| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonDepthwiseConvolutionFloatWorkload.hpp" |
| #include <backends/neon/NeonLayerSupport.hpp> |
| #include <backends/CpuTensorHandle.hpp> |
| #include <backends/aclCommon/ArmComputeTensorUtils.hpp> |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| NeonDepthwiseConvolutionFloatWorkload::NeonDepthwiseConvolutionFloatWorkload( |
| const DepthwiseConvolution2dQueueDescriptor& descriptor, |
| const WorkloadInfo& info) |
| : FloatWorkload<DepthwiseConvolution2dQueueDescriptor>(descriptor, info) |
| { |
| const TensorInfo& weightInfo = m_Data.m_Weight->GetTensorInfo(); |
| |
| m_KernelTensor = std::make_unique<arm_compute::Tensor>(); |
| BuildArmComputeTensor(*m_KernelTensor, weightInfo, 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); |
| |
| m_Data.ValidateInputsOutputs("NeonDepthwiseConvolutionFloatWorkload", 1, 1); |
| |
| arm_compute::ITensor& input = static_cast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = static_cast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| bool use3x3Optimisation = weightInfo.GetShape()[3] == 3 && weightInfo.GetShape()[2] == 3; |
| if (use3x3Optimisation) |
| { |
| m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer3x3>(); |
| static_cast<arm_compute::NEDepthwiseConvolutionLayer3x3*>( |
| m_pDepthwiseConvolutionLayer.get())->configure(&input, |
| m_KernelTensor.get(), |
| m_BiasTensor.get(), |
| &output, |
| padStrideInfo); |
| } |
| else |
| { |
| m_pDepthwiseConvolutionLayer = std::make_unique<arm_compute::NEDepthwiseConvolutionLayer>(); |
| static_cast<arm_compute::NEDepthwiseConvolutionLayer*>( |
| m_pDepthwiseConvolutionLayer.get())->configure(&input, |
| m_KernelTensor.get(), |
| m_BiasTensor.get(), |
| &output, |
| padStrideInfo); |
| } |
| |
| BOOST_ASSERT(m_pDepthwiseConvolutionLayer); |
| |
| InitializeArmComputeTensorDataForFloatTypes(*m_KernelTensor, m_Data.m_Weight); |
| |
| if (m_BiasTensor) |
| { |
| InitializeArmComputeTensorDataForFloatTypes(*m_BiasTensor, m_Data.m_Bias); |
| } |
| |
| m_pDepthwiseConvolutionLayer->prepare(); |
| FreeUnusedTensors(); |
| } |
| |
| void NeonDepthwiseConvolutionFloatWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonDepthwiseConvolutionFloatWorkload_Execute"); |
| BOOST_ASSERT(m_pDepthwiseConvolutionLayer); |
| |
| m_pDepthwiseConvolutionLayer->run(); |
| } |
| |
| void NeonDepthwiseConvolutionFloatWorkload::FreeUnusedTensors() |
| { |
| FreeTensorIfUnused(m_KernelTensor); |
| FreeTensorIfUnused(m_BiasTensor); |
| } |
| |
| } //namespace armnn |