| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonStridedSliceWorkload.hpp" |
| |
| #include "NeonWorkloadUtils.hpp" |
| #include <neon/NeonTensorHandle.hpp> |
| #include <aclCommon/ArmComputeUtils.hpp> |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| #include <backendsCommon/WorkloadUtils.hpp> |
| |
| namespace armnn |
| { |
| |
| arm_compute::Status NeonStridedSliceWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const StridedSliceDescriptor& descriptor) |
| { |
| const arm_compute::TensorInfo aclInput = armcomputetensorutils::BuildArmComputeTensorInfo(input); |
| const arm_compute::TensorInfo aclOutput = armcomputetensorutils::BuildArmComputeTensorInfo(output); |
| |
| arm_compute::Coordinates starts; |
| arm_compute::Coordinates ends; |
| arm_compute::Coordinates strides; |
| |
| std::tie(starts, ends, strides) = SetNeonStridedSliceData(descriptor.m_Begin, |
| descriptor.m_End, |
| descriptor.m_Stride); |
| |
| auto numDimensions = boost::numeric_cast<int>(input.GetNumDimensions()); |
| int32_t begin_mask = ConvertMaskToACLFormat(descriptor.m_BeginMask, numDimensions); |
| int32_t end_mask = ConvertMaskToACLFormat(descriptor.m_EndMask, numDimensions); |
| int32_t shrink_axis_mask = ConvertMaskToACLFormat(descriptor.m_ShrinkAxisMask, numDimensions); |
| |
| return arm_compute::NEStridedSlice::validate(&aclInput, |
| &aclOutput, |
| starts, |
| ends, |
| strides, |
| begin_mask, |
| end_mask, |
| shrink_axis_mask); |
| } |
| |
| NeonStridedSliceWorkload::NeonStridedSliceWorkload(const StridedSliceQueueDescriptor& descriptor, |
| const WorkloadInfo& info) |
| : BaseWorkload<StridedSliceQueueDescriptor>(descriptor, info) |
| { |
| m_Data.ValidateInputsOutputs("NeonStridedSliceWorkload", 1, 1); |
| |
| arm_compute::ITensor& input = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = boost::polymorphic_downcast<IAclTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| arm_compute::Coordinates starts; |
| arm_compute::Coordinates ends; |
| arm_compute::Coordinates strides; |
| |
| std::tie(starts, ends, strides) = SetNeonStridedSliceData(m_Data.m_Parameters.m_Begin, |
| m_Data.m_Parameters.m_End, |
| m_Data.m_Parameters.m_Stride); |
| |
| auto numDimensions = boost::numeric_cast<int>(info.m_InputTensorInfos[0].GetNumDimensions()); |
| int32_t begin_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_BeginMask, numDimensions); |
| int32_t end_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_EndMask, numDimensions); |
| int32_t shrink_axis_mask = ConvertMaskToACLFormat(m_Data.m_Parameters.m_ShrinkAxisMask, numDimensions); |
| |
| arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); |
| input.info()->set_data_layout(aclDataLayout); |
| output.info()->set_data_layout(aclDataLayout); |
| |
| auto layer = std::make_unique<arm_compute::NEStridedSlice>(); |
| |
| layer->configure(&input, |
| &output, |
| starts, |
| ends, |
| strides, |
| begin_mask, |
| end_mask, |
| shrink_axis_mask); |
| m_Layer.reset(layer.release()); |
| } |
| |
| void NeonStridedSliceWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonStridedSliceWorkload_Execute"); |
| m_Layer->run(); |
| } |
| |
| } //namespace armnn |