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//
// Copyright © 2017 Arm Ltd. All rights reserved.
// See LICENSE file in the project root for full license information.
//
#include "backends/CpuTensorHandle.hpp"
#include "backends/ArmComputeTensorUtils.hpp"
#include "backends/NeonLayerSupport.hpp"
#include "NeonConvolution2dBaseWorkload.hpp"
namespace armnn
{
template<armnn::DataType dataType>
NeonConvolution2dBaseWorkload<dataType>::NeonConvolution2dBaseWorkload(const Convolution2dQueueDescriptor& descriptor,
const WorkloadInfo& info)
: TypedWorkload<Convolution2dQueueDescriptor, dataType>(descriptor, info)
{
using arm_compute::NEDirectConvolutionLayer;
using namespace armcomputetensorutils;
ValidateData();
// todo: check tensor shapes match
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();
BuildArmComputeTensor(m_KernelTensor, m_Data.m_Weight->GetTensorInfo());
arm_compute::Tensor* optionalBiasTensor = nullptr;
if (m_Data.m_Parameters.m_BiasEnabled)
{
BuildArmComputeTensor(m_BiasTensor, m_Data.m_Bias->GetTensorInfo());
optionalBiasTensor = &m_BiasTensor;
}
arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX,
m_Data.m_Parameters.m_StrideY,
m_Data.m_Parameters.m_PadLeft,
m_Data.m_Parameters.m_PadRight,
m_Data.m_Parameters.m_PadTop,
m_Data.m_Parameters.m_PadBottom,
arm_compute::DimensionRoundingType::FLOOR);
const bool preferDirectConvolution =
IsNeonDirectConvolutionPreferred(m_Data.m_Weight->GetTensorInfo(),
m_Data.m_Parameters);
if (preferDirectConvolution)
{
auto directConvolutionLayer = std::make_unique<arm_compute::NEDirectConvolutionLayer>();
directConvolutionLayer->configure(&input,
&m_KernelTensor,
optionalBiasTensor,
&output,
padStrideInfo);
m_ConvolutionLayer.reset(directConvolutionLayer.release());
}
else
{
auto convolutionLayer = std::make_unique<arm_compute::NEConvolutionLayer>();
convolutionLayer->configure(&input,
&m_KernelTensor,
optionalBiasTensor,
&output,
padStrideInfo);
m_ConvolutionLayer.reset(convolutionLayer.release());
}
BOOST_ASSERT(m_ConvolutionLayer);
using Type = ResolveType<dataType>;
InitialiseArmComputeTensorData(m_KernelTensor, m_Data.m_Weight->template GetConstTensor<Type>());
}
// Generate known implementations for linker
template class NeonConvolution2dBaseWorkload<DataType::Float32>;
template class NeonConvolution2dBaseWorkload<DataType::QuantisedAsymm8>;
} //namespace armnn
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