1 files changed, 83 insertions, 104 deletions
diff --git a/src/armnn/backends/test/SplitterTestImpl.hpp b/src/armnn/backends/test/SplitterTestImpl.hpp
index b72046e4bc..70b798eafa 100644
--- a/src/armnn/backends/test/SplitterTestImpl.hpp
+++ b/src/armnn/backends/test/SplitterTestImpl.hpp
@@ -25,31 +25,34 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& wo
     unsigned int inputHeight = 6;
     unsigned int inputChannels = 3;
 
-    unsigned int outputWidth1 = 2;
-    unsigned int outputHeight1 = 2;
-    unsigned int outputChannels1 = 3;
+    // NOTE: Compute Library imposes a restriction that the x and y dimension (input height and width)
+    //       cannot be split.
+    //       For the reasons for this see first comment on https://jira.arm.com/browse/IVGCVSW-1239
+    //
+    // this test has therefore been recast to split the channels, then split the resulting subtensor
 
-    unsigned int outputWidth2 = 2;
-    unsigned int outputHeight2 = 4;
-    unsigned int outputChannels2 = 3;
+    // to take channel 0 of original output
+    // and channel 0 and channel 1 of the split subtensor
+    unsigned int outputWidth1 = inputWidth;
+    unsigned int outputHeight1 = inputHeight;
+    unsigned int outputChannels1 = 1;
 
-    unsigned int outputWidth3 = 3;
-    unsigned int outputHeight3 = 6;
-    unsigned int outputChannels3 = 2;
-
-    unsigned int outputWidth4 = 3;
-    unsigned int outputHeight4 = 6;
-    unsigned int outputChannels4 = 1;
+    // to take channel 1 and 2 of the original output
+    unsigned int outputWidth2 = inputWidth;
+    unsigned int outputHeight2 = inputHeight;
+    unsigned int outputChannels2 = 2;
 
 
     // Define the tensor descriptors
     armnn::TensorInfo inputTensorInfo({ inputChannels, inputHeight, inputWidth }, armnn::GetDataType<T>());
+
+    // outputs of the original split
     armnn::TensorInfo outputTensorInfo1({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>());
     armnn::TensorInfo outputTensorInfo2({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo3({ outputChannels3, outputHeight3, outputWidth3 }, armnn::GetDataType<T>());
-    armnn::TensorInfo outputTensorInfo4({ outputChannels4, outputHeight4, outputWidth4 }, armnn::GetDataType<T>());
-    // note that output 5 should match output 2
-    armnn::TensorInfo outputTensorInfo5({ outputChannels2, outputHeight2, outputWidth2 }, armnn::GetDataType<T>());
+
+    // outputs of the subsequent subtensor split
+    armnn::TensorInfo outputTensorInfo3({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>());
+    armnn::TensorInfo outputTensorInfo4({ outputChannels1, outputHeight1, outputWidth1 }, armnn::GetDataType<T>());
 
     // Set quantization parameters if the requested type is a quantized type.
     // The quantization doesn't really matter as the splitter operator doesn't dequantize/quantize
@@ -65,15 +68,12 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& wo
         outputTensorInfo3.SetQuantizationOffset(qOffset);
         outputTensorInfo4.SetQuantizationScale(qScale);
         outputTensorInfo4.SetQuantizationOffset(qOffset);
-        outputTensorInfo5.SetQuantizationScale(qScale);
-        outputTensorInfo5.SetQuantizationOffset(qOffset);
     }
 
     LayerTestResult<T,3> ret1(outputTensorInfo1);
     LayerTestResult<T,3> ret2(outputTensorInfo2);
     LayerTestResult<T,3> ret3(outputTensorInfo3);
     LayerTestResult<T,3> ret4(outputTensorInfo4);
-    LayerTestResult<T,3> ret5(outputTensorInfo5);
 
     auto input = MakeTensor<T, 3>(inputTensorInfo, std::vector<T>(
         QuantizedVector<T>(qScale, qOffset, {
@@ -100,98 +100,74 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& wo
         })
     ));
 
-
+    // channel 0 of the original input
     ret1.outputExpected = MakeTensor<T, 3>(outputTensorInfo1, std::vector<T>(
         QuantizedVector<T>(qScale, qOffset, {
-            1.0f, 2.0f,
-            6.0f, 7.0f,
-
-            31.0f, 32.0f,
-            36.0f, 37.0f,
-
-            61.0f, 62.0f,
-            66.0f, 67.0f,
+            1.0f, 2.0f, 3.0f, 4.0f, 5.0f,
+            6.0f, 7.0f, 8.0f, 9.0f, 10.0f,
+            11.0f, 12.0f, 13.0f, 14.0f, 15.0f,
+            16.0f, 17.0f, 18.0f, 19.0f, 20.0f,
+            21.0f, 22.0f, 23.0f, 24.0f, 25.0f,
+            26.0f, 27.0f, 28.0f, 29.0f, 30.0f,
         })
     ));
 
