| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // See LICENSE file in the project root for full license information. |
| // |
| #pragma once |
| |
| #include <array> |
| |
| namespace armnn |
| { |
| |
| constexpr unsigned int MaxNumOfTensorDimensions = 4U; |
| |
| /// @enum Status enumeration |
| /// @var Status::Successful |
| /// @var Status::Failure |
| enum class Status |
| { |
| Success = 0, |
| Failure = 1 |
| }; |
| |
| enum class DataType |
| { |
| Float16 = 0, |
| Float32 = 1, |
| QuantisedAsymm8 = 2, |
| Signed32 = 3 |
| }; |
| |
| enum class ActivationFunction |
| { |
| Sigmoid = 0, |
| TanH = 1, |
| Linear = 2, |
| ReLu = 3, |
| BoundedReLu = 4, ///< min(a, max(b, input)) |
| SoftReLu = 5, |
| LeakyReLu = 6, |
| Abs = 7, |
| Sqrt = 8, |
| Square = 9 |
| }; |
| |
| enum class PoolingAlgorithm |
| { |
| Max = 0, |
| Average = 1, |
| L2 = 2 |
| }; |
| |
| /// |
| /// The padding method modifies the output of pooling layers. |
| /// In both supported methods, the values are ignored (they are |
| /// not even zeroes, which would make a difference for max pooling |
| /// a tensor with negative values). The difference between |
| /// IgnoreValue and Exclude is that the former counts the padding |
| /// fields in the divisor of Average and L2 pooling, while |
| /// Exclude does not. |
| /// |
| enum class PaddingMethod |
| { |
| /// The padding fields count, but are ignored |
| IgnoreValue = 0, |
| /// The padding fields don't count and are ignored |
| Exclude = 1 |
| }; |
| |
| enum class NormalizationAlgorithmChannel |
| { |
| Across = 0, |
| Within = 1 |
| }; |
| |
| enum class NormalizationAlgorithmMethod |
| { |
| /// Krichevsky 2012: Local Brightness Normalization |
| LocalBrightness = 0, |
| /// Jarret 2009: Local Contrast Normalization |
| LocalContrast = 1 |
| }; |
| |
| enum class OutputShapeRounding |
| { |
| Floor = 0, |
| Ceiling = 1 |
| }; |
| |
| enum class Compute |
| { |
| /// CPU Execution: Reference C++ kernels |
| CpuRef = 0, |
| /// CPU Execution: NEON: ArmCompute |
| CpuAcc = 1, |
| /// GPU Execution: OpenCL: ArmCompute |
| GpuAcc = 2, |
| Undefined = 5 |
| }; |
| |
| class IDeviceSpec |
| { |
| protected: |
| IDeviceSpec() {}; |
| virtual ~IDeviceSpec() {}; |
| }; |
| |
| /// Type of identifiers for bindable layers (inputs, outputs). |
| using LayerBindingId = int; |
| |
| class PermutationVector |
| { |
| public: |
| using ValueType = unsigned int; |
| using SizeType = unsigned int; |
| using ArrayType = std::array<ValueType, MaxNumOfTensorDimensions>; |
| using ConstIterator = typename ArrayType::const_iterator; |
| |
| /// @param dimMappings - Indicates how to translate tensor elements from a given source into the target destination, |
| /// when source and target potentially have different memory layouts. |
| /// |
| /// E.g. For a 4-d tensor laid out in a memory with the format (Batch Element, Height, Width, Channels), |
| /// which is to be passed as an input to ArmNN, each source dimension is mapped to the corresponding |
| /// ArmNN dimension. The Batch dimension remains the same (0 -> 0). The source Height dimension is mapped |
| /// to the location of the ArmNN Height dimension (1 -> 2). Similar arguments are made for the Width and |
| /// Channels (2 -> 3 and 3 -> 1). This will lead to @ref m_DimMappings pointing to the following array: |
| /// [ 0, 2, 3, 1 ]. |
| /// |
| /// Note that the mapping should be reversed if considering the case of ArmNN 4-d outputs (Batch Element, |
| /// Channels, Height, Width) being written to a destination with the format mentioned above. We now have |
| /// 0 -> 0, 2 -> 1, 3 -> 2, 1 -> 3, which, when reordered, lead to the following @ref m_DimMappings contents: |
| /// [ 0, 3, 1, 2 ]. |
| /// |
| PermutationVector(const ValueType *dimMappings, SizeType numDimMappings); |
| |
| PermutationVector(std::initializer_list<ValueType> dimMappings); |
| |
| ValueType operator[](SizeType i) const { return m_DimMappings.at(i); } |
| |
| SizeType GetSize() const { return m_NumDimMappings; } |
| |
| ConstIterator begin() const { return m_DimMappings.begin(); } |
| ConstIterator end() const { return m_DimMappings.end(); } |
| |
| bool IsEqual(const PermutationVector& other) const |
| { |
| return std::equal(begin(), end(), other.begin(), other.end()); |
| } |
| |
| bool IsInverse(const PermutationVector& other) const |
| { |
| bool isInverse = (GetSize() == other.GetSize()); |
| for (SizeType i = 0; isInverse && (i < GetSize()); ++i) |
| { |
| isInverse = (m_DimMappings[other.m_DimMappings[i]] == i); |
| } |
| return isInverse; |
| } |
| |
| private: |
| ArrayType m_DimMappings; |
| /// Number of valid entries in @ref m_DimMappings |
| SizeType m_NumDimMappings; |
| }; |
| |
| /// Define LayerGuid type. |
| using LayerGuid = unsigned int; |
| |
| } |