Types.hpp File Reference

#include <array>
#include <functional>
#include <stdint.h>
#include <chrono>
#include "BackendId.hpp"
#include "Exceptions.hpp"
#include "Deprecated.hpp"

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Classes
class	IBackend
	Each backend should implement an IBackend. More...
class	IDeviceSpec
	Device specific knowledge to be passed to the optimizer. More...
class	PermutationVector

Namespaces
namespace	arm
namespace	arm::pipe
namespace	armnn
	Copyright (c) 2021 ARM Limited and Contributors.

Macros
#define	LIST_OF_LAYER_TYPE
	This list uses X macro technique.
#define	X(name)

Typedefs
using	LayerGuid = arm::pipe::ProfilingGuid
	Define LayerGuid type.
using	IBackendSharedPtr = std::shared_ptr<IBackend>
using	IBackendUniquePtr = std::unique_ptr<IBackend, void(*)(IBackend* backend)>
using	LayerBindingId = int
	Type of identifiers for bindable layers (inputs, outputs).
using	ImportedInputId = unsigned int
using	ImportedOutputId = unsigned int
using	DebugCallbackFunction = std::function<void(LayerGuid guid, unsigned int slotIndex, ITensorHandle* tensorHandle)>
	Define the type of callback for the Debug layer to call.
using	HighResolutionClock = std::chrono::high_resolution_clock::time_point
	Define a timer and associated inference ID for recording execution times.
using	InferenceTimingPair = std::pair<HighResolutionClock, HighResolutionClock>

Enumerations
enum class	Status { Success = 0 , Failure = 1 }
	enumeration More...
enum class	DataType {   Float16 = 0 , Float32 = 1 , QAsymmU8 = 2 , Signed32 = 3 ,   Boolean = 4 , QSymmS16 = 5 , QSymmS8 = 6 , QAsymmS8 = 7 ,   BFloat16 = 8 , Signed64 = 9 }
enum class	DataLayout { NCHW = 1 , NHWC = 2 , NDHWC = 3 , NCDHW = 4 }
enum class	ProfilingDetailsMethod { Undefined = 0 , DetailsWithEvents = 1 , DetailsOnly = 2 }
	Define the behaviour of the internal profiler when outputting network details. More...
enum class	QosExecPriority { Low = 0 , Medium = 1 , High = 2 }
enum class	ActivationFunction {   Sigmoid = 0 , TanH = 1 , Linear = 2 , ReLu = 3 ,   BoundedReLu = 4 , SoftReLu = 5 , LeakyReLu = 6 , Abs = 7 ,   Sqrt = 8 , Square = 9 , Elu = 10 , HardSwish = 11 ,   Gelu = 12 }
enum class	ArgMinMaxFunction { Min = 0 , Max = 1 }
enum class	ComparisonOperation {   Equal = 0 , Greater = 1 , GreaterOrEqual = 2 , Less = 3 ,   LessOrEqual = 4 , NotEqual = 5 }
enum class	LogicalBinaryOperation { LogicalAnd = 0 , LogicalOr = 1 }
enum class	UnaryOperation {   Abs = 0 , Exp = 1 , Sqrt = 2 , Rsqrt = 3 ,   Neg = 4 , LogicalNot = 5 , Log = 6 , Sin = 7 ,   Ceil = 8 }
enum class	BinaryOperation {   Add = 0 , Div = 1 , Maximum = 2 , Minimum = 3 ,   Mul = 4 , Sub = 5 , SqDiff = 6 , Power = 7 ,   FloorDiv = 8 }
enum class	PoolingAlgorithm { Max = 0 , Average = 1 , L2 = 2 }
enum class	ReduceOperation {   Sum = 0 , Max = 1 , Mean = 2 , Min = 3 ,   Prod = 4 }
enum class	ResizeMethod { Bilinear = 0 , NearestNeighbor = 1 }
enum class	Dimensionality { NotSpecified = 0 , Specified = 1 , Scalar = 2 }
enum class	PaddingMethod { IgnoreValue = 0 , Exclude = 1 }
	The padding method modifies the output of pooling layers. More...
enum class	PaddingMode { Constant = 0 , Reflect = 1 , Symmetric = 2 }
	The padding mode controls whether the padding should be filled with constant values (Constant), or reflect the input, either including the border values (Symmetric) or not (Reflect). More...
enum class	NormalizationAlgorithmChannel { Across = 0 , Within = 1 }
enum class	NormalizationAlgorithmMethod { LocalBrightness = 0 , LocalContrast = 1 }
enum class	OutputShapeRounding { Floor = 0 , Ceiling = 1 }
enum class	ShapeInferenceMethod { ValidateOnly = 0 , InferAndValidate = 1 }
	The ShapeInferenceMethod modify how the output shapes are treated. More...
enum class	MemorySource : uint32_t {   Undefined = 0 , Malloc = 1 , DmaBuf = 2 , DmaBufProtected = 4 ,   Gralloc = 8 }
	Define the Memory Source to reduce copies. More...
enum class	MemBlockStrategyType { SingleAxisPacking = 0 , MultiAxisPacking = 1 }
enum class	FusedKernelType { AddMulAdd = 0 }
enum class	BackendCapability : uint32_t { NonConstWeights , AllOrNothing }
	BackendCapability class. More...
enum class	LayerType {   Activation , Addition , ArgMinMax , BatchNormalization ,   BatchToSpaceNd , Comparison , Concat , Constant ,   ConvertFp16ToFp32 , ConvertFp32ToFp16 , Convolution2d , Debug ,   DepthToSpace , DepthwiseConvolution2d , Dequantize , DetectionPostProcess ,   Division , ElementwiseUnary , FakeQuantization , Fill ,   Floor , FullyConnected , Gather , Input ,   InstanceNormalization , L2Normalization , LogicalBinary , LogSoftmax ,   Lstm , QLstm , Map , Maximum ,   Mean , MemCopy , MemImport , Merge ,   Minimum , Multiplication , Normalization , Output ,   Pad , Permute , Pooling2d , PreCompiled ,   Prelu , Quantize , QuantizedLstm , Reshape ,   Rank , Resize , Reduce , Slice ,   Softmax , SpaceToBatchNd , SpaceToDepth , Splitter ,   Stack , StandIn , StridedSlice , Subtraction ,   Switch , Transpose , TransposeConvolution2d , Unmap ,   Cast , Shape , UnidirectionalSequenceLstm , ChannelShuffle ,   Convolution3d , Pooling3d , GatherNd , BatchMatMul ,   ElementwiseBinary , ReverseV2 , Tile , Fused ,   BroadcastTo , ScatterNd , FirstLayer = Activation , LastLayer = ScatterNd }
	When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below. More...
enum class	ScatterNdFunction {   Update = 0 , Add = 1 , Sub = 2 , Max = 3 ,   Min = 4 , Mul = 5 }

