commit 5e51cd803c7d926b2a9bd7c680c8d59fe100c2f2
author Keith Davis <keith.davis@arm.com> Wed Jan 29 16:52:59 2020 +0000
committer Keith Davis <keith.davis@arm.com> Fri Jan 31 16:43:52 2020 +0000
tree 963982112d739a85d25fcc08bed8b16be50f6a94
parent c809a29e5d5bd62fac05c7d6059e1fa409ab4dc1

IVGCVSW-4390 Refactor QUANTIZE to make use of Decoder/Encoder types

 * Add no-ops for CL/NEON Uint8
 * Refactor Quantize workload to Decoder/Encoder types

Signed-off-by: Keith Davis <keith.davis@arm.com>
Change-Id: I80b09de528299b925e2ac38acd9a5019b8d3e4ac

src/backends/reference/workloads/RefQuantizeWorkload.cpp

diff --git a/src/backends/reference/workloads/RefQuantizeWorkload.cpp b/src/backends/reference/workloads/RefQuantizeWorkload.cpp
index ab2ee7f..2eef5f3 100644
--- a/src/backends/reference/workloads/RefQuantizeWorkload.cpp
+++ b/src/backends/reference/workloads/RefQuantizeWorkload.cpp
@@ -5,6 +5,8 @@
 
 #include "RefQuantizeWorkload.hpp"
 
+#include "RefWorkloadUtils.hpp"
+
 #include <armnn/TypesUtils.hpp>
 
 
@@ -14,14 +16,13 @@
 namespace
 {
 
-template<typename T>
-void QuantizeImpl(const void *input, void *output, size_t numValues, float scale, int offset)
+void QuantizeImpl(Decoder<float>& in, Encoder<float>& out, size_t numValues)
 {
-    auto in = static_cast<const float *>(input);
-    auto out = static_cast<T *>(output);
-    for (size_t i = 0; i < numValues; i++, in++, out++)
+    for (unsigned int i = 0; i < numValues; i++)
     {
-        *out = armnn::Quantize<T>(*in, scale, offset);
+        in[i];
+        out[i];
+        out.Set(in.Get());
     }
 }
 
@@ -30,42 +31,24 @@
 RefQuantizeWorkload::RefQuantizeWorkload(const QuantizeQueueDescriptor& descriptor, const WorkloadInfo &info)
     : BaseWorkload(descriptor, info)
     , m_NumElements(info.m_InputTensorInfos[0].GetNumElements())
-    , m_TargetType(info.m_OutputTensorInfos[0].GetDataType())
-    , m_Scale(info.m_OutputTensorInfos[0].GetQuantizationScale())
-    , m_Offset(info.m_OutputTensorInfos[0].GetQuantizationOffset())
 {
 }
 
+void RefQuantizeWorkload::PostAllocationConfigure()
+{
+    const TensorInfo& inputInfo = armnn::GetTensorInfo(m_Data.m_Inputs[0]);
+    m_InputDecoder = MakeDecoder<float>(inputInfo);
+
+    const TensorInfo& outputInfo = armnn::GetTensorInfo(m_Data.m_Outputs[0]);
+    m_OutputEncoder = MakeEncoder<float>(outputInfo);
+}
+
 void RefQuantizeWorkload::Execute() const
 {
-    const void* input = m_Data.m_Inputs[0]->Map(true);
-    void* output =  m_Data.m_Outputs[0]->Map(true);
+    m_InputDecoder->Reset(m_Data.m_Inputs[0]->Map());
+    m_OutputEncoder->Reset(m_Data.m_Outputs[0]->Map());
 
-    switch(m_TargetType)
-    {
-        case DataType::QAsymmU8:
-        {
-            QuantizeImpl<uint8_t>(input, output, m_NumElements, m_Scale, m_Offset);
-            break;
-        }
-        case DataType::QSymmS8:
-        {
-            QuantizeImpl<int8_t>(input, output, m_NumElements, m_Scale, 0);
-            break;
-        }
-        case DataType::QSymmS16:
-        {
-            QuantizeImpl<int16_t>(input, output, m_NumElements, m_Scale, 0);
-            break;
-        }
-        default:
-        {
-            BOOST_ASSERT_MSG(false, "RefQuantizeWorkload: Non quantized output type encountered");
-        }
-    }
-
-    m_Data.m_Inputs[0]->Unmap();
-    m_Data.m_Outputs[0]->Unmap();
+    QuantizeImpl(*m_InputDecoder, *m_OutputEncoder, m_NumElements);
 }
 
 } //namespace armnn
\ No newline at end of file