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Diffstat (limited to 'src/armnn/backends/ArmComputeUtils.hpp')
-rw-r--r-- | src/armnn/backends/ArmComputeUtils.hpp | 125 |
1 files changed, 0 insertions, 125 deletions
diff --git a/src/armnn/backends/ArmComputeUtils.hpp b/src/armnn/backends/ArmComputeUtils.hpp deleted file mode 100644 index db472964ea..0000000000 --- a/src/armnn/backends/ArmComputeUtils.hpp +++ /dev/null @@ -1,125 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#if ARMCOMPUTENEON_ENABLED || ARMCOMPUTECL_ENABLED - -#include <armnn/Tensor.hpp> -#include <armnn/Descriptors.hpp> - -#include <arm_compute/core/Types.h> - -namespace armnn -{ - -inline arm_compute::NormalizationLayerInfo -CreateAclNormalizationLayerInfoForL2Normalization(const armnn::TensorInfo& tensorInfo) -{ - const unsigned int depth = tensorInfo.GetShape()[1]; - - // 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; -} - -} - -#endif // ARMCOMPUTENEON_ENABLED || ARMCOMPUTECL_ENABLED |