IVGCVSW-1899 : Neon backend folder structure

armnn:149855

Change-Id: I26e8cf83422a65049386a5ebdb6d0001627aefaa

1 files changed, 0 insertions, 96 deletions

diff --git a/src/backends/NeonWorkloads/NeonBatchNormalizationFloatWorkload.cpp b/src/backends/NeonWorkloads/NeonBatchNormalizationFloatWorkload.cpp
deleted file mode 100644
index 2383e78df3..0000000000
--- a/src/backends/NeonWorkloads/NeonBatchNormalizationFloatWorkload.cpp
+++ /dev/null
@@ -1,96 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "NeonBatchNormalizationFloatWorkload.hpp"
-#include <backends/CpuTensorHandle.hpp>
-#include <backends/aclCommon/ArmComputeTensorUtils.hpp>
-#include <armnn/ArmNN.hpp>
-
-namespace armnn
-{
-using namespace armcomputetensorutils;
-
-
-arm_compute::Status NeonBatchNormalizationValidate(const TensorInfo& input,
-                                                   const TensorInfo& output,
-                                                   const TensorInfo& mean,
-                                                   const TensorInfo& var,
-                                                   const TensorInfo& beta,
-                                                   const TensorInfo& gamma,
-                                                   const BatchNormalizationDescriptor& descriptor)
-{
-    const arm_compute::TensorInfo aclInputInfo = BuildArmComputeTensorInfo(input);
-    const arm_compute::TensorInfo aclOutputInfo = BuildArmComputeTensorInfo(output);
-    const arm_compute::TensorInfo aclMeanInfo = BuildArmComputeTensorInfo(mean);
-    const arm_compute::TensorInfo aclVarInfo = BuildArmComputeTensorInfo(var);
-    const arm_compute::TensorInfo aclBetaInfo = BuildArmComputeTensorInfo(beta);
-    const arm_compute::TensorInfo aclGammaInfo = BuildArmComputeTensorInfo(gamma);
-
-    return arm_compute::NEBatchNormalizationLayer::validate(&aclInputInfo,
-                                                            &aclOutputInfo,
-                                                            &aclMeanInfo,
-                                                            &aclVarInfo,
-                                                            &aclBetaInfo,
-                                                            &aclGammaInfo,
-                                                            descriptor.m_Eps);
-}
-
-NeonBatchNormalizationFloatWorkload::NeonBatchNormalizationFloatWorkload(
-    const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info)
-    : FloatWorkload<BatchNormalizationQueueDescriptor>(descriptor, info)
-{
-    m_Data.ValidateInputsOutputs("NeonBatchNormalizationFloatWorkload", 1, 1);
-
-    arm_compute::ITensor& input = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Inputs[0])->GetTensor();
-    arm_compute::ITensor& output = boost::polymorphic_downcast<INeonTensorHandle*>(m_Data.m_Outputs[0])->GetTensor();
-
-    m_Mean = std::make_unique<arm_compute::Tensor>();
-    BuildArmComputeTensor(*m_Mean, m_Data.m_Mean->GetTensorInfo());
-
-    m_Variance = std::make_unique<arm_compute::Tensor>();
-    BuildArmComputeTensor(*m_Variance, m_Data.m_Variance->GetTensorInfo());
-
-    m_Gamma = std::make_unique<arm_compute::Tensor>();
-    BuildArmComputeTensor(*m_Gamma, m_Data.m_Gamma->GetTensorInfo());
-
-    m_Beta = std::make_unique<arm_compute::Tensor>();
-    BuildArmComputeTensor(*m_Beta, m_Data.m_Beta->GetTensorInfo());
-
-    m_Layer.configure(&input,
-                      &output,
-                      m_Mean.get(),
-                      m_Variance.get(),
-                      m_Beta.get(),
-                      m_Gamma.get(),
-                      m_Data.m_Parameters.m_Eps);
-
-    InitializeArmComputeTensorDataForFloatTypes(*m_Mean, m_Data.m_Mean);
-    InitializeArmComputeTensorDataForFloatTypes(*m_Variance, m_Data.m_Variance);
-    InitializeArmComputeTensorDataForFloatTypes(*m_Gamma, m_Data.m_Gamma);
-    InitializeArmComputeTensorDataForFloatTypes(*m_Beta, m_Data.m_Beta);
-
-    // Force Compute Library to perform the necessary copying and reshaping, after which
-    // delete all the input tensors that will no longer be needed
-    m_Layer.prepare();
-    FreeUnusedTensors();
-}
-
-void NeonBatchNormalizationFloatWorkload::Execute() const
-{
-    ARMNN_SCOPED_PROFILING_EVENT_NEON("NeonBatchNormalizationFloatWorkload_Execute");
-    m_Layer.run();
-}
-
-void NeonBatchNormalizationFloatWorkload::FreeUnusedTensors()
-{
-    FreeTensorIfUnused(m_Mean);
-    FreeTensorIfUnused(m_Variance);
-    FreeTensorIfUnused(m_Gamma);
-    FreeTensorIfUnused(m_Beta);
-}
-
-} //namespace armnn
-
-