IVGCVSW-6420: Constant flag in tensor info is not set correctly

!android-nn-driver:6532
!armnn-internal-tests:372451

  * Made fix to 2 out of 3 ConstTensor() constructors in Tensor.hpp to
    throw InvalidArgumentException when TensorInfo isConstant parameter
    is false.
  * Added new ConstTensor() constructor in Tensor.cpp to accept vector<>.data()
    using template<typename MemoryType>.
  * Fixed runtime->GetOutputTensorInfo()/GetInputTensorInfo() methods and
    called submethods to return TensorInfo& rather than TensorInfo.
  * Fixed all failing unit tests for CpuRef/CpuAcc/GpuAcc to ensure any
    ConstTensor created has it's TensorInfo isConstant set to true.
  * Added unit tests in TensorTest.cpp to ensure ConstTensor constructors
    throw InvalidArgumentException when TensorInfo isConstat parameter is
    false.
  * Added unit test to ensure an empty ConstTensor constructor will set
    TensorInfo isConatant to true.
  * Indentation fixes.
  * Fix to arm_tensor.i to add isConstant parameter to TensorInfo
    constructor. Added methods IsConstant() and SetConstant().
  * Fix to const_tensor.py to throw ValueError when TensorInfo
    isConstant is set to false when constructing a ConstTensor.
  * Fixed PyArmnn unit tests to set TensorInfo isConstant to
    True when ConstTensor is used.
  * Added unit tests in test_const_tensor.py to ensure ConstTensor
    constructors throw ValueError when TensorInfo isConstat parameter
    is false.

Signed-off-by: Cathal Corbett <cathal.corbett@arm.com>
Change-Id: I44e440dd0422c366d31bbdbc77ad2b4db0bde148

4 files changed, 19 insertions, 14 deletions

diff --git a/samples/AsyncExecutionSample.cpp b/samples/AsyncExecutionSample.cpp
index 6d2fe243dd..a789aade01 100644
--- a/samples/AsyncExecutionSample.cpp
+++ b/samples/AsyncExecutionSample.cpp
@@ -49,7 +49,7 @@ int main()
     INetworkPtr myNetwork = INetwork::Create();
 
     float weightsData[] = {1.0f}; // Identity
-    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32);
+    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true);
     weightsInfo.SetConstant();
     ConstTensor weights(weightsInfo, weightsData);
 
@@ -104,11 +104,12 @@ int main()
     std::vector<std::vector<float>> outputData;
     outputData.resize(2, std::vector<float>(1));
 
-
+    inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
+    inputTensorInfo.SetConstant(true);
     std::vector<InputTensors> inputTensors
     {
-        {{0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData[0].data())}},
-        {{0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData[1].data())}}
+        {{0, armnn::ConstTensor(inputTensorInfo, inputData[0].data())}},
+        {{0, armnn::ConstTensor(inputTensorInfo, inputData[1].data())}}
     };
     std::vector<OutputTensors> outputTensors
     {
diff --git a/samples/CustomMemoryAllocatorSample.cpp b/samples/CustomMemoryAllocatorSample.cpp
index 171d8e2b5d..a1b05d4be0 100644
--- a/samples/CustomMemoryAllocatorSample.cpp
+++ b/samples/CustomMemoryAllocatorSample.cpp
@@ -78,7 +78,7 @@ int main()
     INetworkPtr myNetwork = INetwork::Create();
     armnn::FullyConnectedDescriptor fullyConnectedDesc;
     float weightsData[] = {1.0f}; // Identity
-    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32);
+    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true);
     weightsInfo.SetConstant(true);
     armnn::ConstTensor weights(weightsInfo, weightsData);
     ARMNN_NO_DEPRECATE_WARN_BEGIN
@@ -152,10 +152,11 @@ int main()
     auto* outputPtr = reinterpret_cast<float*>(alignedOutputPtr);
     std::fill_n(outputPtr, numElements, -10.0f);
 
-
+    inputTensorInfo = runtime->GetInputTensorInfo(networkIdentifier, 0);
+    inputTensorInfo.SetConstant(true);
     armnn::InputTensors inputTensors
     {
-        {0, armnn::ConstTensor(runtime->GetInputTensorInfo(networkIdentifier, 0), alignedInputPtr)},
+        {0, armnn::ConstTensor(inputTensorInfo, alignedInputPtr)},
     };
     armnn::OutputTensors outputTensors
     {
diff --git a/samples/DynamicSample.cpp b/samples/DynamicSample.cpp
index ffcc9de083..8a6ff92706 100644
--- a/samples/DynamicSample.cpp
+++ b/samples/DynamicSample.cpp
@@ -62,10 +62,12 @@ int main()
         };
     std::vector<float> outputData(2);
 
+    TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
+    inputTensorInfo.SetConstant(true);
     InputTensors inputTensors
         {
-            {0,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input0Data.data())},
-            {1,armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), input1Data.data())}
+            {0,armnn::ConstTensor(inputTensorInfo, input0Data.data())},
+            {1,armnn::ConstTensor(inputTensorInfo, 0), input1Data.data())}
         };
     OutputTensors outputTensors
         {
diff --git a/samples/SimpleSample.cpp b/samples/SimpleSample.cpp
index 3f94b53ca1..01f078bd56 100644
--- a/samples/SimpleSample.cpp
+++ b/samples/SimpleSample.cpp
@@ -28,7 +28,7 @@ int main()
     INetworkPtr myNetwork = INetwork::Create();
 
     float weightsData[] = {1.0f}; // Identity
-    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32);
+    TensorInfo weightsInfo(TensorShape({1, 1}), DataType::Float32, 0.0f, 0, true);
     weightsInfo.SetConstant();
     ConstTensor weights(weightsInfo, weightsData);
 
@@ -75,11 +75,12 @@ int main()
     std::vector<float> inputData{number};
     std::vector<float> outputData(1);
 
-
-    InputTensors inputTensors{{0, armnn::ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0),
-                                                            inputData.data())}};
+    inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
+    inputTensorInfo.SetConstant(true);
+    InputTensors inputTensors{{0, armnn::ConstTensor(inputTensorInfo,
+                                                     inputData.data())}};
     OutputTensors outputTensors{{0, armnn::Tensor(run->GetOutputTensorInfo(networkIdentifier, 0),
-                                                         outputData.data())}};
+                                                  outputData.data())}};
 
     // Execute network
     run->EnqueueWorkload(networkIdentifier, inputTensors, outputTensors);