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Diffstat (limited to 'src/backends/cl/workloads/ClMeanWorkload.cpp')
-rw-r--r-- | src/backends/cl/workloads/ClMeanWorkload.cpp | 100 |
1 files changed, 100 insertions, 0 deletions
diff --git a/src/backends/cl/workloads/ClMeanWorkload.cpp b/src/backends/cl/workloads/ClMeanWorkload.cpp new file mode 100644 index 0000000000..7e9649b1b6 --- /dev/null +++ b/src/backends/cl/workloads/ClMeanWorkload.cpp @@ -0,0 +1,100 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ClMeanWorkload.hpp" + +#include <backends/cl/ClTensorHandle.hpp> +#include <backends/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 |