+    // channel 1 & 2 of the original input
     ret2.outputExpected = MakeTensor<T, 3>(outputTensorInfo2, std::vector<T>(
         QuantizedVector<T>(qScale, qOffset, {
-            11.0f, 12.0f,
-            16.0f, 17.0f,
-            21.0f, 22.0f,
-            26.0f, 27.0f,
-
-            41.0f, 42.0f,
-            46.0f, 47.0f,
-            51.0f, 52.0f,
-            56.0f, 57.0f,
-
-            71.0f, 72.0f,
-            76.0f, 77.0f,
-            81.0f, 82.0f,
-            86.0f, 87.0f,
+            31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
+            36.0f, 37.0f, 38.0f, 39.0f, 40.0f,
+            41.0f, 42.0f, 43.0f, 44.0f, 45.0f,
+            46.0f, 47.0f, 48.0f, 49.0f, 50.0f,
+            51.0f, 52.0f, 53.0f, 54.0f, 55.0f,
+            56.0f, 57.0f, 58.0f, 59.0f, 60.0f,
+
+            61.0f, 62.0f, 63.0f, 64.0f, 65.0f,
+            66.0f, 67.0f, 68.0f, 69.0f, 70.0f,
+            71.0f, 72.0f, 73.0f, 74.0f, 75.0f,
+            76.0f, 77.0f, 78.0f, 79.0f, 80.0f,
+            81.0f, 82.0f, 83.0f, 84.0f, 85.0f,
+            86.0f, 87.0f, 88.0f, 89.0f, 90.0f,
         })
     ));
 
+    // channel 0 of return 2 (i.e. channels 1 and 2 of the original input)
     ret3.outputExpected = MakeTensor<T, 3>(outputTensorInfo3, std::vector<T>(
         QuantizedVector<T>(qScale, qOffset, {
-            3.0f, 4.0f, 5.0f,
-            8.0f, 9.0f, 10.0f,
-            13.0f, 14.0f, 15.0f,
-            18.0f, 19.0f, 20.0f,
-            23.0f, 24.0f, 25.0f,
-            28.0f, 29.0f, 30.0f,
-
-            33.0f, 34.0f, 35.0f,
-            38.0f, 39.0f, 40.0f,
-            43.0f, 44.0f, 45.0f,
-            48.0f, 49.0f, 50.0f,
-            53.0f, 54.0f, 55.0f,
-            58.0f, 59.0f, 60.0f,
+            31.0f, 32.0f, 33.0f, 34.0f, 35.0f,
+            36.0f, 37.0f, 38.0f, 39.0f, 40.0f,
+            41.0f, 42.0f, 43.0f, 44.0f, 45.0f,
+            46.0f, 47.0f, 48.0f, 49.0f, 50.0f,
+            51.0f, 52.0f, 53.0f, 54.0f, 55.0f,
+            56.0f, 57.0f, 58.0f, 59.0f, 60.0f,
         })
     ));
 
+    // channel 1 of return 2
     ret4.outputExpected = MakeTensor<T, 3>(outputTensorInfo4, std::vector<T>(
         QuantizedVector<T>(qScale, qOffset, {
-            63.0f, 64.0f, 65.0f,
-            68.0f, 69.0f, 70.0f,
-            73.0f, 74.0f, 75.0f,
-            78.0f, 79.0f, 80.0f,
-            83.0f, 84.0f, 85.0f,
-            88.0f, 89.0f, 90.0f,
-        })
-    ));
-
-
-    ret5.outputExpected = MakeTensor<T, 3>(outputTensorInfo5, std::vector<T>(
-        QuantizedVector<T>(qScale, qOffset, {
-            11.0f, 12.0f,
-            16.0f, 17.0f,
-            21.0f, 22.0f,
-            26.0f, 27.0f,
-
-            41.0f, 42.0f,
-            46.0f, 47.0f,
-            51.0f, 52.0f,
-            56.0f, 57.0f,
-
-            71.0f, 72.0f,
-            76.0f, 77.0f,
-            81.0f, 82.0f,
-            86.0f, 87.0f,
+            61.0f, 62.0f, 63.0f, 64.0f, 65.0f,
+            66.0f, 67.0f, 68.0f, 69.0f, 70.0f,
+            71.0f, 72.0f, 73.0f, 74.0f, 75.0f,
+            76.0f, 77.0f, 78.0f, 79.0f, 80.0f,
+            81.0f, 82.0f, 83.0f, 84.0f, 85.0f,
+            86.0f, 87.0f, 88.0f, 89.0f, 90.0f,
         })
     ));
 
+    // NOTE: as a corollary of the no splitting of x and y restriction the x and y values of the view origins
+    //       have to be zero, the co-ordinates are as per the tensor info above channels, height/y, width/x
+    //       note that under the hood the compute engine reverses these i.e. its coordinate system is x, y, channels
     std::vector<unsigned int> wOrigin1 = {0, 0, 0}; //extent of the window is defined by size of output[0]
     armnn::SplitterQueueDescriptor::ViewOrigin window1(wOrigin1);
 
