ClSpaceToBatchNdWorkload.cpp

Go to the documentation of this file.

//
// Copyright © 2017-2023 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//
 
#include "ClSpaceToBatchNdWorkload.hpp"
 
#include <armnn/utility/PolymorphicDowncast.hpp>
 
#include <cl/ClTensorHandle.hpp>
 
namespace armnn
{
using namespace armcomputetensorutils;
 
arm_compute::Status ClSpaceToBatchNdWorkloadValidate(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
            aclInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()});
            aclOutputInfo.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
            aclInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()});
            aclOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()});
        }
        else
        {
            throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION());
        }
 
        statusReshapeInput = arm_compute::CLReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo);
        statusReshapeOutput = arm_compute::CLReshapeLayer::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);
 
    const arm_compute::Status aclStatus = arm_compute::CLSpaceToBatchLayer::validate(&aclInputInfo,
                                                                                     blockWidth,
                                                                                     blockHeight,
                                                                                     paddingLeftTop,
                                                                                     paddingRightBottom,
                                                                                     &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());
    }
}
 
ClSpaceToBatchNdWorkload::ClSpaceToBatchNdWorkload(const SpaceToBatchNdQueueDescriptor& descriptor,
                                                   const WorkloadInfo& info,
                                                   const arm_compute::CLCompileContext& clCompileContext)
    : ClBaseWorkload<SpaceToBatchNdQueueDescriptor>(descriptor, info)
{
    // Report Profiling Details
    ARMNN_REPORT_PROFILING_WORKLOAD_DESC("ClSpaceToBatchNdWorkload_Construct",
                                         descriptor.m_Parameters,
                                         info,
                                         this->GetGuid());
 
    m_Data.ValidateInputsOutputs("ClSpaceToBatchNdWorkload", 1, 1);
 
    arm_compute::ICLTensor& input  = PolymorphicPointerDowncast<IClTensorHandle>(m_Data.m_Inputs[0])->GetTensor();
    arm_compute::ICLTensor& output = PolymorphicPointerDowncast<IClTensorHandle>(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::CLReshapeLayer());
        m_LayerReshapeOutput.reset(new arm_compute::CLReshapeLayer());
 
        m_LayerReshapeInput->configure(clCompileContext, &input, &m_ReshapeInputTensor);
        m_LayerReshapeOutput->configure(clCompileContext, &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);
 
    {
        ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID("ClSpaceToBatchNdWorkload_configure");
        m_Layer.configure(clCompileContext,
                          rank == 3 ? &m_ReshapeInputTensor : &input,
                          blockWidth,
                          blockHeight,
                          paddingLeftTop,
                          paddingRightBottom,
                          rank == 3 ? &m_ReshapeOutputTensor : &output);
    }
}
 
void ClSpaceToBatchNdWorkload::Execute() const
{
    ARMNN_SCOPED_PROFILING_EVENT_CL_NAME_GUID("ClSpaceToBatchNdWorkload_Execute");
    if (m_LayerReshapeInput)
    {
        m_LayerReshapeInput->run();
    }
    RunClFunction(m_Layer, CHECK_LOCATION());
    if (m_LayerReshapeOutput)
    {
        m_LayerReshapeOutput->run();
    }
}
 
} //namespace armnn