| // |
| // Copyright © 2019 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonArgMinMaxWorkload.hpp" |
| #include "NeonWorkloadUtils.hpp" |
| |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| |
| #include <armnn/backends/TensorHandle.hpp> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| #include <armnnUtils/TensorUtils.hpp> |
| |
| #include <arm_compute/runtime/NEON/functions/NEArgMinMaxLayer.h> |
| |
| namespace |
| { |
| unsigned int CalcAclAxis(unsigned int numDimensions, unsigned int axisIndex) |
| { |
| return (numDimensions - axisIndex) - 1; |
| } |
| |
| } //namespace |
| |
| namespace armnn |
| { |
| |
| arm_compute::Status NeonArgMinMaxWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const ArgMinMaxDescriptor& descriptor) |
| { |
| const arm_compute::TensorInfo aclInput = armcomputetensorutils::BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutput = armcomputetensorutils::BuildArmComputeTensorInfo(output); |
| |
| auto numDims = input.GetNumDimensions(); |
| auto unsignedAxis = armnnUtils::GetUnsignedAxis(numDims, descriptor.m_Axis); |
| int aclAxis = armnn::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); |
| |
| if (descriptor.m_Function == ArgMinMaxFunction::Max) |
| { |
| return arm_compute::NEArgMinMaxLayer::validate(&aclInput, aclAxis, &aclOutput, |
| arm_compute::ReductionOperation::ARG_IDX_MAX); |
| } |
| else |
| { |
| return arm_compute::NEArgMinMaxLayer::validate(&aclInput, aclAxis, &aclOutput, |
| arm_compute::ReductionOperation::ARG_IDX_MIN); |
| } |
| } |
| |
| |
| NeonArgMinMaxWorkload::NeonArgMinMaxWorkload(const ArgMinMaxQueueDescriptor& descriptor, |
| const WorkloadInfo& info) |
| : NeonBaseWorkload<ArgMinMaxQueueDescriptor>(descriptor, info) |
| { |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonArgMinMaxWorkload_Construct", |
| descriptor.m_Parameters, |
| info, |
| this->GetGuid()); |
| |
| arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| auto numDims = info.m_InputTensorInfos[0].GetNumDimensions(); |
| auto unsignedAxis = armnnUtils::GetUnsignedAxis(numDims, m_Data.m_Parameters.m_Axis); |
| int aclAxis = armnn::numeric_cast<int>(CalcAclAxis(numDims, unsignedAxis)); |
| |
| auto layer = std::make_unique<arm_compute::NEArgMinMaxLayer>(); |
| |
| if (m_Data.m_Parameters.m_Function == ArgMinMaxFunction::Max) |
| { |
| layer->configure(&input, aclAxis, &output, arm_compute::ReductionOperation::ARG_IDX_MAX); |
| } |
| else |
| { |
| layer->configure(&input, aclAxis, &output, arm_compute::ReductionOperation::ARG_IDX_MIN); |
| } |
| |
| m_ArgMinMaxLayer.reset(layer.release()); |
| } |
| |
| void NeonArgMinMaxWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonArgMinMaxWorkload_Execute", this->GetGuid()); |
| m_ArgMinMaxLayer->run(); |
| } |
| |
| } //namespace armnn |
| |