IVGCVSW-5544 Fix FullyConnected Delegate tests

 * Correct input shape

Signed-off-by: Narumol Prangnawarat <narumol.prangnawarat@arm.com>
Change-Id: I9d1fe4c8ef32a9dfba7f7fdd6af314e9a522fce8

3 files changed, 87 insertions, 54 deletions

diff --git a/delegate/src/FullyConnected.hpp b/delegate/src/FullyConnected.hpp
index b79f6a2bb2..53251f7c55 100644
--- a/delegate/src/FullyConnected.hpp
+++ b/delegate/src/FullyConnected.hpp
@@ -129,6 +129,27 @@ TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData,
         biasTensorInfo = armnn::TensorInfo(armnn::TensorShape({1}), GetDataType(tfLiteInputTensor));
     }
 
+    armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
+
+    if (inputTensorInfo.GetNumDimensions() > 2)
+    {
+        // Calculate reshape to flatten to 2D [batch_size, input_size]
+        std::vector<unsigned int> reshapedDimensions(2);
+        reshapedDimensions[1] = weightsTensorInfo.GetShape()[1];
+        reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1];
+
+        if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0)
+        {
+            TF_LITE_MAYBE_KERNEL_LOG(
+                tfLiteContext,
+                "TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ",
+                reshapedDimensions[1], operatorCode, nodeIndex);
+            return kTfLiteError;
+        }
+
+        reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() });
+    }
+
     armnn::FullyConnectedDescriptor descriptor;
     descriptor.m_TransposeWeightMatrix = true;
     descriptor.m_BiasEnabled           = biasEnabled;
@@ -141,7 +162,7 @@ TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData,
                                    IsFullyConnectedSupported,
                                    delegateData.m_Backends,
                                    isSupported,
-                                   inputTensorInfo,
+                                   reshapedTensorInfo,
                                    outputTensorInfo,
                                    weightsTensorInfo,
                                    biasTensorInfo,
@@ -184,22 +205,6 @@ TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData,
     if (inputTensorInfo.GetNumDimensions() > 2)
     {
         // Add reshape to flatten to 2D [batch_size, input_size]
-        std::vector<unsigned int> reshapedDimensions(2);
-        reshapedDimensions[1] = weightsTensorInfo.GetShape()[1];
-        reshapedDimensions[0] = inputTensorInfo.GetNumElements() / reshapedDimensions[1];
-
-        if (inputTensorInfo.GetNumElements() % reshapedDimensions[1] != 0)
-        {
-            TF_LITE_MAYBE_KERNEL_LOG(
-                tfLiteContext,
-                "TfLiteArmnnDelegate: Failed to deduce input tensor shape from filter size #%d #%d node #%d: ",
-                reshapedDimensions[1], operatorCode, nodeIndex);
-            return kTfLiteError;
-        }
-
-        armnn::TensorInfo reshapedTensorInfo = GetTensorInfoForTfLiteTensor(tfLiteInputTensor);
-        reshapedTensorInfo.SetShape(armnn::TensorShape{ 2, reshapedDimensions.data() });
-
         armnn::ReshapeDescriptor reshapeDescriptor;
         reshapeDescriptor.m_TargetShape = reshapedTensorInfo.GetShape();
         reshapeLayer = delegateData.m_Network->AddReshapeLayer(reshapeDescriptor);
@@ -210,7 +215,6 @@ TfLiteStatus VisitFullyConnectedOperator(DelegateData& delegateData,
         // Connect
         delegateData.m_OutputSlotForNode[tfLiteNode->inputs->data[0]]->Connect(reshapeLayer->GetInputSlot(0));
         reshapeLayer->GetOutputSlot(0).Connect(layer->GetInputSlot(0));
-        armnn::IOutputSlot& outputSlot = layer->GetOutputSlot(0);
         delegateData.m_OutputSlotForNode[tfLiteNode->outputs->data[0]] = &outputSlot;
     }
 
diff --git a/delegate/src/test/FullyConnectedTest.cpp b/delegate/src/test/FullyConnectedTest.cpp
index 1d33381d6e..018f7f5190 100644
--- a/delegate/src/test/FullyConnectedTest.cpp
+++ b/delegate/src/test/FullyConnectedTest.cpp
@@ -8,9 +8,6 @@
 namespace
 {
 
-TEST_SUITE("FullyConnectedTest")
-{
-
 void FullyConnectedFp32Test(std::vector<armnn::BackendId>& backends)
 {
     std::vector<int32_t> inputTensorShape   { 1, 4, 1, 1 };
@@ -61,68 +58,100 @@ void FullyConnectedActicationTest(std::vector<armnn::BackendId>& backends)
                               weightsData);
 }
 
-void FullyConnectedUint8Test(std::vector<armnn::BackendId>& backends)
+void FullyConnectedInt8Test(std::vector<armnn::BackendId>& backends)
 {
     std::vector<int32_t> inputTensorShape   { 1, 4, 2, 1 };
     std::vector<int32_t> weightsTensorShape { 1, 4 };
     std::vector<int32_t> biasTensorShape    { 1 };
     std::vector<int32_t> outputTensorShape  { 2, 1 };
 
