diff options
Diffstat (limited to 'src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp')
| -rw-r--r-- | src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp | 153 |
1 files changed, 126 insertions, 27 deletions
diff --git a/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp b/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp index ad3a602f48..f6d96041cc 100644 --- a/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp +++ b/src/backends/cl/workloads/ClBatchToSpaceNdWorkload.cpp @@ -1,43 +1,88 @@ // -// Copyright © 2017, 2023 Arm Ltd and Contributors. All rights reserved. +// Copyright © 2017, 2019-2023 Arm Ltd and Contributors. All rights reserved. // SPDX-License-Identifier: MIT // #include "ClBatchToSpaceNdWorkload.hpp" -#include <cl/ClTensorHandle.hpp> -#include <armnn/backends/TensorHandle.hpp> -#include <aclCommon/ArmComputeTensorUtils.hpp> - -#include <armnn/utility/NumericCast.hpp> +#include <armnn/utility/PolymorphicDowncast.hpp> -#include "ClWorkloadUtils.hpp" +#include <cl/ClTensorHandle.hpp> namespace armnn { + using namespace armcomputetensorutils; arm_compute::Status ClBatchToSpaceNdWorkloadValidate(const TensorInfo& input, const TensorInfo& output, const BatchToSpaceNdDescriptor& descriptor) { - DataLayout dataLayout = descriptor.m_DataLayout; - const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, dataLayout); + arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); + arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_DataLayout); - // ArmNN blockShape is [H, W] Cl asks for W, H + arm_compute::Status statusBatchToSpace = 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 + aclInputInfo.set_tensor_shape({inputShape.x(), 1, inputShape.y(), inputShape.z()}); + aclOutputInfo.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 + aclInputInfo.set_tensor_shape({1, inputShape.x(), inputShape.y(), inputShape.z()}); + aclOutputInfo.set_tensor_shape({1, outputShape.x(), outputShape.y(), outputShape.z()}); + } + else + { + throw InvalidArgumentException("Unsupported or unknown DataLayout", CHECK_LOCATION()); + } + + statusReshapeInput = arm_compute::CLReshapeLayer::validate(&aclInputInfo, &aclReshapeInputInfo); + statusReshapeOutput = arm_compute::CLReshapeLayer::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 = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); + int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); - const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, dataLayout); + const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor, rank); - const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor); + statusBatchToSpace = arm_compute::CLBatchToSpaceLayer::validate(rank == 3 ? &aclReshapeInputInfo : &aclInputInfo, + blockWidth, + blockHeight, + rank == 3 ? &aclReshapeOutputInfo : &aclOutputInfo, + cropInfo); - const arm_compute::Status aclStatus = arm_compute::CLBatchToSpaceLayer::validate(&aclInputInfo, - blockWidth, - blockHeight, - &aclOutputInfo, - cropInfo); - return aclStatus; + if (statusReshapeInput.error_code() == arm_compute::ErrorCode::OK && + statusReshapeOutput.error_code() == arm_compute::ErrorCode::OK && + statusBatchToSpace.error_code() == arm_compute::ErrorCode::OK) + { + return arm_compute::Status(arm_compute::ErrorCode::OK, + "All BatchToSpace layers validate status OK."); + } + else + { + return arm_compute::Status(arm_compute::ErrorCode::RUNTIME_ERROR, + "BatchToSpace layer validate status failed." + + statusBatchToSpace.error_description() + + statusReshapeInput.error_description() + + statusReshapeOutput.error_description()); + } } ClBatchToSpaceNdWorkload::ClBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& descriptor, @@ -53,30 +98,84 @@ ClBatchToSpaceNdWorkload::ClBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDesc 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(); + arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(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); - // ArmNN blockShape is [H, W] Cl asks for W, H - int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[0]); - int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]); + 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); - arm_compute::ICLTensor& output = static_cast<IClTensorHandle*>(m_Data.m_Outputs[0])->GetTensor(); - output.info()->set_data_layout(aclDataLayout); + 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::CLReshapeLayer()); + m_LayerReshapeOutput.reset(new arm_compute::CLReshapeLayer()); + + m_LayerReshapeInput->configure(clCompileContext, &input, &m_ReshapeInputTensor); + m_LayerReshapeOutput->configure(clCompileContext, &m_ReshapeOutputTensor, &output); + } + + // ArmNN blockShape is [H, W] ACl asks for W, H + int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[0]); + int32_t blockWidth = (rank == 3) ? 1 : armnn::numeric_cast<int32_t>(descriptor.m_Parameters.m_BlockShape[1]); const arm_compute::CropInfo cropInfo = BuildArmComputeCropInfo(descriptor.m_Parameters); { ARMNN_SCOPED_PROFILING_EVENT(Compute::Undefined, "ClBatchToSpaceNdWorkload_configure"); - m_Layer.configure(clCompileContext, &input, blockWidth, blockHeight, &output, cropInfo); + m_Layer.configure(clCompileContext, + (rank == 3) ? &m_ReshapeInputTensor : &input, + blockWidth, + blockHeight, + (rank == 3) ? &m_ReshapeOutputTensor : &output, + cropInfo); } } void ClBatchToSpaceNdWorkload::Execute() const { ARMNN_SCOPED_PROFILING_EVENT_CL_GUID("ClBatchToSpaceNdWorkload_Execute", this->GetGuid()); + if (m_LayerReshapeInput) + { + m_LayerReshapeInput->run(); + } RunClFunction(m_Layer, CHECK_LOCATION()); + if (m_LayerReshapeOutput) + { + m_LayerReshapeOutput->run(); + } } } //namespace armnn |