diff options
Diffstat (limited to 'src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp')
| -rw-r--r-- | src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp | 26 |
1 files changed, 16 insertions, 10 deletions
diff --git a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp index 3d479ff80d..2a35475541 100644 --- a/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp +++ b/src/backends/neon/workloads/NeonBatchToSpaceNdWorkload.cpp @@ -19,14 +19,14 @@ using namespace armcomputetensorutils; arm_compute::Status NeonBatchToSpaceNdWorkloadValidate(const TensorInfo& input, const TensorInfo& output, - const BatchToSpaceNdDescriptor& desc) + const BatchToSpaceNdDescriptor& descriptor) { - const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, desc.m_DataLayout); - const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, desc.m_DataLayout); + const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input, descriptor.m_DataLayout); + const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output, descriptor.m_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]); + int32_t blockHeight = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[0]); + int32_t blockWidth = armnn::numeric_cast<int32_t>(descriptor.m_BlockShape[1]); const arm_compute::Status aclStatus = arm_compute::NEBatchToSpaceLayer::validate(&aclInputInfo, blockWidth, @@ -35,10 +35,16 @@ arm_compute::Status NeonBatchToSpaceNdWorkloadValidate(const TensorInfo& input, return aclStatus; } -NeonBatchToSpaceNdWorkload::NeonBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& desc, +NeonBatchToSpaceNdWorkload::NeonBatchToSpaceNdWorkload(const BatchToSpaceNdQueueDescriptor& descriptor, const WorkloadInfo& info) - : BaseWorkload<BatchToSpaceNdQueueDescriptor>(desc, info) + : BaseWorkload<BatchToSpaceNdQueueDescriptor>(descriptor, info) { + // Report Profiling Details + ARMNN_REPORT_PROFILING_WORKLOAD_DESC("NeonBatchToSpaceWorkload_Construct", + descriptor.m_Parameters, + info, + this->GetGuid()); + m_Data.ValidateInputsOutputs("NeonBatchToSpaceNdWorkload", 1, 1); arm_compute::ITensor& input = @@ -51,8 +57,8 @@ NeonBatchToSpaceNdWorkload::NeonBatchToSpaceNdWorkload(const BatchToSpaceNdQueue output.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]); + 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]); m_Layer.reset(new arm_compute::NEBatchToSpaceLayer()); m_Layer->configure(&input, blockWidth, blockHeight, &output); @@ -63,7 +69,7 @@ void NeonBatchToSpaceNdWorkload::Execute() const { if (m_Layer) { - ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonSpaceToBatchNdWorkload_Execute"); + ARMNN_SCOPED_PROFILING_EVENT_NEON_GUID("NeonSpaceToBatchNdWorkload_Execute", this->GetGuid()); m_Layer->run(); } } |