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author | David Beck <david.beck@arm.com> | 2018-09-24 15:59:27 +0100 |
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committer | Matthew Bentham <matthew.bentham@arm.com> | 2018-10-10 16:16:57 +0100 |
commit | 0dbe0ee25312b728d77383d11c465156e64ae757 (patch) | |
tree | af37a9802e3ad551e1bf63f7636508cde7a41643 /src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp | |
parent | b4540bef0b0327683fe8e63f727c1212800dc2a9 (diff) | |
download | armnn-0dbe0ee25312b728d77383d11c465156e64ae757.tar.gz |
IVGCVSW-1899 : Neon backend folder structure
armnn:149855
Change-Id: I26e8cf83422a65049386a5ebdb6d0001627aefaa
Diffstat (limited to 'src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp')
-rw-r--r-- | src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp | 70 |
1 files changed, 70 insertions, 0 deletions
diff --git a/src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp b/src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp new file mode 100644 index 0000000000..472c75f222 --- /dev/null +++ b/src/backends/neon/workloads/NeonNormalizationFloatWorkload.cpp @@ -0,0 +1,70 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "NeonNormalizationFloatWorkload.hpp" +#include <backends/neon/NeonLayerSupport.hpp> +#include <backends/aclCommon/ArmComputeUtils.hpp> +#include <backends/aclCommon/ArmComputeTensorUtils.hpp> + +namespace armnn +{ + +arm_compute::Status NeonNormalizationWorkloadValidate(const TensorInfo& input, + const TensorInfo& output, + const NormalizationDescriptor& descriptor) +{ + const arm_compute::TensorInfo aclInput = armcomputetensorutils::BuildArmComputeTensorInfo(input); + const arm_compute::TensorInfo aclOutput = armcomputetensorutils::BuildArmComputeTensorInfo(output); + + arm_compute::NormalizationLayerInfo normalizationInfo = + armcomputetensorutils::BuildArmComputeNormalizationLayerInfo(descriptor); + + return arm_compute::NENormalizationLayer::validate(&aclInput, &aclOutput, normalizationInfo); +} + +NeonNormalizationFloatWorkload::NeonNormalizationFloatWorkload(const NormalizationQueueDescriptor& descriptor, + const WorkloadInfo& info, + std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) + : FloatWorkload<NormalizationQueueDescriptor>(descriptor, info) + , m_NormalizationLayer(memoryManager) +{ + m_Data.ValidateInputsOutputs("NeonNormalizationFloatWorkload", 1, 1); + std::string reasonIfUnsupported; + if (!IsNeonNormalizationDescParamsSupported(&reasonIfUnsupported, m_Data.m_Parameters)) + { + throw UnimplementedException(reasonIfUnsupported); + } + + // Input and output tensors have to have the same dimensionality. + if (info.m_InputTensorInfos[0].GetShape()[1] != info.m_OutputTensorInfos[0].GetShape()[1] + || info.m_InputTensorInfos[0].GetShape()[0] != info.m_OutputTensorInfos[0].GetShape()[0] + || info.m_InputTensorInfos[0].GetShape()[3] != info.m_OutputTensorInfos[0].GetShape()[3] + || info.m_InputTensorInfos[0].GetShape()[2] != info.m_OutputTensorInfos[0].GetShape()[2]) + { + throw InvalidArgumentException("Normalization requires input and output tensors to have equal dimensionality."); + } + + arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); + + const arm_compute::NormType normType = + ConvertNormalizationAlgorithmChannelToAclNormType(m_Data.m_Parameters.m_NormChannelType); + arm_compute::NormalizationLayerInfo normalizationInfo(normType, + m_Data.m_Parameters.m_NormSize, + m_Data.m_Parameters.m_Alpha, + m_Data.m_Parameters.m_Beta, + m_Data.m_Parameters.m_K, + false); + + m_NormalizationLayer.configure(&input, &output, normalizationInfo); +} + +void NeonNormalizationFloatWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonNormalizationFloatWorkload_Execute"); + m_NormalizationLayer.run(); +} + +} //namespace armnn |