diff options
Diffstat (limited to 'src/backends/cl/workloads/ClConvolution2dWorkload.cpp')
-rw-r--r-- | src/backends/cl/workloads/ClConvolution2dWorkload.cpp | 119 |
1 files changed, 119 insertions, 0 deletions
diff --git a/src/backends/cl/workloads/ClConvolution2dWorkload.cpp b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp new file mode 100644 index 0000000000..521711becc --- /dev/null +++ b/src/backends/cl/workloads/ClConvolution2dWorkload.cpp @@ -0,0 +1,119 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ClConvolution2dWorkload.hpp" + +#include "ClWorkloadUtils.hpp" + +#include <backends/cl/ClLayerSupport.hpp> +#include <backends/cl/ClTensorHandle.hpp> +#include <backends/cl/ClLayerSupport.hpp> +#include <backends/aclCommon/ArmComputeUtils.hpp> +#include <backends/aclCommon/ArmComputeTensorUtils.hpp> +#include <backends/CpuTensorHandle.hpp> + +#include <arm_compute/runtime/CL/functions/CLConvolutionLayer.h> + +namespace armnn +{ +using namespace armcomputetensorutils; + +arm_compute::Status ClConvolution2dWorkloadValidate(const TensorInfo& input, + const TensorInfo& output, + const Convolution2dDescriptor& descriptor, + const TensorInfo& weights, + const boost::optional<TensorInfo>& biases) +{ + const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); + const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); + const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights, descriptor.m_DataLayout); + + arm_compute::TensorInfo aclBiasesInfo; + arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr; + + if (descriptor.m_BiasEnabled) + { + BOOST_ASSERT(biases.is_initialized()); + + aclBiasesInfo = BuildArmComputeTensorInfo(biases.get(), descriptor.m_DataLayout); + optionalAclBiasesInfo = &aclBiasesInfo; + } + + arm_compute::PadStrideInfo layerInfo = BuildArmComputePadStrideInfo(descriptor); + + return arm_compute::CLConvolutionLayer::validate(&aclInputInfo, + &aclWeightsInfo, + optionalAclBiasesInfo, + &aclOutputInfo, + layerInfo); +} + +ClConvolution2dWorkload::ClConvolution2dWorkload(const Convolution2dQueueDescriptor& descriptor, + const WorkloadInfo& info, std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) + : BaseWorkload<Convolution2dQueueDescriptor>(descriptor, info) + , m_ConvolutionLayer(memoryManager) +{ + // todo: check tensor shapes match. + const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo(); + + m_KernelTensor = std::make_unique<arm_compute::CLTensor>(); + BuildArmComputeTensor(*m_KernelTensor, weightInfo, m_Data.m_Parameters.m_DataLayout); + + arm_compute::PadStrideInfo padStrideInfo(m_Data.m_Parameters.m_StrideX, + m_Data.m_Parameters.m_StrideY, + m_Data.m_Parameters.m_PadLeft, + m_Data.m_Parameters.m_PadRight, + m_Data.m_Parameters.m_PadTop, + m_Data.m_Parameters.m_PadBottom, + arm_compute::DimensionRoundingType::FLOOR); + + if (m_Data.m_Parameters.m_BiasEnabled) + { + m_BiasTensor = std::make_unique<arm_compute::CLTensor>(); + BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), m_Data.m_Parameters.m_DataLayout); + } + + m_Data.ValidateInputsOutputs("ClConvolution2dWorkload", 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::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); + input.info()->set_data_layout(aclDataLayout); + output.info()->set_data_layout(aclDataLayout); + + m_ConvolutionLayer.configure(&input, + m_KernelTensor.get(), + m_BiasTensor.get(), + &output, + padStrideInfo); + + InitializeArmComputeClTensorData(*m_KernelTensor, m_Data.m_Weight); + + if (m_BiasTensor) + { + InitializeArmComputeClTensorData(*m_BiasTensor, m_Data.m_Bias); + } + + // Force Compute Library to perform the necessary copying and reshaping, after which + // delete all the input tensors that will no longer be needed + m_ConvolutionLayer.prepare(); + FreeUnusedTensors(); +} + +void ClConvolution2dWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT_CL("ClConvolution2dWorkload_Execute"); + + m_ConvolutionLayer.run(); +} + +void ClConvolution2dWorkload::FreeUnusedTensors() +{ + FreeTensorIfUnused(m_KernelTensor); + FreeTensorIfUnused(m_BiasTensor); +} + +} //namespace armnn |