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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonBatchNormalizationFloatWorkload.hpp"
#include <backends/CpuTensorHandle.hpp>
#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
#include <armnn/ArmNN.hpp>
namespace armnn
{
using namespace armcomputetensorutils;
arm_compute::Status NeonBatchNormalizationValidate(const TensorInfo& input,
const TensorInfo& output,
const TensorInfo& mean,
const TensorInfo& var,
const TensorInfo& beta,
const TensorInfo& gamma,
const BatchNormalizationDescriptor& descriptor)
{
const DataLayout dataLayout = descriptor.m_DataLayout.GetDataLayout();
const arm_compute::TensorInfo aclInputInfo =
armcomputetensorutils::BuildArmComputeTensorInfo(input, dataLayout);
const arm_compute::TensorInfo aclOutputInfo =
armcomputetensorutils::BuildArmComputeTensorInfo(output, dataLayout);
const arm_compute::TensorInfo aclMeanInfo =
armcomputetensorutils::BuildArmComputeTensorInfo(mean, dataLayout);
const arm_compute::TensorInfo aclVarInfo =
armcomputetensorutils::BuildArmComputeTensorInfo(var, dataLayout);
const arm_compute::TensorInfo aclBetaInfo =
armcomputetensorutils::BuildArmComputeTensorInfo(beta, dataLayout);
const arm_compute::TensorInfo aclGammaInfo =
armcomputetensorutils::BuildArmComputeTensorInfo(gamma, dataLayout);
return arm_compute::NEBatchNormalizationLayer::validate(&aclInputInfo,
&aclOutputInfo,
&aclMeanInfo,
&aclVarInfo,
&aclBetaInfo,
&aclGammaInfo,
descriptor.m_Eps);
}
NeonBatchNormalizationFloatWorkload::NeonBatchNormalizationFloatWorkload(
const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info)
: FloatWorkload<BatchNormalizationQueueDescriptor>(descriptor, info)
{
m_Data.ValidateInputsOutputs("NeonBatchNormalizationFloatWorkload", 1, 1);
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();
arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout.GetDataLayout());
input.info()->set_data_layout(aclDataLayout);
output.info()->set_data_layout(aclDataLayout);
m_Mean = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_Mean, m_Data.m_Mean->GetTensorInfo());
m_Variance = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_Variance, m_Data.m_Variance->GetTensorInfo());
m_Gamma = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_Gamma, m_Data.m_Gamma->GetTensorInfo());
m_Beta = std::make_unique<arm_compute::Tensor>();
BuildArmComputeTensor(*m_Beta, m_Data.m_Beta->GetTensorInfo());
m_Layer.configure(&input,
&output,
m_Mean.get(),
m_Variance.get(),
m_Beta.get(),
m_Gamma.get(),
m_Data.m_Parameters.m_Eps);
InitializeArmComputeTensorData(*m_Mean, m_Data.m_Mean);
InitializeArmComputeTensorData(*m_Variance, m_Data.m_Variance);
InitializeArmComputeTensorData(*m_Gamma, m_Data.m_Gamma);
InitializeArmComputeTensorData(*m_Beta, m_Data.m_Beta);
// Force Compute Library to perform the necessary copying and reshaping, after which
// delete all the input tensors that will no longer be needed
m_Layer.prepare();
FreeUnusedTensors();
}
void NeonBatchNormalizationFloatWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonBatchNormalizationFloatWorkload_Execute");
m_Layer.run();
}
void NeonBatchNormalizationFloatWorkload::FreeUnusedTensors()
{
FreeTensorIfUnused(m_Mean);
FreeTensorIfUnused(m_Variance);
FreeTensorIfUnused(m_Gamma);
FreeTensorIfUnused(m_Beta);
}
} //namespace armnn
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