-    std::vector<unsigned int> wOrigin2 = {0, 2, 0}; //extent of the window is defined by size of output[1]
+    std::vector<unsigned int> wOrigin2 = {1, 0, 0}; //extent of the window is defined by size of output[1]
     armnn::SplitterQueueDescriptor::ViewOrigin window2(wOrigin2);
 
-    std::vector<unsigned int> wOrigin3 = {0, 0, 2}; //extent of the window is defined by size of output[2]
+    std::vector<unsigned int> wOrigin3 = {0, 0, 0}; //extent of the window is defined by size of output[2]
     armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3);
 
-    std::vector<unsigned int> wOrigin4 = {2, 0, 2}; //extent of the window is defined by size of output[3]
+    std::vector<unsigned int> wOrigin4 = {1, 0, 0}; //extent of the window is defined by size of output[3]
     armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4);
 
     bool subTensorsSupported = workloadFactory.SupportsSubTensors();
@@ -210,43 +186,29 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& wo
 
     std::unique_ptr<armnn::ITensorHandle> outputHandle3 =
         subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo3.GetShape(), wOrigin3.data()) :
+            workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo3.GetShape(), wOrigin3.data()) :
             workloadFactory.CreateTensorHandle(outputTensorInfo3);
 
     std::unique_ptr<armnn::ITensorHandle> outputHandle4 =
         subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo4.GetShape(), wOrigin4.data()) :
+            workloadFactory.CreateSubTensorHandle(*outputHandle2, outputTensorInfo4.GetShape(), wOrigin4.data()) :
             workloadFactory.CreateTensorHandle(outputTensorInfo4);
 
-    std::unique_ptr<armnn::ITensorHandle> outputHandle5 =
-        subTensorsSupported ?
-            workloadFactory.CreateSubTensorHandle(*inputHandle, outputTensorInfo5.GetShape(), wOrigin2.data()) :
-            workloadFactory.CreateTensorHandle(outputTensorInfo5);
-
+    // Do the first split
     armnn::SplitterQueueDescriptor data;
     armnn::WorkloadInfo info;
     AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get());
     AddOutputToWorkload(data, info, outputTensorInfo1, outputHandle1.get());
     AddOutputToWorkload(data, info, outputTensorInfo2, outputHandle2.get());
-    AddOutputToWorkload(data, info, outputTensorInfo3, outputHandle3.get());
-    AddOutputToWorkload(data, info, outputTensorInfo4, outputHandle4.get());
-    AddOutputToWorkload(data, info, outputTensorInfo5, outputHandle5.get());
 
     data.m_ViewOrigins.push_back(window1);
     data.m_ViewOrigins.push_back(window2);
-    data.m_ViewOrigins.push_back(window3);
-    data.m_ViewOrigins.push_back(window4);
-    //add window2 again (to have an overlapping split)
-    data.m_ViewOrigins.push_back(window2);
 
     std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateSplitter(data, info);
 
     inputHandle->Allocate();
     outputHandle1->Allocate();
     outputHandle2->Allocate();
-    outputHandle3->Allocate();
-    outputHandle4->Allocate();
-    outputHandle5->Allocate();
 
     CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0]);
 
@@ -254,11 +216,28 @@ std::vector<LayerTestResult<T,3>> SplitterTestCommon(armnn::IWorkloadFactory& wo
 
     CopyDataFromITensorHandle(&ret1.output[0][0][0], outputHandle1.get());
     CopyDataFromITensorHandle(&ret2.output[0][0][0], outputHandle2.get());
+
+//    // Do the second split
+    armnn::SplitterQueueDescriptor data2;
+    armnn::WorkloadInfo info2;
+    AddInputToWorkload(data2, info2, outputTensorInfo2, outputHandle2.get());
+    AddOutputToWorkload(data2, info2, outputTensorInfo3, outputHandle3.get());
+    AddOutputToWorkload(data2, info2, outputTensorInfo4, outputHandle4.get());
+
+    data2.m_ViewOrigins.push_back(window3);
+    data2.m_ViewOrigins.push_back(window4);
+
+    std::unique_ptr<armnn::IWorkload> workload2 = workloadFactory.CreateSplitter(data2, info2);
+
+    outputHandle3->Allocate();
+    outputHandle4->Allocate();
+
+    workload2->Execute();
+
     CopyDataFromITensorHandle(&ret3.output[0][0][0], outputHandle3.get());
     CopyDataFromITensorHandle(&ret4.output[0][0][0], outputHandle4.get());
-    CopyDataFromITensorHandle(&ret5.output[0][0][0], outputHandle5.get());
 
-    std::vector<LayerTestResult<T,3>> ret = {ret1, ret2, ret3, ret4, ret5};
+    std::vector<LayerTestResult<T,3>> ret = {ret1, ret2, ret3, ret4,};
 
     return ret;
 }