IVGCVSW-5826 Change weights layout for depthwise to [1,H,W,I*M]

 * This change is necessary because tflite uses a [1,H,W,I*M] format
   and uses the I*M dimension for per axis quantization. Our previous
   layout [M,I,H,W] can't handle the correlating quantization scales.
 * Updates Onnx-, TfLiteParser and TfliteDelegate
 * Updates the CpuRef, CpuAcc and GpuAcc backends
 * Adjusts unit tests
 * Adds test to ensure models with old layout can still be read and
   executed
 * Adds conversion function to previous layout [1,H,W,I*M] --> [M,I,H,W]
   which can be used by backend developers

!android-nn-driver:5553

Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ifef23368b8c3702cf315a5838d214f7dc13c0152

1 files changed, 3 insertions, 3 deletions

diff --git a/src/armnn/test/optimizations/FuseActivationTests.cpp b/src/armnn/test/optimizations/FuseActivationTests.cpp
index 9e332136f6..35b5bbc2da 100644
--- a/src/armnn/test/optimizations/FuseActivationTests.cpp
+++ b/src/armnn/test/optimizations/FuseActivationTests.cpp
@@ -81,9 +81,9 @@ public:
     using LayerType = DepthwiseConvolution2dLayer;
     static const bool isElementWise = false;
 
-    static TensorShape GetInputShape()   { return TensorShape( {1, 4, 4, 3}); }   // NHWCin
-    static TensorShape GetOutputShape()  { return TensorShape( {1, 3, 3, 12}); }  // NHWCout
-    static TensorShape GetWeightsShape() { return TensorShape( {4, 3, 2, 2}); }   // MCinHW
+    static TensorShape GetInputShape()   { return TensorShape( {1, 4, 4, 3}); }   // [N,H,W,Cin]
+    static TensorShape GetOutputShape()  { return TensorShape( {1, 3, 3, 12}); }  // [N,H,W,Cout]
+    static TensorShape GetWeightsShape() { return TensorShape( {1, 2, 2, 12}); }  // [1,H,W,Cout]
 
     constexpr static const unsigned int inputSize  = 48; //batchIn * heightIn * widthIn * channelIn;
     constexpr static const unsigned int outputSize = 108; //batchOut * heightOut * widthOut * channelOut;