src/backends/ClWorkloads/ClConvolution2dFloatWorkload.cpp

//
// Copyright © 2017 Arm Ltd. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "ClConvolution2dFloatWorkload.hpp"
#include <backends/ClTensorHandle.hpp>
#include <backends/CpuTensorHandle.hpp>
#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
#include <backends/ClLayerSupport.hpp>

#include "ClWorkloadUtils.hpp"

namespace armnn
{
using namespace armcomputetensorutils;

ClConvolution2dFloatWorkload::ClConvolution2dFloatWorkload(const Convolution2dQueueDescriptor& descriptor,
    const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager)
    : FloatWorkload<Convolution2dQueueDescriptor>(descriptor, info)
    , m_ConvolutionLayer(memoryManager)
{

    // todo: check tensor shapes match.
    const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo();

    m_KernelTensor = std::make_unique<arm_compute::CLTensor>();
    BuildArmComputeTensor(*m_KernelTensor, weightInfo, descriptor.m_DataLayout);

    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);

    if (m_Data.m_Parameters.m_BiasEnabled)
    {
        m_BiasTensor = std::make_unique<arm_compute::CLTensor>();
        BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), descriptor.m_DataLayout);
    }

    m_Data.ValidateInputsOutputs("ClConvolution2dFloat32Workload", 1, 1);

    arm_compute::ICLTensor& input  = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();

    m_ConvolutionLayer.configure(&input,
                                 m_KernelTensor.get(),
                                 m_BiasTensor.get(),
                                 &output,
                                 padStrideInfo);

    InitializeArmComputeClTensorData(*m_KernelTensor, m_Data.m_Weight);

    if (m_BiasTensor)
    {
        InitializeArmComputeClTensorData(*m_BiasTensor, m_Data.m_Bias);
    }

    // Force Compute Library to perform the necessary copying and reshaping, after which
    // delete all the input tensors that will no longer be needed
    m_ConvolutionLayer.prepare();
    FreeUnusedTensors();
}

void ClConvolution2dFloatWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_CL("ClConvolution2dFloat32Workload_Execute");

    m_ConvolutionLayer.run();
}

void ClConvolution2dFloatWorkload::FreeUnusedTensors()
{
    FreeTensorIfUnused(m_KernelTensor);
    FreeTensorIfUnused(m_BiasTensor);
}

} //namespace armnn