| // |
| // Copyright © 2020-2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "NeonSpaceToBatchNdWorkload.hpp" |
| |
| #include <armnn/utility/PolymorphicDowncast.hpp> |
| |
| namespace armnn |
| { |
| |
| using namespace armcomputetensorutils; |
| |
| arm_compute::Status NeonSpaceToBatchNdWorkloadValidate(const TensorInfo& input, |
| const TensorInfo& output, |
| const SpaceToBatchNdDescriptor& descriptor) |
| { |
| arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); |
| arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); |
| |
| arm_compute::Status statusSpaceToBatch = arm_compute::Status(arm_compute::ErrorCode::OK); |
| arm_compute::Status statusReshapeInput = arm_compute::Status(arm_compute::ErrorCode::OK); |
| arm_compute::Status statusReshapeOutput = arm_compute::Status(arm_compute::ErrorCode::OK); |
| |
| arm_compute::TensorInfo aclReshapeInputInfo = aclInputInfo; |
| arm_compute::TensorInfo aclReshapeOutputInfo = aclOutputInfo; |
| |
| // When a spacial dimension is missing (rank=3) set W to 1 |
| const unsigned int rank = input.GetNumDimensions(); |
| if (rank == 3) |
| { |
| const arm_compute::TensorShape inputShape = aclInputInfo.tensor_shape(); |
| const arm_compute::TensorShape outputShape = aclOutputInfo.tensor_shape(); |
| |
| if (descriptor.m_DataLayout == armnn::DataLayout::NHWC) |
| { |
| // In ACL dimensions are right to left: C, W, H, N |
| aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()}); |
| aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()}); |
| } |
| else if (descriptor.m_DataLayout == armnn::DataLayout::NCHW) |
| { |
| // In ACL dimensions are right to left: W, H, C, N |
| aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()}); |
| aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()}); |
| } |
| else |
| { |
| throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION()); |
| } |
| |
| statusReshapeInput = arm_compute::NEReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo); |
| statusReshapeOutput = arm_compute::NEReshapeLayer::validate(&aclReshapeOutputInfo, &aclOutputInfo); |
| } |
| |
| // ArmNN blockShape is [H, W] ACl asks for W, H |
| int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]); |
| int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); |
| |
| unsigned int padLeft = (rank == 3) ? 0 : descriptor.m_PadList[1].first; |
| unsigned int padRight = (rank == 3) ? 0 : descriptor.m_PadList[1].second; |
| arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(padLeft, |
| descriptor.m_PadList[0].first); |
| arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D(padRight, |
| descriptor.m_PadList[0].second); |
| |
| statusSpaceToBatch = arm_compute::NESpaceToBatchLayer::validate(rank == 3 ? &aclReshapeInputInfo : &aclInputInfo, |
| blockWidth, |
| blockHeight, |
| paddingLeftTop, |
| paddingRightBottom, |
| rank == 3 ? &aclReshapeOutputInfo : &aclOutputInfo); |
| |
| if (statusReshapeInput.error_code() == arm_compute::ErrorCode::OK && |
| statusReshapeOutput.error_code() == arm_compute::ErrorCode::OK && |
| statusSpaceToBatch.error_code() == arm_compute::ErrorCode::OK) |
| { |
| return arm_compute::Status(arm_compute::ErrorCode::OK, |
| "All SpaceToBatch layers validate status OK."); |
| } |
| else |
| { |
| return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, |
| "SpaceToBatch layer validate status failed." |
| + statusSpaceToBatch.error_description() |
| + statusReshapeInput.error_description() |
| + statusReshapeOutput.error_description()); |
| } |
| } |
| |
| NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueueDescriptor& descriptor, |
| const WorkloadInfo& info) |
| : NeonBaseWorkload<SpaceToBatchNdQueueDescriptor>(descriptor, info) |
| { |
| // Report Profiling Details |
| ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonSpaceToBatchNdWorkload_Construct", |
| descriptor.m_Parameters, |
| info, |
| this->GetGuid()); |
| |
| m_Data.ValidateInputsOutputs("NESpaceToBatchNdWorkload", 1, 1); |
| |
| arm_compute::ITensor& input = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Inputs[0])->GetTensor(); |
| arm_compute::ITensor& output = PolymorphicPointerDowncast<IAclTensorHandle>(m_Data.m_Outputs[0])->GetTensor(); |
| |
| arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout); |
| input.info()->set_data_layout(aclDataLayout); |
| output.info()->set_data_layout(aclDataLayout); |
| |
| arm_compute::TensorInfo aclReshapeInputInfo = BuildArmComputeTensorInfo(info.m_InputTensorInfos[0], |
| m_Data.m_Parameters.m_DataLayout); |
| arm_compute::TensorInfo aclReshapeOutputInfo = BuildArmComputeTensorInfo(info.m_OutputTensorInfos[0], |
| m_Data.m_Parameters.m_DataLayout); |
| |
| const unsigned int rank = info.m_InputTensorInfos[0].GetNumDimensions(); |
| if (rank == 3) |
| { |
| const arm_compute::TensorShape inputShape = input.info()->tensor_shape(); |
| const arm_compute::TensorShape outputShape = output.info()->tensor_shape(); |
| |
| // When a spacial dimension is missing set W to 1 |
| if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NHWC) |
| { |
| // In ACL dimensions are right to left: C, W, H, N |
| aclReshapeInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()}); |
| aclReshapeOutputInfo.set_tensor_shape({outputShape.x(), 1, outputShape.y(), outputShape.z()}); |
| } |
| else if (m_Data.m_Parameters.m_DataLayout == armnn::DataLayout::NCHW) |
| { |
| // In ACL dimensions are right to left: W, H, C, N |
| aclReshapeInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()}); |
| aclReshapeOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()}); |
| } |
| else |
| { |
| throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION()); |
| } |
| |
| m_ReshapeInputTensor.allocator()->init(aclReshapeInputInfo); |
| m_ReshapeOutputTensor.allocator()->init(aclReshapeOutputInfo); |
| |
| InitialiseArmComputeTensorEmpty(m_ReshapeInputTensor); |
| InitialiseArmComputeTensorEmpty(m_ReshapeOutputTensor); |
| |
| m_LayerReshapeInput.reset(new arm_compute::NEReshapeLayer()); |
| m_LayerReshapeOutput.reset(new arm_compute::NEReshapeLayer()); |
| |
| m_LayerReshapeInput->configure(&input, &m_ReshapeInputTensor); |
| m_LayerReshapeOutput->configure(&m_ReshapeOutputTensor, &output); |
| } |
| |
| // ArmNN blockShape is [H, W] ACl asks for W, H |
| int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]); |
| int32_t blockWidth = (rank == 3) ? 1: armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]); |
| |
| unsigned int padLeft = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].first; |
| unsigned int padRight = (rank == 3) ? 0 : descriptor.m_Parameters.m_PadList[1].second; |
| arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D(padLeft, |
| descriptor.m_Parameters.m_PadList[0].first); |
| arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D(padRight, |
| descriptor.m_Parameters.m_PadList[0].second); |
| |
| m_Layer.reset(new arm_compute::NESpaceToBatchLayer()); |
| m_Layer->configure((rank == 3) ? &m_ReshapeInputTensor : &input, |
| blockWidth, |
| blockHeight, |
| paddingLeftTop, |
| paddingRightBottom, |
| (rank == 3) ? &m_ReshapeOutputTensor : &output); |
| m_Layer->prepare(); |
| } |
| |
| void NeonSpaceToBatchNdWorkload::Execute() const |
| { |
| ARMNN_SCOPED_PROFILING_EVENT_NEON_NAME_GUID("NeonSpaceToBatchNdWorkload_Execute"); |
| if (m_LayerReshapeInput) |
| { |
| m_LayerReshapeInput->run(); |
| } |
| if (m_Layer) |
| { |
| m_Layer->run(); |
| } |
| if (m_LayerReshapeOutput) |
| { |
| m_LayerReshapeOutput->run(); |
| } |
| } |
| |
| } //namespace armnn |