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Diffstat (limited to 'src/backends/aclCommon/ArmComputeUtils.hpp')
| -rw-r--r-- | src/backends/aclCommon/ArmComputeUtils.hpp | 173 |
1 files changed, 0 insertions, 173 deletions
diff --git a/src/backends/aclCommon/ArmComputeUtils.hpp b/src/backends/aclCommon/ArmComputeUtils.hpp deleted file mode 100644 index 4d690901c6..0000000000 --- a/src/backends/aclCommon/ArmComputeUtils.hpp +++ /dev/null @@ -1,173 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <armnn/Descriptors.hpp> -#include <armnn/Tensor.hpp> - -#include <arm_compute/core/Types.h> - -#include <boost/assert.hpp> - -namespace armnn -{ - -inline arm_compute::NormalizationLayerInfo -CreateAclNormalizationLayerInfoForL2Normalization(const armnn::TensorInfo& tensorInfo, - armnn::DataLayout dataLayout) -{ - unsigned int depthDimension = dataLayout == armnn::DataLayout::NCHW ? 1 : 3; - const unsigned int depth = tensorInfo.GetShape()[depthDimension]; - - // At the time of writing, {CL|Neon}L2Normalization performs the reduction only along dimension 0. This version of - // L2 Normalization always performs the reduction along the depth axis, though. Thus, we repurpose - // {CL|Neon}NormalizationLayers to act as depthwise L2 normalizations by carefully chosing the normalization - // parameters. - // - // Please refer to both the reference implementation of the normalization layer and the implementation of - // {CL|Neon}NormalizationLayer when checking the derivations for the parameter values below. - - // Make sure normalization covers the entire depth range. ACL requires the normalization size to be odd. - // CL: This does not result in extra kernel threads not doing any work: See usage of the RADIUS parameter in - // ACL's normalization_layer_cross_map() CL function. - const uint32_t normSize = depth * 2u + 1u; - - // See ACL's NormalizationLayerInfo::scale_coeff() definition. - // For the reference implementation, to make alpha_ become 1, we'd have to use alpha = normSize instead. - const float alpha = 1.0f; - - // Don't offset the reduction. - const float kappa = 0.0f; - - // pow(reduction, -0.5) = 1 / sqrt(reduction) - const float beta = 0.5f; - - return arm_compute::NormalizationLayerInfo(arm_compute::NormType::CROSS_MAP, normSize, alpha, beta, kappa, false); -} - -inline arm_compute::ActivationLayerInfo::ActivationFunction -ConvertActivationFunctionToAclActivationFunction(ActivationFunction armnnFunction) -{ - using AclActivationFunction = arm_compute::ActivationLayerInfo::ActivationFunction; - - switch (armnnFunction) - { - case ActivationFunction::Linear: return AclActivationFunction::LINEAR; - // Arm compute's 'logistic' function is non-parameterized, so it is exactly a sigmoid function. - case ActivationFunction::Sigmoid: return AclActivationFunction::LOGISTIC; - case ActivationFunction::ReLu: return AclActivationFunction::RELU; - case ActivationFunction::BoundedReLu: return AclActivationFunction::LU_BOUNDED_RELU; - case ActivationFunction::SoftReLu: return AclActivationFunction::SOFT_RELU; - case ActivationFunction::LeakyReLu: return AclActivationFunction::LEAKY_RELU; - case ActivationFunction::Abs: return AclActivationFunction::ABS; - case ActivationFunction::Sqrt: return AclActivationFunction::SQRT; - case ActivationFunction::Square: return AclActivationFunction::SQUARE; - case ActivationFunction::TanH: return AclActivationFunction::TANH; - default: throw InvalidArgumentException("Unsupported activation function"); - } -} - -inline arm_compute::ActivationLayerInfo -ConvertActivationDescriptorToAclActivationLayerInfo(const ActivationDescriptor& actDesc) -{ - return arm_compute::ActivationLayerInfo(ConvertActivationFunctionToAclActivationFunction(actDesc.m_Function), - actDesc.m_A, actDesc.m_B); -} - -inline arm_compute::PoolingType ConvertPoolingAlgorithmToAclPoolingType(PoolingAlgorithm poolingAlgorithm) -{ - using arm_compute::PoolingType; - - switch (poolingAlgorithm) - { - case PoolingAlgorithm::Max: return PoolingType::MAX; - case PoolingAlgorithm::Average: return PoolingType::AVG; - case PoolingAlgorithm::L2: return PoolingType::L2; - default: throw InvalidArgumentException("Unsupported pooling algorithm"); - } -} - -inline arm_compute::DimensionRoundingType ConvertOutputShapeRoundingToAclDimensionRoundingType(OutputShapeRounding - rounding) -{ - using arm_compute::DimensionRoundingType; - - switch (rounding) - { - case OutputShapeRounding::Ceiling: return DimensionRoundingType::CEIL; - case OutputShapeRounding::Floor: return DimensionRoundingType::FLOOR; - default: throw InvalidArgumentException("Unsupported Output Shape Rounding type"); - } -} - -inline arm_compute::NormType -ConvertNormalizationAlgorithmChannelToAclNormType(NormalizationAlgorithmChannel channelType) -{ - using arm_compute::NormType; - switch (channelType) - { - case NormalizationAlgorithmChannel::Across: return NormType::CROSS_MAP; - case NormalizationAlgorithmChannel::Within: return NormType::IN_MAP_2D; - default: throw InvalidArgumentException("Unsupported normalization algorithm channel type"); - } -} - -inline arm_compute::FullyConnectedLayerInfo -ConvertFullyConnectedDescriptorToAclFullyConnectedLayerInfo(const FullyConnectedDescriptor& fullyConnectedDesc) -{ - arm_compute::FullyConnectedLayerInfo fc_info; - fc_info.transpose_weights = fullyConnectedDesc.m_TransposeWeightMatrix; - return fc_info; -} - -inline arm_compute::InterpolationPolicy ConvertResizeMethodToAclInterpolationPolicy(ResizeMethod resizeMethod) -{ - switch (resizeMethod) - { - case ResizeMethod::Bilinear: - return arm_compute::InterpolationPolicy::BILINEAR; - case ResizeMethod::NearestNeighbor: - return arm_compute::InterpolationPolicy::NEAREST_NEIGHBOR; - default: - throw InvalidArgumentException("Unsupported resize method"); - } -} - -inline unsigned int ComputeSoftmaxAclAxis(const SoftmaxDescriptor& softmaxDesc, const armnn::TensorInfo& tensor) -{ - // Detect the Android default value of -1 and return the ACL default value of 1. - if (softmaxDesc.m_Axis == -1) - { - return 1; - } - - unsigned int dim = tensor.GetNumDimensions(); - - BOOST_ASSERT(dim != 0); - - // Currently ArmNN support axis 1. - return dim - 1; -} - -inline std::set<unsigned int> ComputeSplitAxis(const armnn::SplitterDescriptor& desc, const TensorShape& input) -{ - unsigned int numSplit = desc.GetNumViews(); - unsigned int numDimensions = desc.GetNumDimensions(); - std::set<unsigned int> splitAxis; - - for (unsigned int i = 0; i < numSplit; ++i) - { - for (unsigned int dimIdx = 0; dimIdx < numDimensions; ++dimIdx) - { - if (desc.GetViewSizes(i)[dimIdx] != input[dimIdx]) - { - splitAxis.insert(dimIdx); - } - } - } - return splitAxis; -} - -} // namespace armnn |