NeonSpaceToBatchNdWorkload.cpp

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//
// Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "NeonSpaceToBatchNdWorkload.hpp"
 
#include <armnn/utility/PolymorphicDowncast.hpp>
 
namespace armnn
{
 
using namespace armcomputetensorutils;
 
arm_compute::Status NeonSpaceToBatchNdWorkloadValidate(const TensorInfo& input,
                                                       const TensorInfo& output,
                                                       const SpaceToBatchNdDescriptor& descriptor)
{
    arm_compute::TensorInfo aclInputInfo  = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
    arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
 
    arm_compute::Status statusSpaceToBatch  = arm_compute::Status(arm_compute::ErrorCode::OK);
    arm_compute::Status statusReshapeInput  = arm_compute::Status(arm_compute::ErrorCode::OK);
    arm_compute::Status statusReshapeOutput = arm_compute::Status(arm_compute::ErrorCode::OK);
 
    arm_compute::TensorInfo aclReshapeInputInfo  = aclInputInfo;
    arm_compute::TensorInfo aclReshapeOutputInfo = aclOutputInfo;
 
    // When a spacial dimension is missing (rank=3) set W to 1
    const unsigned int rank = input.GetNumDimensions();
    if (rank == 3)
    {
        const arm_compute::TensorShape inputShape  = aclInputInfo.tensor_shape();
        const arm_compute::TensorShape outputShape = aclOutputInfo.tensor_shape();
 
        if (descriptor.m_DataLayout == armnn::DataLayout::NHWC)
        {
            // In ACL dimensions are right to left: C, W, H, N
            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
        }
        else if (descriptor.m_DataLayout == armnn::DataLayout::NCHW)
        {
            // In ACL dimensions are right to left: W, H, C, N
            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
        }
        else
        {
            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
        }
 
        statusReshapeInput = arm_compute::NEReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo);
        statusReshapeOutput = arm_compute::NEReshapeLayer::validate(&aclReshapeOutputInfo, &aclOutputInfo);
    }
 
    // ArmNN blockShape is [H, W] ACl asks for W, H
    int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]);
    int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]);
 
    unsigned int padLeft  = (rank == 3) ? 0 : descriptor.m_PadList[1].first;
    unsigned int padRight = (rank == 3) ? 0 : descriptor.m_PadList[1].second;
    arm_compute::Size2D paddingLeftTop      = BuildArmComputeSize2D(padLeft,
                                                                    descriptor.m_PadList[0].first);
    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(padRight,
                                                                    descriptor.m_PadList[0].second);
 
    statusSpaceToBatch = arm_compute::NESpaceToBatchLayer::validate(rank == 3 ? &aclReshapeInputInfo : &aclInputInfo,
                                                                    blockWidth,
                                                                    blockHeight,
                                                                    paddingLeftTop,
                                                                    paddingRightBottom,
                                                                    rank == 3 ? &aclReshapeOutputInfo : &aclOutputInfo);
 
    if (statusReshapeInput.error_code()  == arm_compute::ErrorCode::OK &&
        statusReshapeOutput.error_code() == arm_compute::ErrorCode::OK &&
        statusSpaceToBatch.error_code()  == arm_compute::ErrorCode::OK)
    {
        return arm_compute::Status(arm_compute::ErrorCode::OK,
                                   "All SpaceToBatch layers validate status OK.");
    }
    else
    {
        return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR,
                                   "SpaceToBatch layer validate status failed."
                                   + statusSpaceToBatch.error_description()
                                   + statusReshapeInput.error_description()
                                   + statusReshapeOutput.error_description());
    }
}
 
NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueueDescriptor& descriptor,
                                                       const WorkloadInfo& info)
        : NeonBaseWorkload<SpaceToBatchNdQueueDescriptor>(descriptor, info)
{
    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonSpaceToBatchNdWorkload_Construct",
                                         descriptor.m_Parameters,
                                         info,
                                         this->GetGuid());
 
    m_Data.ValidateInputsOutputs("NESpaceToBatchNdWorkload", 1, 1);
 
    arm_compute::ITensor& input  = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ITensor& output = 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);
 
    arm_compute::TensorInfo aclReshapeInputInfo = BuildArmComputeTensorInfo(info.m_InputTensorInfos[0],
                                                                            m_Data.m_Parameters.m_DataLayout);
    arm_compute::TensorInfo aclReshapeOutputInfo = BuildArmComputeTensorInfo(info.m_OutputTensorInfos[0],
                                                                             m_Data.m_Parameters.m_DataLayout);
 
    const unsigned int rank = info.m_InputTensorInfos[0].GetNumDimensions();
    if (rank == 3)
    {
        const arm_compute::TensorShape inputShape  = input.info()->tensor_shape();
        const arm_compute::TensorShape outputShape = output.info()->tensor_shape();
 
        // When a spacial dimension is missing set W to 1
        if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NHWC)
        {
            // In ACL dimensions are right to left: C, W, H, N
            aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
            aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()});
        }
        else if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NCHW)
        {
            // In ACL dimensions are right to left: W, H, C, N
            aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
            aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
        }
        else
        {
            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
        }
 
        m_ReshapeInputTensor.allocator()->init(aclReshapeInputInfo);
        m_ReshapeOutputTensor.allocator()->init(aclReshapeOutputInfo);
 
        InitialiseArmComputeTensorEmpty(m_ReshapeInputTensor);
        InitialiseArmComputeTensorEmpty(m_ReshapeOutputTensor);
 
        m_LayerReshapeInput.reset(new arm_compute::NEReshapeLayer());
        m_LayerReshapeOutput.reset(new arm_compute::NEReshapeLayer());
 
        m_LayerReshapeInput->configure(&input, &m_ReshapeInputTensor);
        m_LayerReshapeOutput->configure(&m_ReshapeOutputTensor, &output);
    }
 
    // ArmNN blockShape is [H, W] ACl asks for W, H
    int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]);
    int32_t blockWidth = (rank == 3) ? 1: armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]);
 
    unsigned int padLeft  = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].first;
    unsigned int padRight = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].second;
    arm_compute::Size2D paddingLeftTop      = BuildArmComputeSize2D(padLeft,
                                                                    descriptor.m_Parameters.m_PadList[0].first);
    arm_compute::Size2D paddingRightBottom  = BuildArmComputeSize2D(padRight,
                                                                    descriptor.m_Parameters.m_PadList[0].second);
 
    m_Layer.reset(new arm_compute::NESpaceToBatchLayer());
    m_Layer->configure((rank == 3) ? &m_ReshapeInputTensor : &input,
                       blockWidth,
                       blockHeight,
                       paddingLeftTop,
                       paddingRightBottom,
                       (rank == 3) ? &m_ReshapeOutputTensor : &output);
    m_Layer->prepare();
}
 
void NeonSpaceToBatchNdWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonSpaceToBatchNdWorkload_Execute");
    if (m_LayerReshapeInput)
    {
        m_LayerReshapeInput->run();
    }
    if (m_Layer)
    {
        m_Layer->run();
    }
    if (m_LayerReshapeOutput)
    {
        m_LayerReshapeOutput->run();
    }
}
 
} //namespace armnn