diff options
Diffstat (limited to 'src/backends/reference/workloads/RefL2NormalizationWorkload.cpp')
| -rw-r--r-- | src/backends/reference/workloads/RefL2NormalizationWorkload.cpp | 108 |
1 files changed, 54 insertions, 54 deletions
diff --git a/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp b/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp index 3764b9a49a..6fec1abe6f 100644 --- a/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp +++ b/src/backends/reference/workloads/RefL2NormalizationWorkload.cpp @@ -4,13 +4,13 @@ // #include "RefL2NormalizationWorkload.hpp" - #include "RefWorkloadUtils.hpp" #include "Decoders.hpp" #include "Encoders.hpp" -#include "DataLayoutIndexed.hpp" -#include "Profiling.hpp" +#include <Profiling.hpp> + +#include <armnnUtils/DataLayoutIndexed.hpp> #include <boost/numeric/conversion/cast.hpp> @@ -21,80 +21,80 @@ using namespace armnnUtils; namespace armnn { RefL2NormalizationWorkload::RefL2NormalizationWorkload( - const L2NormalizationQueueDescriptor& descriptor, - const WorkloadInfo& info) - : BaseWorkload<L2NormalizationQueueDescriptor>(descriptor, info) {} + const L2NormalizationQueueDescriptor& descriptor, + const WorkloadInfo& info) + : BaseWorkload<L2NormalizationQueueDescriptor>(descriptor, info) {} - void RefL2NormalizationWorkload::Execute() const - { - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefL2NormalizationWorkload_Execute"); +void RefL2NormalizationWorkload::Execute() const +{ + ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefL2NormalizationWorkload_Execute"); - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); + const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); + const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]); - auto inputDecoder = MakeDecoder<float>(inputInfo, m_Data.m_Inputs[0]->Map()); - auto outputEncoder = MakeEncoder<float>(outputInfo, m_Data.m_Outputs[0]->Map()); + auto inputDecoder = MakeDecoder<float>(inputInfo, m_Data.m_Inputs[0]->Map()); + auto outputEncoder = MakeEncoder<float>(outputInfo, m_Data.m_Outputs[0]->Map()); - DataLayoutIndexed dataLayout(m_Data.m_Parameters.m_DataLayout); + DataLayoutIndexed dataLayout(m_Data.m_Parameters.m_DataLayout); - const TensorShape& shape = inputInfo.GetShape(); - unsigned int paddedShapeArray[4]; - const int idxShift = 4 - boost::numeric_cast<int>(shape.GetNumDimensions()); + const TensorShape& shape = inputInfo.GetShape(); + unsigned int paddedShapeArray[4]; + const int idxShift = 4 - boost::numeric_cast<int>(shape.GetNumDimensions()); - const unsigned int batches = (idxShift == 0) ? shape[0] : 1; - paddedShapeArray[0] = batches; + const unsigned int batches = (idxShift == 0) ? shape[0] : 1; + paddedShapeArray[0] = batches; - const int channelsIdx = boost::numeric_cast<int>(dataLayout.GetChannelsIndex()); - const unsigned int channels = (channelsIdx - idxShift >= 0) - ? shape[boost::numeric_cast<unsigned int>(channelsIdx - idxShift)] - : 1; - paddedShapeArray[channelsIdx] = channels; + const int channelsIdx = boost::numeric_cast<int>(dataLayout.GetChannelsIndex()); + const unsigned int channels = (channelsIdx - idxShift >= 0) + ? shape[boost::numeric_cast<unsigned int>(channelsIdx - idxShift)] + : 1; + paddedShapeArray[channelsIdx] = channels; - const int heightIdx = boost::numeric_cast<int>(dataLayout.GetHeightIndex()); - const unsigned int height = (heightIdx - idxShift >= 0) - ? shape[boost::numeric_cast<unsigned int>(heightIdx - idxShift)] - : 1; - paddedShapeArray[heightIdx] = height; + const int heightIdx = boost::numeric_cast<int>(dataLayout.GetHeightIndex()); + const unsigned int height = (heightIdx - idxShift >= 0) + ? shape[boost::numeric_cast<unsigned int>(heightIdx - idxShift)] + : 1; + paddedShapeArray[heightIdx] = height; - const int widthIdx = boost::numeric_cast<int>(dataLayout.GetWidthIndex()); - const unsigned int width = (widthIdx - idxShift >= 0) - ? shape[boost::numeric_cast<unsigned int>(widthIdx - idxShift)] - : 1; - paddedShapeArray[widthIdx] = width; + const int widthIdx = boost::numeric_cast<int>(dataLayout.GetWidthIndex()); + const unsigned int width = (widthIdx - idxShift >= 0) + ? shape[boost::numeric_cast<unsigned int>(widthIdx - idxShift)] + : 1; + paddedShapeArray[widthIdx] = width; - const TensorShape& paddedShape = TensorShape(4, paddedShapeArray); + const TensorShape& paddedShape = TensorShape(4, paddedShapeArray); - for (unsigned int n = 0; n < batches; ++n) + for (unsigned int n = 0; n < batches; ++n) + { + for (unsigned int c = 0; c < channels; ++c) { - for (unsigned int c = 0; c < channels; ++c) + for (unsigned int h = 0; h < height; ++h) { - for (unsigned int h = 0; h < height; ++h) + for (unsigned int w = 0; w < width; ++w) { - for (unsigned int w = 0; w < width; ++w) + float reduction = 0.0; + for (unsigned int d = 0; d < channels; ++d) { - float reduction = 0.0; - for (unsigned int d = 0; d < channels; ++d) - { - unsigned int inputIndex = dataLayout.GetIndex(paddedShape, n, d, h, w); + unsigned int inputIndex = dataLayout.GetIndex(paddedShape, n, d, h, w); - (*inputDecoder)[inputIndex]; - const float value = inputDecoder->Get(); - reduction += value * value; - } + (*inputDecoder)[inputIndex]; + const float value = inputDecoder->Get(); + reduction += value * value; + } - unsigned int index = dataLayout.GetIndex(paddedShape, n, c, h, w); + unsigned int index = dataLayout.GetIndex(paddedShape, n, c, h, w); - float maximum = reduction < m_Data.m_Parameters.m_Eps ? m_Data.m_Parameters.m_Eps : reduction; + float maximum = reduction < m_Data.m_Parameters.m_Eps ? m_Data.m_Parameters.m_Eps : reduction; - const float scale = 1.0f / sqrtf(maximum); + const float scale = 1.0f / sqrtf(maximum); - (*inputDecoder)[index]; - (*outputEncoder)[index]; - outputEncoder->Set(inputDecoder->Get() * scale); - } + (*inputDecoder)[index]; + (*outputEncoder)[index]; + outputEncoder->Set(inputDecoder->Get() * scale); } } } } +} } //namespace armnn |