diff options
author | David Beck <david.beck@arm.com> | 2018-09-24 15:59:27 +0100 |
---|---|---|
committer | Matthew Bentham <matthew.bentham@arm.com> | 2018-10-10 16:16:57 +0100 |
commit | 0dbe0ee25312b728d77383d11c465156e64ae757 (patch) | |
tree | af37a9802e3ad551e1bf63f7636508cde7a41643 /src/backends/neon/workloads/NeonConvolution2dBaseWorkload.cpp | |
parent | b4540bef0b0327683fe8e63f727c1212800dc2a9 (diff) | |
download | armnn-0dbe0ee25312b728d77383d11c465156e64ae757.tar.gz |
IVGCVSW-1899 : Neon backend folder structure
armnn:149855
Change-Id: I26e8cf83422a65049386a5ebdb6d0001627aefaa
Diffstat (limited to 'src/backends/neon/workloads/NeonConvolution2dBaseWorkload.cpp')
-rw-r--r-- | src/backends/neon/workloads/NeonConvolution2dBaseWorkload.cpp | 146 |
1 files changed, 146 insertions, 0 deletions
diff --git a/src/backends/neon/workloads/NeonConvolution2dBaseWorkload.cpp b/src/backends/neon/workloads/NeonConvolution2dBaseWorkload.cpp new file mode 100644 index 0000000000..547f563d59 --- /dev/null +++ b/src/backends/neon/workloads/NeonConvolution2dBaseWorkload.cpp @@ -0,0 +1,146 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include <backends/CpuTensorHandle.hpp> +#include <backends/aclCommon/ArmComputeTensorUtils.hpp> +#include <backends/neon/NeonLayerSupport.hpp> + +#include "NeonConvolution2dBaseWorkload.hpp" + +#include <armnn/Types.hpp> +#include <Half.hpp> + +namespace armnn +{ + +using namespace armcomputetensorutils; + +arm_compute::Status NeonConvolution2dWorkloadValidate(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::NEConvolutionLayer::validate(&aclInputInfo, + &aclWeightsInfo, + optionalAclBiasesInfo, + &aclOutputInfo, + layerInfo); +} + +template<armnn::DataType... dataTypes> +NeonConvolution2dBaseWorkload<dataTypes...>::NeonConvolution2dBaseWorkload( + const Convolution2dQueueDescriptor& descriptor, const WorkloadInfo& info, + std::shared_ptr<arm_compute::MemoryManagerOnDemand>& memoryManager) + : TypedWorkload<Convolution2dQueueDescriptor, dataTypes...>(descriptor, info) +{ + using arm_compute::NEDirectConvolutionLayer; + + ValidateData(); + + // todo: check tensor shapes match. + + arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); + arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); + + m_KernelTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_KernelTensor, m_Data.m_Weight->GetTensorInfo(), descriptor.m_DataLayout); + + if (m_Data.m_Parameters.m_BiasEnabled) + { + m_BiasTensor = std::make_unique<arm_compute::Tensor>(); + BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo(), descriptor.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); + + const bool preferDirectConvolution = + IsNeonDirectConvolutionPreferred(m_Data.m_Weight->GetTensorInfo(), + m_Data.m_Parameters); + + if (preferDirectConvolution) + { + auto directConvolutionLayer = std::make_unique<arm_compute::NEDirectConvolutionLayer>(memoryManager); + directConvolutionLayer->configure(&input, + m_KernelTensor.get(), + m_BiasTensor.get(), + &output, + padStrideInfo); + m_ConvolutionLayer.reset(directConvolutionLayer.release()); + } + else + { + auto convolutionLayer = std::make_unique<arm_compute::NEConvolutionLayer>(memoryManager); + convolutionLayer->configure(&input, + m_KernelTensor.get(), + m_BiasTensor.get(), + &output, + padStrideInfo); + m_ConvolutionLayer.reset(convolutionLayer.release()); + } + BOOST_ASSERT(m_ConvolutionLayer); + + armnn::DataType dataType = m_Data.m_Weight->GetTensorInfo().GetDataType(); + + switch (dataType) + { + case DataType::Float16: + { + InitialiseArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight->template GetConstTensor<Half>()); + break; + } + case DataType::Float32: + { + InitialiseArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight->template GetConstTensor<float>()); + break; + } + case DataType::QuantisedAsymm8: + { + InitialiseArmComputeTensorData(*m_KernelTensor, m_Data.m_Weight->template GetConstTensor<uint8_t>()); + break; + } + default: + { + BOOST_ASSERT_MSG(false, "Unknown DataType."); + } + } +} + +template<armnn::DataType... dataTypes> +void NeonConvolution2dBaseWorkload<dataTypes...>::FreeUnusedTensors() +{ + FreeTensorIfUnused(m_KernelTensor); + FreeTensorIfUnused(m_BiasTensor); +} + +// Generates known implementations for linker. +template class NeonConvolution2dBaseWorkload<armnn::DataType::Float16, armnn::DataType::Float32>; +template class NeonConvolution2dBaseWorkload<armnn::DataType::QuantisedAsymm8>; + +} //namespace armnn + |