBias()), reasonIfUnsupported, |
| "Reference Lstm: input and OutputGateBias types are mismatched"); |
| if (!descriptor.m_CifgEnabled) |
| { |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputToInputWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and InputToInputWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetRecurrentToInputWeights()), |
| reasonIfUnsupported, |
| "Reference Lstm: input and RecurrentToInputWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputGateBias()), reasonIfUnsupported, |
| "Reference Lstm: input and InputGateBias types are mismatched"); |
| if (descriptor.m_PeepholeEnabled) |
| { |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellToInputWeights()), |
| reasonIfUnsupported, |
| "Reference Lstm: input and CellToInputWeights types are mismatched"); |
| } |
| } |
| if (descriptor.m_PeepholeEnabled) |
| { |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellToForgetWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and CellToForgetWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellToOutputWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and CellToOutputWeights types are mismatched"); |
| } |
| if (descriptor.m_ProjectionEnabled) |
| { |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetProjectionWeights()), reasonIfUnsupported, |
| "Reference Lstm: input and mProjectionWeights types are mismatched"); |
| if (paramsInfo.m_ProjectionBias != nullptr) |
| { |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetProjectionBias()), reasonIfUnsupported, |
| "Reference Lstm: input and ProjectionBias types are mismatched"); |
| } |
| } |
| if (descriptor.m_LayerNormEnabled) |
| { |
| if (!descriptor.m_CifgEnabled) |
| { |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetInputLayerNormWeights()), |
| reasonIfUnsupported, |
| "Reference Lstm: input and InputLayerNormWeights types are mismatched"); |
| } |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetForgetLayerNormWeights()), |
| reasonIfUnsupported, |
| "Reference Lstm: input and ForgetLayerNormWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetCellLayerNormWeights()), |
| reasonIfUnsupported, |
| "Reference Lstm: input and CellLayerNormWeights types are mismatched"); |
| supported &= CheckSupportRule(TypesAreEqual(input, paramsInfo.GetOutputLayerNormWeights()), |
| reasonIfUnsupported, |
| "Reference Lstm: input and OutputLayerNormWeights types are mismatched"); |
| } |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMaximumSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference maximum: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference maximum: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference maximum: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference maximum: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference maximum: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference maximum: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMeanSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const MeanDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| std::string meanLayerStr = "Mean"; |
| std::string outputTensorStr = "output"; |
| |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Mean: input type not supported."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Mean: input and output types are mismatched"); |
| |
| if (descriptor.m_KeepDims) |
| { |
| supported &= CheckSupportRule(TensorNumDimensionsAreCorrect(output, input.GetNumDimensions()), |
| reasonIfUnsupported, |
| CreateIncorrectDimensionsErrorMsg(input.GetNumDimensions(), |
| output.GetNumDimensions(), |
| meanLayerStr, outputTensorStr).data()); |
| } |
| else if (descriptor.m_Axis.empty()) |
| { |
| supported &= CheckSupportRule(TensorNumDimensionsAreCorrect(output, 1), |
| reasonIfUnsupported, |
| CreateIncorrectDimensionsErrorMsg(1, output.GetNumDimensions(), |
| meanLayerStr, outputTensorStr).data()); |
| } |
| else |
| { |
| auto outputDim = input.GetNumDimensions() - boost::numeric_cast<unsigned int>(descriptor.m_Axis.size()); |
| |
| if (outputDim > 0) |
| { |
| supported &= CheckSupportRule(TensorNumDimensionsAreCorrect(output, outputDim), |
| reasonIfUnsupported, |
| CreateIncorrectDimensionsErrorMsg(outputDim, output.GetNumDimensions(), |
| meanLayerStr, outputTensorStr).data()); |
| } |
| else |
| { |
| supported &= CheckSupportRule(TensorNumDimensionsAreCorrect(output, 1), |
| reasonIfUnsupported, |
| CreateIncorrectDimensionsErrorMsg(1, output.GetNumDimensions(), |
| meanLayerStr, outputTensorStr).data()); |
| } |
| } |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMergerSupported(const std::vector<const TensorInfo*> inputs, |
| const TensorInfo& output, |
| const MergerDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return IsConcatSupported(inputs, output, descriptor, reasonIfUnsupported); |
| } |
| |
| bool RefLayerSupport::IsMemCopySupported(const TensorInfo &input, |
| const TensorInfo &output, |
| Optional<std::string &> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,5> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16, |
| DataType::Boolean |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference MemCopy: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference MemCopy: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference MemCopy: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMinimumSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference minimum: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference minimum: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference minimum: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference minimum: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference minimum: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference minimum: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsMultiplicationSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference multiplication: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference multiplication: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference multiplication: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference multiplication: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference multiplication: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference multiplication: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsNormalizationSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const NormalizationDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| |
