ClBatchToSpaceNdWorkload.cpp

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

#include "ClBatchToSpaceNdWorkload.hpp"

#include <cl/ClTensorHandle.hpp>
#include <backendsCommon/CpuTensorHandle.hpp>
#include <aclCommon/ArmComputeTensorUtils.hpp>

#include <armnn/utility/NumericCast.hpp>

#include "ClWorkloadUtils.hpp"

namespace armnn
{
using namespace armcomputetensorutils;

ClBatchToSpaceNdWorkload::ClBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& desc,
                                                   const WorkloadInfo& info)
                                                   : BaseWorkload<BatchToSpaceNdQueueDescriptor>(desc, info)
{
    m_Data.ValidateInputsOutputs("ClBatchToSpaceNdWorkload", 1, 1);

    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);

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

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

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

    m_Layer.configure(&input, blockWidth, blockHeight, &output);
}

void ClBatchToSpaceNdWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_CL("ClBatchToSpaceNdWorkload_Execute");
    RunClFunction(m_Layer, CHECK_LOCATION());
}

arm_compute::Status ClBatchToSpaceNdWorkloadValidate(const TensorInfo& input,
                                                     const TensorInfo& output,
                                                     const BatchToSpaceNdDescriptor& desc) {
    DataLayout dataLayout = desc.m_DataLayout;
    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, dataLayout);

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

    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, dataLayout);

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

} //namespace armnn