diff options
Diffstat (limited to 'src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp')
-rw-r--r-- | src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp | 54 |
1 files changed, 54 insertions, 0 deletions
diff --git a/src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp b/src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp new file mode 100644 index 0000000000..739390d5a1 --- /dev/null +++ b/src/armnn/backends/NeonWorkloads/NeonNormalizationFloat32Workload.cpp @@ -0,0 +1,54 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// See LICENSE file in the project root for full license information. +// + +#include "NeonNormalizationFloat32Workload.hpp" +#include "backends/NeonLayerSupport.hpp" +#include "backends/ArmComputeUtils.hpp" + +namespace armnn +{ + +NeonNormalizationFloat32Workload::NeonNormalizationFloat32Workload(const NormalizationQueueDescriptor& descriptor, + const WorkloadInfo& info) + : Float32Workload<NormalizationQueueDescriptor>(descriptor, info) +{ + m_Data.ValidateInputsOutputs("NeonNormalizationFloat32Workload", 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 NeonNormalizationFloat32Workload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuAcc, "NeonNormalizationFloat32Workload_Execute"); + m_NormalizationLayer.run(); +} + +} //namespace armnn |