| // Define supported types |
| std::array<DataType, 4> supportedTypes = |
| { |
| DataType::Float16, |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| bool supported = true; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference normalization: input type not supported."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference normalization: output type not supported."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference normalization: input and output shapes have different " |
| "num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsOutputSupported(const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| return true; |
| } |
| |
| bool RefLayerSupport::IsPadSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const PadDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| // Define supported output and inputs types. |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference pad: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference pad: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference pad: input and output types are mismatched."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsPermuteSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const PermuteDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| // Define supported output and inputs types. |
| std::array<DataType,3> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference permute: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference permute: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference permute: input and output types are mismatched."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsPooling2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const Pooling2dDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| // Define supported output and inputs types. |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference poolind2d: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference poolind2d: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference poolind2d: input and output types are mismatched."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsQuantizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| // Define supported output types. |
| std::array<DataType,1> supportedInputTypes = { |
| DataType::Float32, |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedInputTypes), reasonIfUnsupported, |
| "Reference quantize: input type not supported."); |
| |
| // Define supported output types. |
| std::array<DataType,2> supportedOutputTypes = { |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedOutputTypes), reasonIfUnsupported, |
| "Reference quantize: output type not supported."); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference quantize: input and output shapes have different num total elements."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsReshapeSupported(const TensorInfo& input, |
| const ReshapeDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| // Define supported output types. |
| std::array<DataType,5> supportedOutputTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::Signed32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| return CheckSupportRule(TypeAnyOf(input, supportedOutputTypes), reasonIfUnsupported, |
| "Reference reshape: input type not supported."); |
| } |
| |
| bool RefLayerSupport::IsResizeBilinearSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference ResizeBilinear: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference ResizeBilinear: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference ResizeBilinear: input and output types not matching"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsResizeSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const ResizeDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Resize: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference Resize: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Resize: input and output types not matching"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsRsqrtSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference rsqrt: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference rsqrt: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference rsqrt: input and output types not matching"); |
| |
| supported &= CheckSupportRule(ShapesAreSameTotalSize(input, output), reasonIfUnsupported, |
| "Reference Rsqrt: input and output shapes have different number of total elements"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSliceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SliceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| std::array<DataType, 3> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference Slice: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference Slice: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference Slice: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSoftmaxSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SoftmaxDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference concatenation: output type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference concatenation: input type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference concatenation: input type not supported"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSpaceToBatchNdSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SpaceToBatchNdDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(output); |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference SpaceToBatchNd: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference SpaceToBatchNd: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference SpaceToBatchNd: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSpaceToDepthSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const SpaceToDepthDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference SpaceToDepth: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference SpaceToDepth: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference SpaceToDepth: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSplitterSupported(const TensorInfo& input, |
