| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "ClBatchToSpaceNdWorkload.hpp" |
| |
| #include <cl/ClTensorHandle.hpp> |
| #include <backendsCommon/TensorHandle.hpp> |
| #include <aclCommon/ArmComputeTensorUtils.hpp> |
| |
| #include <armnn/utility/NumericCast.hpp> |
| |
| #include "ClWorkloadUtils.hpp" |
| |
| namespace armnn |
| { |
| using namespace armcomputetensorutils; |
| |
| ClBatchToSpaceNdWorkload::ClBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& desc, |
| const WorkloadInfo& info, |
| const arm_compute::CLCompileContext& clCompileContext) |
| : BaseWorkload<BatchToSpaceNdQueueDescriptor>(desc, info) |
| { |
| m_Data.ValidateInputsOutputs("ClBatchToSpaceNdWorkload", 1, 1); |
| |
| arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); |
| |
| arm_compute::ICLTensor& input = static_cast<IClTensorHandle*>(m_Data.m_Inputs[0])->GetTensor(); |
| input.info()->set_data_layout(aclDataLayout); |
| |
| // ArmNN blockShape is [H, W] Cl asks for W, H |
| int32_t blockHeight = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[0]); |
| int32_t blockWidth = armnn::numeric_cast<int32_t>(desc.m_Parameters.m_BlockShape[1]); |
| |
| arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); |
| output.info()->set_data_layout(aclDataLayout); |
| |
| m_Layer.configure(clCompileContext, &input, blockWidth, blockHeight, &output); |
| } |
| |
| void ClBatchToSpaceNdWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_CL("ClBatchToSpaceNdWorkload_Execute"); |
| RunClFunction(m_Layer, CHECK_LOCATION()); |
| } |
| |
| arm_compute::Status ClBatchToSpaceNdWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const BatchToSpaceNdDescriptor& desc) { |
| DataLayout dataLayout = desc.m_DataLayout; |
| const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, dataLayout); |
| |
| // ArmNN blockShape is [H, W] Cl asks for W, H |
| int32_t blockHeight = armnn::numeric_cast<int32_t>(desc.m_BlockShape[0]); |
| int32_t blockWidth = armnn::numeric_cast<int32_t>(desc.m_BlockShape[1]); |
| |
| const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, dataLayout); |
| |
| const arm_compute::Status aclStatus = arm_compute::CLBatchToSpaceLayer::validate(&aclInputInfo, |
| blockWidth, |
| blockHeight, |
| &aclOutputInfo); |
| return aclStatus; |
| } |
| |
| } //namespace armnn |