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, 11 insertions, 51 deletions

diff --git a/src/backends/reference/workloads/BatchNormImpl.hpp b/src/backends/reference/workloads/BatchNormImpl.hpp
index 799e7a327b..c0250b9e0f 100644
--- a/src/backends/reference/workloads/BatchNormImpl.hpp
+++ b/src/backends/reference/workloads/BatchNormImpl.hpp
@@ -5,60 +5,20 @@
 
 #pragma once
 
-#include "RefWorkloadUtils.hpp"
-#include "TensorBufferArrayView.hpp"
+#include "Encoders.hpp"
+#include "Decoders.hpp"
 
-#include <armnn/Tensor.hpp>
-
-#include <DataLayoutIndexed.hpp>
-
-#include <cmath>
+#include <backendsCommon/WorkloadData.hpp>
 
 namespace armnn
 {
 
-template<typename NormData>
-static void BatchNormImpl(NormData     data,
-                          const float* varIn,
-                          const float* meanIn,
-                          const float* gammaIn,
-                          const float* betaIn,
-                          float*       outputData,
-                          const float* inputData)
-{
-    const TensorInfo& inputInfo = GetTensorInfo(data.m_Inputs[0]);
-    const TensorInfo& outputInfo = GetTensorInfo(data.m_Outputs[0]);
-
-    TensorBufferArrayView<const float> input(inputInfo.GetShape(),
-                                             inputData,
-                                             data.m_Parameters.m_DataLayout);
-    TensorBufferArrayView<float> output(outputInfo.GetShape(),
-                                        outputData,
-                                        data.m_Parameters.m_DataLayout);
-
-    armnnUtils::DataLayoutIndexed dataLayout(data.m_Parameters.m_DataLayout);
-
-    for (unsigned int c = 0; c < inputInfo.GetShape()[dataLayout.GetChannelsIndex()]; c++)
-    {
-        float var   = varIn[c];
-        float mean  = meanIn[c];
-        float gamma = gammaIn[c];
-        float beta  = betaIn[c];
-
-        float mult = gamma / sqrtf(var + data.m_Parameters.m_Eps);
-        float add  = beta - mult * mean;
-
-        for (unsigned int n = 0; n < inputInfo.GetShape()[0]; n++)
-        {
-            for (unsigned int h = 0; h < inputInfo.GetShape()[dataLayout.GetHeightIndex()]; h++)
-            {
-                for (unsigned int w = 0; w < inputInfo.GetShape()[dataLayout.GetWidthIndex()]; w++)
-                {
-                    output.Get(n, c, h, w) = mult * input.Get(n, c, h, w) + add;
-                }
-            }
-        }
-    }
-}
+void BatchNormImpl(const BatchNormalizationQueueDescriptor& data,
+                   Decoder<float>& meanIn,
+                   Decoder<float>& varIn,
+                   Decoder<float>& betaIn,
+                   Decoder<float>& gammaIn,
+                   Decoder<float>& inputData,
+                   Encoder<float>& outputData);
 
-} //namespace armnn
+} // namespace armnn