| const ViewsDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference splitter: input type not supported"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSplitterSupported(const TensorInfo& input, |
| const std::vector<std::reference_wrapper<TensorInfo>>& outputs, |
| const ViewsDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference splitter: output type not supported"); |
| for (const TensorInfo output : outputs) |
| { |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference splitter: input type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference splitter: input and output types mismatched."); |
| } |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsStackSupported(const std::vector<const TensorInfo*>& inputs, |
| const TensorInfo& output, |
| const StackDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| |
| bool supported = true; |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference stack: output type not supported"); |
| for (const TensorInfo* input : inputs) |
| { |
| BOOST_ASSERT(input != nullptr); |
| supported &= CheckSupportRule(TypeAnyOf(*input, supportedTypes), reasonIfUnsupported, |
| "Reference stack: input type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(*input, output), reasonIfUnsupported, |
| "Reference stack: input and output types mismatched."); |
| } |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsStridedSliceSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const StridedSliceDescriptor& descriptor, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| ignore_unused(descriptor); |
| bool supported = true; |
| |
| std::array<DataType,3> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference StridedSlice: input type not supported"); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference StridedSlice: output type not supported"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference StridedSlice: input and output types are mismatched"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsSubtractionSupported(const TensorInfo& input0, |
| const TensorInfo& input1, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input0, supportedTypes), reasonIfUnsupported, |
| "Reference subtraction: input 0 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(input1, supportedTypes), reasonIfUnsupported, |
| "Reference subtraction: input 1 is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference subtraction: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, input1), reasonIfUnsupported, |
| "Reference subtraction: input 0 and Input 1 types are mismatched"); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input0, output), reasonIfUnsupported, |
| "Reference subtraction: input and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input0, input1, output), reasonIfUnsupported, |
| "Reference subtraction: shapes are not suitable for implicit broadcast."); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsPreluSupported(const TensorInfo& input, |
| const TensorInfo& alpha, |
| const TensorInfo& output, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType, 4> supportedTypes |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "PReLU: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(alpha, supportedTypes), reasonIfUnsupported, |
| "PReLU: alpha is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "PReLU: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, alpha, output), reasonIfUnsupported, |
| "PReLU: input, alpha and output types are mismatched"); |
| |
| supported &= CheckSupportRule(ShapesAreBroadcastCompatible(input, alpha, output), reasonIfUnsupported, |
| "PReLU: shapes are not suitable for implicit broadcast"); |
| |
| return supported; |
| } |
| |
| bool RefLayerSupport::IsTransposeConvolution2dSupported(const TensorInfo& input, |
| const TensorInfo& output, |
| const TransposeConvolution2dDescriptor& descriptor, |
| const TensorInfo& weights, |
| const Optional<TensorInfo>& biases, |
| Optional<std::string&> reasonIfUnsupported) const |
| { |
| bool supported = true; |
| |
| std::array<DataType,4> supportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::QuantisedAsymm8, |
| DataType::QuantisedSymm16 |
| }; |
| |
| supported &= CheckSupportRule(TypeAnyOf(input, supportedTypes), reasonIfUnsupported, |
| "Reference TransposeConvolution2d: input is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(output, supportedTypes), reasonIfUnsupported, |
| "Reference TransposeConvolution2d: output is not a supported type."); |
| |
| supported &= CheckSupportRule(TypeAnyOf(weights, supportedTypes), reasonIfUnsupported, |
| "Reference TransposeConvolution2d: weights is not a supported type."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, output), reasonIfUnsupported, |
| "Reference TransposeConvolution2d: input and output types mismatched."); |
| |
| supported &= CheckSupportRule(TypesAreEqual(input, weights), reasonIfUnsupported, |
| "Reference TransposeConvolution2d: input and weights types mismatched."); |
| |
| if (biases.has_value()) |
| { |
| std::array<DataType,3> biasesSupportedTypes = |
| { |
| DataType::Float32, |
| DataType::Float16, |
| DataType::Signed32 |
| }; |
| supported &= CheckSupportRule(TypeAnyOf(biases.value(), biasesSupportedTypes), reasonIfUnsupported, |
| "Reference TransposeConvolution2d: biases is not a supported type."); |
| } |
| |
| return supported; |
| } |
| |
| } // namespace armnn |
