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//
// 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"
#include "backends/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);
}
NeonNormalizationFloat32Workload::NeonNormalizationFloat32Workload(const NormalizationQueueDescriptor& descriptor,
const WorkloadInfo& info,
std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
: FloatWorkload<NormalizationQueueDescriptor>(descriptor, info)
, m_NormalizationLayer(memoryManager)
{
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_NEON("NeonNormalizationFloat32Workload_Execute");
m_NormalizationLayer.run();
}
} //namespace armnn
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