Functions
const char *	GetLayerTypeAsCString (LayerType type)

Variables
constexpr unsigned int	MaxNumOfTensorDimensions = 5U
constexpr unsigned int	LOWEST_CAPTURE_PERIOD = 10000u
	The lowest performance data capture interval we support is 10 miliseconds.
constexpr unsigned int	EXPIRE_RATE = 3U
	Variable to control expire rate of priority queue.

Macro Definition Documentation

LIST_OF_LAYER_TYPE

#define LIST_OF_LAYER_TYPE

This list uses X macro technique.

See https://en.wikipedia.org/wiki/X_Macro for more info

Definition at line 410 of file Types.hpp.

#define LIST_OF_LAYER_TYPE \
    X(Activation) \
    X(Addition) \
    X(ArgMinMax) \
    X(BatchNormalization) \
    X(BatchToSpaceNd)      \
    X(Comparison) \
    X(Concat) \
    X(Constant) \
    X(ConvertFp16ToFp32) \
    X(ConvertFp32ToFp16) \
    X(Convolution2d) \
    X(Debug) \
    X(DepthToSpace) \
    X(DepthwiseConvolution2d) \
    X(Dequantize) \
    X(DetectionPostProcess) \
    X(Division) \
    X(ElementwiseUnary) \
    X(FakeQuantization) \
    X(Fill) \
    X(Floor) \
    X(FullyConnected) \
    X(Gather) \
    X(Input) \
    X(InstanceNormalization) \
    X(L2Normalization) \
    X(LogicalBinary) \
    X(LogSoftmax) \
    X(Lstm) \
    X(QLstm) \
    X(Map) \
    X(Maximum) \
    X(Mean) \
    X(MemCopy) \
    X(MemImport) \
    X(Merge) \
    X(Minimum) \
    X(Multiplication) \
    X(Normalization) \
    X(Output) \
    X(Pad) \
    X(Permute) \
    X(Pooling2d) \
    X(PreCompiled) \
    X(Prelu) \
    X(Quantize) \
    X(QuantizedLstm) \
    X(Reshape) \
    X(Rank) \
    X(Resize) \
    X(Reduce) \
    X(Slice) \
    X(Softmax) \
    X(SpaceToBatchNd) \
    X(SpaceToDepth) \
    X(Splitter) \
    X(Stack) \
    X(StandIn) \
    X(StridedSlice) \
    X(Subtraction) \
    X(Switch) \
    X(Transpose) \
    X(TransposeConvolution2d) \
    X(Unmap) \
    X(Cast) \
    X(Shape) \
    X(UnidirectionalSequenceLstm) \
    X(ChannelShuffle) \
    X(Convolution3d) \
    X(Pooling3d) \
    X(GatherNd) \
    X(BatchMatMul) \
    X(ElementwiseBinary) \
    X(ReverseV2) \
    X(Tile) \
    X(Fused) \
    X(BroadcastTo)         \
    X(ScatterNd) \
// New layers should be added at last position to minimize instability.

Referenced by armnn::GetLayerTypeAsCString().

X

#define X

(

name

)

Value:

name,

Definition at line 495 of file Types.hpp.

Typedef Documentation

LayerGuid

using LayerGuid = arm::pipe::ProfilingGuid

Define LayerGuid type.

Definition at line 26 of file Types.hpp.