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//
// 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 <armnn/utility/NumericCast.hpp>
#include <armnn/utility/PolymorphicDowncast.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 = armnn::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)
{
// Report Profiling Details
ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonStridedSliceWorkload_Construct",
descriptor.m_Parameters,
info,
this->GetGuid());
m_Data.ValidateInputsOutputs("NeonStridedSliceWorkload", 1, 1);
arm_compute::ITensor& input = PolymorphicDowncast<IAclTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
arm_compute::ITensor& output = PolymorphicDowncast<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 = armnn::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_GUID("NeonStridedSliceWorkload_Execute", this->GetGuid());
m_Layer->run();
}
} //namespace armnn
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