src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp

//
// Copyright © 2020, 2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "NeonBatchToSpaceNdWorkload.hpp"

#include "NeonWorkloadUtils.hpp"

#include <armnn/utility/NumericCast.hpp>
#include <armnn/utility/PolymorphicDowncast.hpp>

#include <ResolveType.hpp>

namespace armnn
{

using namespace armcomputetensorutils;

arm_compute::Status NeonBatchToSpaceNdWorkloadValidate(const TensorInfo& input,
                                                       const TensorInfo& output,
                                                       const BatchToSpaceNdDescriptor& descriptor)
{
    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);

    // ArmNN blockShape is [H, W] Cl asks for W, H
    int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]);
    int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);

    const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor);

    const arm_compute::Status aclStatus = arm_compute::NEBatchToSpaceLayer::validate(&aclInputInfo,
                                                                                     blockWidth,
                                                                                     blockHeight,
                                                                                     &aclOutputInfo,
                                                                                     cropInfo);
    return aclStatus;
}

NeonBatchToSpaceNdWorkload::NeonBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& descriptor,
                                                       const WorkloadInfo& info)
    : NeonBaseWorkload<BatchToSpaceNdQueueDescriptor>(descriptor, info)
{
    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonBatchToSpaceWorkload_Construct",
                                         descriptor.m_Parameters,
                                         info,
                                         this->GetGuid());

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

    arm_compute::ITensor& input  =
            armnn::PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ITensor& output =
            armnn::PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor();

    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
    input.info()->set_data_layout(aclDataLayout);
    output.info()->set_data_layout(aclDataLayout);

    // ArmNN blockShape is [H, W] Cl asks for W, H
    int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[0]);
    int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);

    const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor.m_Parameters);

    m_Layer.reset(new arm_compute::NEBatchToSpaceLayer());
    m_Layer->configure(&input, blockWidth, blockHeight, &output, cropInfo);
    m_Layer->prepare();
}

void NeonBatchToSpaceNdWorkload::Execute() const
{
    if (m_Layer)
    {
        ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid());
        m_Layer->run();
    }
}

} //namespace armnn