// // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // #include "NeonPadWorkload.hpp" #include #include #include #include #include "NeonWorkloadUtils.hpp" namespace armnn { using namespace armcomputetensorutils; NeonPadWorkload::NeonPadWorkload(const PadQueueDescriptor& descriptor, const WorkloadInfo& info) : BaseWorkload(descriptor, info) { m_Data.ValidateInputsOutputs("NeonPadWorkload", 1, 1); arm_compute::ITensor& input = static_cast(m_Data.m_Inputs[0])->GetTensor(); arm_compute::ITensor& output = static_cast(m_Data.m_Outputs[0])->GetTensor(); std::vector> reversed_PadList(descriptor.m_Parameters.m_PadList.size()); std::reverse_copy(std::begin(descriptor.m_Parameters.m_PadList), std::end(descriptor.m_Parameters.m_PadList), std::begin(reversed_PadList)); arm_compute::PaddingList padList = static_cast(reversed_PadList); arm_compute::PixelValue pixelValue = GetPixelValue(input, descriptor.m_Parameters.m_PadValue); auto layer = std::make_unique(); layer->configure(&input, &output, padList, pixelValue); m_Layer.reset(layer.release()); } void NeonPadWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonPadWorkload_Execute"); m_Layer->run(); } arm_compute::Status NeonPadWorkloadValidate(const TensorInfo& input, const TensorInfo& output, const PadDescriptor& descriptor) { const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input); const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output); std::vector> reversed_PadList(descriptor.m_PadList.size()); std::reverse_copy(std::begin(descriptor.m_PadList), std::end(descriptor.m_PadList), std::begin(reversed_PadList)); arm_compute::PaddingList padList = static_cast(reversed_PadList); return arm_compute::NEPadLayer::validate(&aclInputInfo, &aclOutputInfo, padList); } } // namespace armnn