IVGCVSW-3212 Refactor the Reference BatchNormalization workloads to

handle Float32 and QAsymm8 types

 * Removed the type-specific workload implementations
 * Added type-independent RefBatchNormalizationWorkload implementation
 * Reworked BachNormImpl to use decoders/encoders
 * Improved the validation of the BatchNorm queue descriptor
 * Fixed unit tests where necessary

Change-Id: Icf3fa1332292d38ec2fa0b1cb984cab78426034b
Signed-off-by: Matteo Martincigh <matteo.martincigh@arm.com>

1 files changed, 0 insertions, 47 deletions

diff --git a/src/backends/reference/workloads/RefBatchNormalizationUint8Workload.cpp b/src/backends/reference/workloads/RefBatchNormalizationUint8Workload.cpp
deleted file mode 100644
index e248ad4b9d..0000000000
--- a/src/backends/reference/workloads/RefBatchNormalizationUint8Workload.cpp
+++ /dev/null
@@ -1,47 +0,0 @@
-//
-// Copyright © 2017 Arm Ltd. All rights reserved.
-// SPDX-License-Identifier: MIT
-//
-
-#include "RefBatchNormalizationUint8Workload.hpp"
-
-#include "BatchNormImpl.hpp"
-#include "RefWorkloadUtils.hpp"
-
-#include "Profiling.hpp"
-
-#include <vector>
-
-namespace armnn
-{
-RefBatchNormalizationUint8Workload::RefBatchNormalizationUint8Workload(
-    const BatchNormalizationQueueDescriptor& descriptor, const WorkloadInfo& info)
-       : Uint8Workload<BatchNormalizationQueueDescriptor>(descriptor, info),
-         m_Mean(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Mean))),
-         m_Variance(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Variance))),
-         m_Beta(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Beta))),
-         m_Gamma(std::make_unique<ScopedCpuTensorHandle>(*(descriptor.m_Gamma))) {}
-
-void RefBatchNormalizationUint8Workload::Execute() const
-{
-    ARMNN_SCOPED_PROFILING_EVENT(Compute::CpuRef, "RefBatchNormalizationUint8Workload_Execute");
-
-    const TensorInfo& inputInfo0 = GetTensorInfo(m_Data.m_Inputs[0]);
-    const TensorInfo& varInfo = GetTensorInfo(m_Variance.get());
-    const TensorInfo& meanInfo = GetTensorInfo(m_Mean.get());
-    const TensorInfo& gammaInfo = GetTensorInfo(m_Gamma.get());
-    const TensorInfo& betaInfo = GetTensorInfo(m_Beta.get());
-    const TensorInfo& outputInfo = GetTensorInfo(m_Data.m_Outputs[0]);
-
-    auto input = Dequantize(GetInputTensorDataU8(0, m_Data), inputInfo0);
-    auto var = Dequantize(m_Variance->GetConstTensor<uint8_t>(), varInfo);
-    auto mean = Dequantize(m_Mean->GetConstTensor<uint8_t>(), meanInfo);
-    auto gamma = Dequantize(m_Gamma->GetConstTensor<uint8_t>(), gammaInfo);
-    auto beta = Dequantize(m_Beta->GetConstTensor<uint8_t>(), betaInfo);
-
-    std::vector<float> results(outputInfo.GetNumElements());
-    BatchNormImpl(m_Data, var.data(), mean.data(), gamma.data(), beta.data(), results.data(), input.data());
-    Quantize(GetOutputTensorDataU8(0, m_Data), results.data(), outputInfo);
-}
-
-} //namespace armnn