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//
// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
#include "NeonConvolution3dWorkload.hpp"
#include <aclCommon/ArmComputeTensorUtils.hpp>
#include <aclCommon/ArmComputeUtils.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>
#include <armnn/backends/TensorHandle.hpp>
#include <neon/workloads/NeonWorkloadUtils.hpp>
#include <arm_compute/runtime/NEON/functions/NEConv3D.h>
#include <armnn/Types.hpp>
#include <Half.hpp>
namespace armnn
{
using namespace armcomputetensorutils;
arm_compute::Status NeonConvolution3dWorkloadValidate(const TensorInfo& input,
const TensorInfo& output,
const Convolution3dDescriptor& descriptor,
const TensorInfo& weights,
const Optional<TensorInfo>& biases,
bool isFastMathEnabled,
const ActivationDescriptor* activationDescriptor)
{
const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout);
arm_compute::TensorInfo aclBiasesInfo;
arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr;
if (descriptor.m_BiasEnabled)
{
ARMNN_ASSERT(biases.has_value());
aclBiasesInfo = BuildArmComputeTensorInfo(biases.value(), descriptor.m_DataLayout);
optionalAclBiasesInfo = &aclBiasesInfo;
}
const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
const arm_compute::Conv3dInfo aclConv3DInfo = ComputeConv3DInfo(descriptor,
isFastMathEnabled,
activationDescriptor);
return arm_compute::NEConv3D::validate(&aclInputInfo,
&aclWeightsInfo,
optionalAclBiasesInfo,
&aclOutputInfo,
aclConv3DInfo);
}
NeonConvolution3dWorkload::NeonConvolution3dWorkload(const Convolution3dQueueDescriptor& descriptor,
const WorkloadInfo& info,
std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager,
const bool isFastMathEnabled)
: NeonBaseWorkload<Convolution3dQueueDescriptor>(descriptor, info)
{
IgnoreUnused(memoryManager);
using arm_compute::NEConv3D;
uint32_t numInputs = m_Data.m_Parameters.m_BiasEnabled ? 3: 2;
m_Data.ValidateInputsOutputs("NeonConvolution3dWorkload", numInputs, 1);
arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& weights = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[1])->GetTensor();
arm_compute::ITensor* biasesPtr = nullptr;
if (m_Data.m_Parameters.m_BiasEnabled)
{
biasesPtr = &PolymorphicDowncast<IAclTensorHandle *>(m_Data.m_Inputs[2])->GetTensor();
}
arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
input.info()->set_data_layout(aclDataLayout);
weights.info()->set_data_layout(aclDataLayout);
output.info()->set_data_layout(aclDataLayout);
const arm_compute::Conv3dInfo aclConv3DInfo = ComputeConv3DInfo(descriptor, isFastMathEnabled);
auto convolutionLayer = std::make_unique<arm_compute::NEConv3D>();
convolutionLayer->configure(&input,
&weights,
biasesPtr,
&output,
aclConv3DInfo);
// Add details for profiling output
WorkloadInfo detailsInfo;
detailsInfo.m_InputTensorInfos = info.m_InputTensorInfos;
detailsInfo.m_OutputTensorInfos = info.m_OutputTensorInfos;
// Report Profiling Details
ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonConvolution3dWorkload_Construct",
descriptor.m_Parameters,
detailsInfo,
this->GetGuid());
m_ConvolutionLayer.reset(convolutionLayer.release());
ARMNN_ASSERT(m_ConvolutionLayer);
m_ConvolutionLayer->prepare();
}
void NeonConvolution3dWorkload::Execute() const
{
ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonConvolution3dWorkload_Execute", this->GetGuid());
m_ConvolutionLayer->run();
}
} //namespace armnn
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