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Diffstat (limited to 'src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp')
-rw-r--r-- | src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp | 94 |
1 files changed, 94 insertions, 0 deletions
diff --git a/src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp b/src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp new file mode 100644 index 0000000000..1ec1417a58 --- /dev/null +++ b/src/backends/NeonWorkloads/NeonDepthwiseConvolutionFloatWorkload.cpp @@ -0,0 +1,94 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "NeonDepthwiseConvolutionFloatWorkload.hpp" +#include "backends/NeonLayerSupport.hpp" +#include "backends/CpuTensorHandle.hpp" +#include "backends/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); + + 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); + + 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 |