| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // See LICENSE file in the project root for full license information. |
| // |
| |
| #include "backends/CpuTensorHandle.hpp" |
| #include "backends/ArmComputeTensorUtils.hpp" |
| #include "backends/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 TensorInfo& biases) |
| { |
| const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output); |
| const arm_compute::TensorInfo aclWeightsInfo = BuildArmComputeTensorInfo(weights); |
| arm_compute::TensorInfo aclBiasesInfo; |
| arm_compute::TensorInfo *optionalAclBiasesInfo = nullptr; |
| |
| if (descriptor.m_BiasEnabled) |
| { |
| aclBiasesInfo = BuildArmComputeTensorInfo(biases); |
| 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()); |
| |
| if (m_Data.m_Parameters.m_BiasEnabled) |
| { |
| m_BiasTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_BiasTensor, m_Data.m_Bias->GetTensorInfo()); |
| } |
| |
| 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 |
| |