MLCE-80 Remove strong typing from NeonBatchNormalization

Technical debt work towards adding some new Neon workloads

Change-Id: I08ab6dd14d0e89d4ebc8a878fb69caa5681012bf

1 files changed, 0 insertions, 104 deletions

diff --git a/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp b/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp
deleted file mode 100644
index a8181f66d9..0000000000
--- a/src/backends/neon/workloads/NeonBatchNormalizationFloatWorkload.cpp
+++ /dev/null
@@ -1,104 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "NeonBatchNormalizationFloatWorkload.hpp"
-#include <backendsCommon/CpuTensorHandle.hpp>
-#include <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 =
-          armcomputetensorutils::BuildArmComputeTensorInfo(input, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclOutputInfo =
-          armcomputetensorutils::BuildArmComputeTensorInfo(output, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclMeanInfo =
-          armcomputetensorutils::BuildArmComputeTensorInfo(mean, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclVarInfo =
-          armcomputetensorutils::BuildArmComputeTensorInfo(var, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclBetaInfo =
-          armcomputetensorutils::BuildArmComputeTensorInfo(beta, descriptor.m_DataLayout);
-    const arm_compute::TensorInfo aclGammaInfo =
-          armcomputetensorutils::BuildArmComputeTensorInfo(gamma, descriptor.m_DataLayout);
-
-    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();
-
-    arm_compute::DataLayout aclDataLayout = ConvertDataLayout(m_Data.m_Parameters.m_DataLayout);
-    input.info()->set_data_layout(aclDataLayout);
-    output.info()->set_data_layout(aclDataLayout);
-
-    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);
-
-    InitializeArmComputeTensorData(*m_Mean, m_Data.m_Mean);
-    InitializeArmComputeTensorData(*m_Variance, m_Data.m_Variance);
-    InitializeArmComputeTensorData(*m_Gamma, m_Data.m_Gamma);
-    InitializeArmComputeTensorData(*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