diff options
Diffstat (limited to 'src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp')
| -rw-r--r-- | src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp | 172 |
1 files changed, 135 insertions, 37 deletions
diff --git a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp index e0adc6220e..291fa8110e 100644 --- a/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp +++ b/src/backends/neon/workloads/NeonSpaceToBatchNdWorkload.cpp @@ -5,13 +5,8 @@ #include "NeonSpaceToBatchNdWorkload.hpp" -#include "NeonWorkloadUtils.hpp" - -#include <armnn/utility/NumericCast.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> -#include <ResolveType.hpp> - namespace armnn { @@ -21,24 +16,77 @@ arm_compute::Status NeonSpaceToBatchNdWorkloadValidate(const TensorInfo& input, const TensorInfo& output, const SpaceToBatchNdDescriptor& descriptor) { - const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); - const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); + 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] Cl asks for W, H + // 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 = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); - - arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D( - descriptor.m_PadList[1].first, descriptor.m_PadList[0].first); - arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D( - descriptor.m_PadList[1].second, descriptor.m_PadList[0].second); - - return arm_compute::NESpaceToBatchLayer::validate(&aclInputInfo, - blockWidth, - blockHeight, - paddingLeftTop, - paddingRightBottom, - &aclOutputInfo); + 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, @@ -53,41 +101,91 @@ NeonSpaceToBatchNdWorkload::NeonSpaceToBatchNdWorkload(const SpaceToBatchNdQueue 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(); - - // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[0]); - int32_t blockWidth = armnn::numeric_cast<int32_t>(m_Data.m_Parameters.m_BlockShape[1]); - - arm_compute::Size2D paddingLeftTop = BuildArmComputeSize2D( - m_Data.m_Parameters.m_PadList[1].first, m_Data.m_Parameters.m_PadList[0].first); - arm_compute::Size2D paddingRightBottom = BuildArmComputeSize2D( - m_Data.m_Parameters.m_PadList[1].second, m_Data.m_Parameters.m_PadList[0].second); + 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(&input, + m_Layer->configure((rank == 3) ? &m_ReshapeInputTensor : &input, blockWidth, blockHeight, paddingLeftTop, paddingRightBottom, - &output); + (rank == 3) ? &m_ReshapeOutputTensor : &output); m_Layer->prepare(); } void NeonSpaceToBatchNdWorkload::Execute() const { + ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid()); + if (m_LayerReshapeInput) + { + m_LayerReshapeInput->run(); + } if (m_Layer) { - ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid()); m_Layer->run(); } + if (m_LayerReshapeOutput) + { + m_LayerReshapeOutput->run(); + } } } //namespace armnn
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