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Diffstat (limited to 'src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp')
| -rw-r--r-- | src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp | 185 |
1 files changed, 0 insertions, 185 deletions
diff --git a/src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp b/src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp deleted file mode 100644 index 5c24416624..0000000000 --- a/src/backends/RefWorkloads/RefNormalizationFloat32Workload.cpp +++ /dev/null @@ -1,185 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// - -#include "RefNormalizationFloat32Workload.hpp" - -#include "RefWorkloadUtils.hpp" - -#include "Profiling.hpp" - -#include <armnn/Tensor.hpp> - -#include <boost/log/trivial.hpp> -#include <boost/numeric/conversion/cast.hpp> - -namespace armnn -{ - -// Helper function to compute "Within" normalization using Krichevsky 2012: Local Brightness Normalization. -static void NormalizeWithinUingLbr(const float* inputData, - float* outputData, - const TensorShape& tensorShape, - uint32_t norm_size, - float alpha, - float beta, - float kappa) -{ - const unsigned int batchSize = tensorShape[0]; - const unsigned int depth = tensorShape[1]; - const unsigned int rows = tensorShape[2]; - const unsigned int cols = tensorShape[3]; - - int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ - - for (unsigned int n = 0; n < batchSize; n++) - { - for (unsigned int c = 0; c < depth; c++) - { - for (unsigned int h = 0; h < rows; h++) - { - for (unsigned int w = 0; w < cols; w++) - { - float accumulated_scale = 0.0; - for (int y = -radius; y <= radius; y++) - { - for (int x = -radius; x <= radius; x++) - { - int i = boost::numeric_cast<int>(w) + x; - int j = boost::numeric_cast<int>(h) + y; - - if ((i < 0) || (i >= boost::numeric_cast<int>(cols))) - { - continue; - } - - if ((j < 0) || (j >= boost::numeric_cast<int>(rows))) - { - continue; - } - - float inval = inputData[n * cols * rows * depth + - c * cols * rows + - boost::numeric_cast<unsigned int>(j) * cols + - boost::numeric_cast<unsigned int>(i)]; - - accumulated_scale += inval*inval; - } - } - outputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w] = inputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w] / (powf((kappa + (accumulated_scale * alpha)), beta)); - } - } - } - } -} - -// Helper function to compute "Across" normalization using Krichevsky 2012: Local Brightness Normalization. -void NormalizeAcrossUingLbr(const float* inputData, - float* outputData, - const TensorShape& tensorShape, - uint32_t norm_size, - float alpha, - float beta, - float kappa) -{ - const unsigned int batchSize = tensorShape[0]; - const unsigned int depth = tensorShape[1]; - const unsigned int rows = tensorShape[2]; - const unsigned int cols = tensorShape[3]; - - int radius = boost::numeric_cast<int>(norm_size / 2u); /* Strong Assumption on rounding Mode */ - - for (unsigned int n = 0; n < batchSize; n++) - { - for (unsigned int c = 0; c < depth; c++) - { - for (unsigned int h = 0; h < rows; h++) - { - for (unsigned int w = 0; w < cols; w++) - { - float accumulated_scale = 0.0; - for (int z = -radius; z <= radius; z++) - { - int k = boost::numeric_cast<int>(c) + z; - - if ((k < 0) || (k >= boost::numeric_cast<int>(depth))) - { - continue; - } - - float inval = inputData[n * cols * rows * depth + - boost::numeric_cast<unsigned int>(k) * cols * rows + - h * cols + - w]; - - accumulated_scale += inval*inval; - } - float scale = kappa + (accumulated_scale * alpha); - scale = powf(scale, -beta); - outputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w] = scale * - inputData[n * cols * rows * depth + - c * cols * rows + - h * cols + - w]; - } - } - } - } -} - -void RefNormalizationFloat32Workload::Execute() const -{ - ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefNormalizationFloat32Workload_Execute"); - - const TensorInfo& inputInfo = GetTensorInfo(m_Data.m_Inputs[0]); - - float* outputData = GetOutputTensorDataFloat(0, m_Data); - const float* inputData = GetInputTensorDataFloat(0, m_Data); - - - if (NormalizationAlgorithmMethod::LocalBrightness == m_Data.m_Parameters.m_NormMethodType) - { - if (NormalizationAlgorithmChannel::Within == m_Data.m_Parameters.m_NormChannelType) - { - NormalizeWithinUingLbr(inputData, - outputData, - inputInfo.GetShape(), - m_Data.m_Parameters.m_NormSize, - m_Data.m_Parameters.m_Alpha, - m_Data.m_Parameters.m_Beta, - m_Data.m_Parameters.m_K); - } - else if (NormalizationAlgorithmChannel::Across == m_Data.m_Parameters.m_NormChannelType) - { - NormalizeAcrossUingLbr(inputData, - outputData, - inputInfo.GetShape(), - m_Data.m_Parameters.m_NormSize, - m_Data.m_Parameters.m_Alpha, - m_Data.m_Parameters.m_Beta, - m_Data.m_Parameters.m_K); - } - else - { - BOOST_LOG_TRIVIAL(warning) << "Illegal NORMALIZATION mode in normalization_f32"; - return; - } - } - else - { - BOOST_LOG_TRIVIAL(warning) << "Lcr method (Jarret 2009: Local Contrast Normalization) not supported yet."; - return; - } -} - -} //namespace armnn |