-    std::vector<uint8_t> inputValues = { 1, 2, 3, 4, 10, 20, 30, 40 };
-    std::vector<uint8_t> weightsData = { 2, 3, 4, 5 };
+    std::vector<int8_t> inputValues = { 1, 2, 3, 4, 5, 10, 15, 20 };
+    std::vector<int8_t> weightsData = { 2, 3, 4, 5 };
 
-    std::vector<uint8_t> expectedOutputValues = { (40 + 10) / 2, (400 + 10) / 2 };
+    std::vector<int8_t> expectedOutputValues = { 25, 105 };  // (40 + 10) / 2, (200 + 10) / 2
 
     // bias is set std::vector<int32_t> biasData = { 10 } in the model
     // input and weights quantization scale 1.0f and offset 0 in the model
     // output quantization scale 2.0f and offset 0 in the model
-    FullyConnectedTest<uint8_t>(backends,
-                              ::tflite::TensorType_UINT8,
-                              tflite::ActivationFunctionType_NONE,
-                              inputTensorShape,
-                              weightsTensorShape,
-                              biasTensorShape,
-                              outputTensorShape,
-                              inputValues,
-                              expectedOutputValues,
-                              weightsData);
+    FullyConnectedTest<int8_t>(backends,
+                                ::tflite::TensorType_INT8,
+                                tflite::ActivationFunctionType_NONE,
+                                inputTensorShape,
+                                weightsTensorShape,
+                                biasTensorShape,
+                                outputTensorShape,
+                                inputValues,
+                                expectedOutputValues,
+                                weightsData);
 }
 
-TEST_CASE ("FULLY_CONNECTED_FP32_GpuAcc_Test")
+TEST_SUITE("FullyConnected_GpuAccTests")
+{
+
+TEST_CASE ("FullyConnected_FP32_GpuAcc_Test")
 {
-    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
-                                               armnn::Compute::CpuRef };
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
     FullyConnectedFp32Test(backends);
 }
 
-TEST_CASE ("FULLY_CONNECTED_FP32_CpuAcc_Test")
+TEST_CASE ("FullyConnected_Int8_GpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    FullyConnectedInt8Test(backends);
+}
+
+TEST_CASE ("FullyConnected_Activation_GpuAcc_Test")
 {
-    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc,
-                                               armnn::Compute::CpuRef };
+    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc };
+    FullyConnectedActicationTest(backends);
+}
+
+} // End of TEST_SUITE("FullyConnected_GpuAccTests")
+
+TEST_SUITE("FullyConnected_CpuAccTests")
+{
+
+TEST_CASE ("FullyConnected_FP32_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
     FullyConnectedFp32Test(backends);
 }
 
-TEST_CASE ("FULLY_CONNECTED_UINT8_GpuAcc_Test")
+TEST_CASE ("FullyConnected_Int8_CpuAcc_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    FullyConnectedInt8Test(backends);
+}
+
+TEST_CASE ("FullyConnected_Activation_CpuAcc_Test")
 {
-    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
-                                               armnn::Compute::CpuRef };
-    FullyConnectedUint8Test(backends);
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuAcc };
+    FullyConnectedActicationTest(backends);
+}
+
+} // End of TEST_SUITE("FullyConnected_CpuAccTests")
+
+TEST_SUITE("FullyConnected_CpuRefTests")
+{
+
+TEST_CASE ("FullyConnected_FP32_CpuRef_Test")
+{
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    FullyConnectedFp32Test(backends);
 }
 
-TEST_CASE ("FULLY_CONNECTED_UINT8_CpuAcc_Test")
+TEST_CASE ("FullyConnected_Int8_CpuRef_Test")
 {
-    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
-                                               armnn::Compute::CpuRef };
-    FullyConnectedUint8Test(backends);
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
+    FullyConnectedInt8Test(backends);
 }
 
-TEST_CASE ("FULLY_CONNECTED_Activation_GpuAcc_Test")
+TEST_CASE ("FullyConnected_Activation_CpuRef_Test")
 {
-    std::vector<armnn::BackendId> backends = { armnn::Compute::GpuAcc,
-                                               armnn::Compute::CpuRef };
+    std::vector<armnn::BackendId> backends = { armnn::Compute::CpuRef };
     FullyConnectedActicationTest(backends);
 }
 
-} // End of TEST_SUITE("FullyConnectedTest")
+} // End of TEST_SUITE("FullyConnected_CpuRefTests")
 
 } // anonymous namespace
\ No newline at end of file
diff --git a/delegate/src/test/FullyConnectedTestHelper.hpp b/delegate/src/test/FullyConnectedTestHelper.hpp
index 4eed9580f1..4b30424d86 100644
--- a/delegate/src/test/FullyConnectedTestHelper.hpp
+++ b/delegate/src/test/FullyConnectedTestHelper.hpp
@@ -41,7 +41,7 @@ std::vector<char> CreateFullyConnectedTfLiteModel(tflite::TensorType tensorType,
                                                     sizeof(T) * weightsData.size()));
 
     auto biasTensorType = ::tflite::TensorType_FLOAT32;
-    if (tensorType == ::tflite::TensorType_UINT8)
+    if (tensorType == ::tflite::TensorType_INT8)
     {
         biasTensorType = ::tflite::TensorType_INT32;
         std::vector<int32_t> biasData = { 10 };