193 virtual const BackendId& GetId()
const = 0;
206 virtual const BackendIdSet& GetSupportedBackends()
const = 0;
217 using ArrayType = std::array<ValueType, MaxNumOfTensorDimensions>;
248 if (m_NumDimMappings != other.m_NumDimMappings)
return false;
249 for (
unsigned int i = 0; i < m_NumDimMappings; ++i)
251 if (m_DimMappings[i] != other.m_DimMappings[i])
return false;
258 bool isInverse = (GetSize() == other.
GetSize());
259 for (
SizeType i = 0; isInverse && (i < GetSize()); ++i)
261 isInverse = (m_DimMappings[other.m_DimMappings[i]] == i);
283 using DebugCallbackFunction = std::function<void(LayerGuid guid, unsigned int slotIndex, ITensorHandle* tensorHandle)>;
289 static constexpr uint64_t MIN_STATIC_GUID = 1llu << 63;
298 operator uint64_t()
const {
return m_Guid; }
302 return m_Guid == other.
m_Guid;
307 return m_Guid != other.
m_Guid;
312 return m_Guid < other.
m_Guid;
317 return m_Guid <= other.
m_Guid;
322 return m_Guid > other.
m_Guid;
327 return m_Guid >= other.
m_Guid;
349 #define LIST_OF_LAYER_TYPE \ 353 X(BatchNormalization) \ 358 X(ConvertBf16ToFp32) \ 359 X(ConvertFp16ToFp32) \ 360 X(ConvertFp32ToBf16) \ 361 X(ConvertFp32ToFp16) \ 365 X(DepthwiseConvolution2d) \ 367 X(DetectionPostProcess) \ 369 X(ElementwiseUnary) \ 370 X(FakeQuantization) \ 376 X(InstanceNormalization) \ 414 X(TransposeConvolution2d) \ 421 #define X(name) name, 437 struct hash<
armnn::profiling::ProfilingGuid>
441 return hash<uint64_t>()(uint64_t(guid));
447 struct hash<
armnn::profiling::ProfilingDynamicGuid>
451 return hash<uint64_t>()(uint64_t(guid));
457 struct hash<
armnn::profiling::ProfilingStaticGuid>
461 return hash<uint64_t>()(uint64_t(guid));
bool operator<(const ProfilingGuid &other) const
bool operator>=(const ProfilingGuid &other) const
bool operator==(const ProfilingGuid &other) const
std::unordered_set< BackendId > BackendIdSet
std::size_t operator()(armnn::profiling::ProfilingGuid const &guid) const noexcept
typename ArrayType::const_iterator ConstIterator
Each backend should implement an IBackend.
Strongly typed guids to distinguish between those generated at runtime, and those that are statically...
The padding fields don't count and are ignored.
#define LIST_OF_LAYER_TYPE
This list uses X macro technique.
NormalizationAlgorithmChannel
Copyright (c) 2021 ARM Limited and Contributors.
std::function< void(LayerGuid guid, unsigned int slotIndex, ITensorHandle *tensorHandle)> DebugCallbackFunction
Define the type of callback for the Debug layer to call.
std::size_t operator()(armnn::profiling::ProfilingDynamicGuid const &guid) const noexcept
int LayerBindingId
Type of identifiers for bindable layers (inputs, outputs).
PaddingMethod
The padding method modifies the output of pooling layers.
std::shared_ptr< IBackend > IBackendSharedPtr
bool operator<=(const ProfilingGuid &other) const
#define ARMNN_DEPRECATED_ENUM_MSG(message)
std::size_t operator()(armnn::profiling::ProfilingStaticGuid const &guid) const noexcept
std::array< ValueType, MaxNumOfTensorDimensions > ArrayType
Validate all output shapes.
Device specific knowledge to be passed to the optimizer.
constexpr unsigned int LOWEST_CAPTURE_PERIOD
The lowest performance data capture interval we support is 10 miliseconds.
std::unique_ptr< IBackend, void(*)(IBackend *backend)> IBackendUniquePtr
min(a, max(b, input)) ReLu1 & ReLu6.
ValueType operator[](SizeType i) const
bool IsEqual(const PermutationVector &other) const
The padding fields count, but are ignored.
ProfilingGuid(uint64_t guid)
bool operator!=(const ProfilingGuid &other) const
Jarret 2009: Local Contrast Normalization.
ConstIterator begin() const
ConstIterator end() const
Infer missing output shapes and validate all output shapes.
Krichevsky 2012: Local Brightness Normalization.
bool IsInverse(const PermutationVector &other) const
NormalizationAlgorithmMethod
bool operator>(const ProfilingGuid &other) const
ShapeInferenceMethod
The ShapeInferenceMethod modify how the output shapes are treated.
const char * GetLayerTypeAsCString(LayerType type)
constexpr unsigned int MaxNumOfTensorDimensions
LayerType
When adding a new layer, adapt also the LastLayer enum value in the enum class LayerType below...