IVGCVSW-1978: Support NHWC for ResizeBilinear CpuRef

* Adds implementation to plumb DataLayout parameter
  for ResizeBilinear on CpuRef.
* Adds unit tests to execute ResizeBilinear on CpuRef
  using the NHWC data layout.
* Adds DataLayoutIndexed API, allowing easy access to
  the Channels, Height and Width of a tensor based on
  its data layout. This reduces code duplication.
* Refactors original ResizeBilinear implementation and
  tests to use the DataLayoutIndexed API when required.

Change-Id: Ic2b8916cdd2e370d070175547079d774daf6d7bf

4 files changed, 32 insertions, 18 deletions

diff --git a/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp b/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp
index 50ee7a218a..8d86bdcf34 100644
--- a/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp
+++ b/src/backends/reference/workloads/RefResizeBilinearFloat32Workload.cpp
@@ -23,7 +23,8 @@ void RefResizeBilinearFloat32Workload::Execute() const
     ResizeBilinear(GetInputTensorDataFloat(0, m_Data),
         inputInfo,
         GetOutputTensorDataFloat(0, m_Data),
-        outputInfo);
+        outputInfo,
+        m_Data.m_Parameters.m_DataLayout);
 }
 
 } //namespace armnn
diff --git a/src/backends/reference/workloads/ResizeBilinear.cpp b/src/backends/reference/workloads/ResizeBilinear.cpp
index 0bce3c7ed8..e098c6c20d 100644
--- a/src/backends/reference/workloads/ResizeBilinear.cpp
+++ b/src/backends/reference/workloads/ResizeBilinear.cpp
@@ -25,27 +25,31 @@ inline float Lerp(float a, float b, float w)
 
 }
 
-void ResizeBilinear(const float* in, const TensorInfo& inputInfo, float* out, const TensorInfo& outputInfo)
+void ResizeBilinear(const float*      in,
+                    const TensorInfo& inputInfo,
+                    float*            out,
+                    const TensorInfo& outputInfo,
+                    DataLayoutIndexed dataLayout)
 {
     // We follow the definition of TensorFlow and AndroidNN: the top-left corner of a texel in the output
     // image is projected into the input image to figure out the interpolants and weights. Note that this
     // will yield different results than if projecting the centre of output texels.
 
     const unsigned int batchSize = inputInfo.GetShape()[0];
-    const unsigned int channelCount = inputInfo.GetShape()[1];
+    const unsigned int channelCount = inputInfo.GetShape()[dataLayout.GetChannelsIndex()];
 
-    const unsigned int inputHeight = inputInfo.GetShape()[2];
-    const unsigned int inputWidth = inputInfo.GetShape()[3];
-    const unsigned int outputHeight = outputInfo.GetShape()[2];
-    const unsigned int outputWidth = outputInfo.GetShape()[3];
+    const unsigned int inputHeight = inputInfo.GetShape()[dataLayout.GetHeightIndex()];
+    const unsigned int inputWidth = inputInfo.GetShape()[dataLayout.GetWidthIndex()];
+    const unsigned int outputHeight = outputInfo.GetShape()[dataLayout.GetHeightIndex()];
+    const unsigned int outputWidth = outputInfo.GetShape()[dataLayout.GetWidthIndex()];
 
     // How much to scale pixel coordinates in the output image, to get the corresponding pixel coordinates
     // in the input image.
     const float scaleY = boost::numeric_cast<float>(inputHeight) / boost::numeric_cast<float>(outputHeight);
     const float scaleX = boost::numeric_cast<float>(inputWidth) / boost::numeric_cast<float>(outputWidth);
 
-    TensorBufferArrayView<const float> input(inputInfo.GetShape(), in);
-    TensorBufferArrayView<float> output(outputInfo.GetShape(), out);
+    TensorBufferArrayView<const float> input(inputInfo.GetShape(), in, dataLayout);
+    TensorBufferArrayView<float> output(outputInfo.GetShape(), out, dataLayout);
 
     for (unsigned int n = 0; n < batchSize; ++n)
     {
diff --git a/src/backends/reference/workloads/ResizeBilinear.hpp b/src/backends/reference/workloads/ResizeBilinear.hpp
index 847b8e8bef..92b229d3bb 100644
--- a/src/backends/reference/workloads/ResizeBilinear.hpp
+++ b/src/backends/reference/workloads/ResizeBilinear.hpp
@@ -10,6 +10,10 @@
 namespace armnn
 {
 
-void ResizeBilinear(const float* in, const TensorInfo& inputInfo, float* out, const TensorInfo& outputInfo);
+void ResizeBilinear(const float*      in,
+                    const TensorInfo& inputInfo,
+                    float*            out,
+                    const TensorInfo& outputInfo,
+                    DataLayoutIndexed dataLayout = DataLayout::NCHW);
 
 } //namespace armnn
diff --git a/src/backends/reference/workloads/TensorBufferArrayView.hpp b/src/backends/reference/workloads/TensorBufferArrayView.hpp
index e19810ca87..aba44e4593 100644
--- a/src/backends/reference/workloads/TensorBufferArrayView.hpp
+++ b/src/backends/reference/workloads/TensorBufferArrayView.hpp
@@ -15,28 +15,33 @@ template <typename DataType>
 class TensorBufferArrayView
 {
 public:
-    TensorBufferArrayView(const TensorShape& shape, DataType* data)
+    TensorBufferArrayView(const TensorShape& shape, DataType* data, DataLayoutIndexed dataLayout = DataLayout::NCHW)
         : m_Shape(shape)
         , m_Data(data)
+        , m_DataLayout(dataLayout)
     {
     }
 
     DataType& Get(unsigned int b, unsigned int c, unsigned int h, unsigned int w) const
     {
-        BOOST_ASSERT( b < m_Shape[0] || (m_Shape[0] == 0 && b == 0) );
-        BOOST_ASSERT( c < m_Shape[1] || (m_Shape[1] == 0 && c == 0) );
-        BOOST_ASSERT( h < m_Shape[2] || (m_Shape[2] == 0 && h == 0) );
-        BOOST_ASSERT( w < m_Shape[3] || (m_Shape[3] == 0 && w == 0) );
+        BOOST_ASSERT( b < m_Shape[0] || ( m_Shape[0]   == 0 && b == 0 ) );
+        BOOST_ASSERT( c < m_Shape[m_DataLayout.GetChannelsIndex()] ||
+            ( m_Shape[m_DataLayout.GetChannelsIndex()] == 0 && c == 0) );
+        BOOST_ASSERT( h < m_Shape[m_DataLayout.GetHeightIndex()] ||
+            ( m_Shape[m_DataLayout.GetHeightIndex()]   == 0 && h == 0) );
+        BOOST_ASSERT( w < m_Shape[m_DataLayout.GetWidthIndex()] ||
+            ( m_Shape[m_DataLayout.GetWidthIndex()]    == 0 && w == 0) );
 
         return m_Data[b * m_Shape[1] * m_Shape[2] * m_Shape[3]
-                    + c * m_Shape[2] * m_Shape[3]
-                    + h * m_Shape[3]
+                    + c * m_Shape[m_DataLayout.GetHeightIndex()] * m_Shape[m_DataLayout.GetWidthIndex()]
+                    + h * m_Shape[m_DataLayout.GetWidthIndex()]
                     + w];
     }
 
 private:
     const TensorShape m_Shape;
-    DataType* m_Data;
+    DataType*         m_Data;
+    DataLayoutIndexed m_DataLayout;
 };
 
 } //namespace armnn