| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ClMeanWorkload.hpp" |
| |
| #include <cl/ClTensorHandle.hpp> |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| |
| #include "ClWorkloadUtils.hpp" |
| |
| namespace |
| { |
| |
| void ConvertArmnnAxesToAclCoordinates(size_t inputDimensions, |
| unsigned int originalInputRank, |
| const std::vector<unsigned int>& armnnAxes, |
| arm_compute::Coordinates& outAclCoords) |
| { |
| if (armnnAxes.empty()) |
| { |
| // If no reduction axes were provided, then the input must be reduced along all dimensions. |
| // Since arm_compute::CLReduceMean does not accept an empty vector as the reduction dimensions, we then |
| // manually create a vector including all the input dimensions (in reversed order) as: |
| // |
| // { inputDimensions - 1, inputDimensions - 2, ..., 1, 0 } |
| // |
| outAclCoords.set_num_dimensions(inputDimensions); |
| std::generate(outAclCoords.begin(), outAclCoords.end(), [d = inputDimensions - 1] () mutable { return d--; }); |
| } |
| else |
| { |
| // Create a vector of reduction dimensions (in reversed order) with the given reduction axes. |
| // |
| // Adjust the given reduction axes according to the original rank of the input tensor (before ACL applied any |
| // dimension correction). |
| // For example, if the input tensor originally had 4 dimensions, and one of the reduction axes was 2, then the |
| // new value for that reduction axis should be 1. |
| // |
| // Example: |
| // ArmNN input shape = { 1, 1, 3, 2 } -> ACL input shape = { 2, 3 } |
| // ArmNN reduction axis = { 2 } -> ACL reduction axis = { 1 } |
| // ArmNN reduction axis = { 3 } -> ACL reduction axis = { 0 } |
| // |
| // The transformation: ACL reduction axis index = original rank - ArmNN reduction axis index - 1 |
| // |
| outAclCoords.set_num_dimensions(armnnAxes.size()); |
| std::transform(armnnAxes.begin(), armnnAxes.end(), |
| outAclCoords.begin(), |
| [originalInputRank](unsigned int i){ return originalInputRank - i - 1; }); |
| } |
| } |
| |
| } // anonymous namespace |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| arm_compute::Status ClMeanValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const MeanDescriptor& desc) |
| { |
| const arm_compute::TensorInfo aclInputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutputInfo = armcomputetensorutils::BuildArmComputeTensorInfo(output); |
| |
| arm_compute::Coordinates coords; |
| ConvertArmnnAxesToAclCoordinates(aclInputInfo.num_dimensions(), |
| input.GetNumDimensions(), |
| desc.m_Axis, |
| coords); |
| |
| return arm_compute::CLReduceMean::validate(&aclInputInfo, coords, desc.m_KeepDims, &aclOutputInfo); |
| } |
| |
| ClMeanWorkload::ClMeanWorkload(const MeanQueueDescriptor& descriptor, const WorkloadInfo& info) |
| : BaseWorkload<MeanQueueDescriptor>(descriptor, info) |
| { |
| m_Data.ValidateInputsOutputs("ClMeanWorkload", 1, 1); |
| |
| arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| arm_compute::Coordinates coords; |
| ConvertArmnnAxesToAclCoordinates(input.info()->num_dimensions(), |
| info.m_InputTensorInfos[0].GetNumDimensions(), |
| m_Data.m_Parameters.m_Axis, |
| coords); |
| |
| m_Layer.configure(&input, coords, m_Data.m_Parameters.m_KeepDims, &output); |
| } |
| |
| void ClMeanWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_CL("ClMeanWorkload_Execute"); |
| m_Layer.run(); |
| } |
| |
| } //namespace armnn |