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

1 files changed, 15 insertions, 9 deletions

diff --git a/src/armnn/test/optimizations/FoldPadTests.cpp b/src/armnn/test/optimizations/FoldPadTests.cpp
index 11f09e80e0..a598983706 100644
--- a/src/armnn/test/optimizations/FoldPadTests.cpp
+++ b/src/armnn/test/optimizations/FoldPadTests.cpp
@@ -45,7 +45,7 @@ TEST_CASE("FoldPadLayerIntoConvolution2dLayer")
     convolution2dDescriptor.m_DataLayout  = DataLayout::NHWC;
 
     std::vector<float> weightsVector(18);
-    ConstTensor        weights(TensorInfo(4, weightsShape, DataType::Float32), weightsVector);
+    ConstTensor        weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), weightsVector);
 
     Convolution2dLayer* conv2dLayer = graph.AddLayer<Convolution2dLayer>(convolution2dDescriptor, "conv2d");
     conv2dLayer->m_Weight = std::make_unique<ScopedTensorHandle>(weights);
@@ -122,7 +122,7 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConvolution2dLayer")
     depthwiseConvolution2dDescriptor.m_DataLayout  = DataLayout::NHWC;
 
     std::vector<float> weightsVector(18);
-    ConstTensor        weights(TensorInfo(4, weightsShape, DataType::Float32), weightsVector);
+    ConstTensor        weights(TensorInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true), weightsVector);
 
     auto* depthwiseConv2dLayer = graph.AddLayer<DepthwiseConvolution2dLayer>(depthwiseConvolution2dDescriptor,
                                                                              "depthwiseConv2d");
@@ -526,7 +526,9 @@ TEST_CASE("FoldPadLayerIntoPooling2dLayer_ExecuteInferenceWithAndWithoutOptimiza
         NetworkId            networkIdentifier;
         CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == Status::Success);
 
-        InputTensors inputTensors{{0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}};
+        TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
+        inputTensorInfo.SetConstant(true);
+        InputTensors inputTensors{{0, ConstTensor(inputTensorInfo, inputData.data())}};
 
         // Set the initial values of the data to different values to the golden data just in case the inference fails.
         std::vector<float> optimizedData(32, -std::numeric_limits<float>::infinity());
@@ -614,10 +616,10 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio
                                               11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
                                               21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
                                               31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};
-        TensorInfo            weightsInfo(4, weightsShape, DataType::Float32);
+        TensorInfo            weightsInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true);
         ConstTensor           weights(weightsInfo, weightsData);
         std::vector<float>    biasVector   = {5, 6, 7, 8};
-        TensorInfo            biasInfo({4}, DataType::Float32);
+        TensorInfo            biasInfo({4}, DataType::Float32, 0.0f, 0, true);
         ConstTensor           bias(biasInfo, biasVector);
         Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
 
@@ -644,7 +646,9 @@ TEST_CASE("FoldPadLayerIntoConv2dLayer_ExecuteInferenceWithAndWithoutOptimizatio
         NetworkId            networkIdentifier;
         CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == Status::Success);
 
-        InputTensors inputTensors{{0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}};
+        TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
+        inputTensorInfo.SetConstant(true);
+        InputTensors inputTensors{{0, ConstTensor(inputTensorInfo, inputData.data())}};
 
         // Set the initial values of the data to different values to the golden data just in case the inference fails.
         std::vector<float> optimizedData(100, -std::numeric_limits<float>::infinity());
@@ -732,10 +736,10 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConv2dLayer_ExecuteInferenceWithAndWithoutOp
                                               11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,
                                               21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
                                               31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42};
-        TensorInfo            weightsInfo(4, weightsShape, DataType::Float32);
+        TensorInfo            weightsInfo(4, weightsShape, DataType::Float32, 0.0f, 0, true);
         ConstTensor           weights(weightsInfo, weightsData);
         std::vector<float>    biasVector   = {5, 6, 7, 8, 9, 10, 11, 12, 5, 6, 7, 8};
-        TensorInfo            biasInfo({12}, DataType::Float32);
+        TensorInfo            biasInfo({12}, DataType::Float32, 0.0f, 0, true);
         ConstTensor           bias(biasInfo, biasVector);
         Optional<ConstTensor> optionalBias = Optional<ConstTensor>(bias);
 
@@ -762,7 +766,9 @@ TEST_CASE("FoldPadLayerIntoDepthwiseConv2dLayer_ExecuteInferenceWithAndWithoutOp
         NetworkId            networkIdentifier;
         CHECK(run->LoadNetwork(networkIdentifier, std::move(optimizedNetwork)) == Status::Success);
 
-        InputTensors inputTensors{{0, ConstTensor(run->GetInputTensorInfo(networkIdentifier, 0), inputData.data())}};
+        TensorInfo inputTensorInfo = run->GetInputTensorInfo(networkIdentifier, 0);
+        inputTensorInfo.SetConstant(true);
+        InputTensors inputTensors{{0, ConstTensor(inputTensorInfo, inputData.data())}};
 
         // Set the initial values of the data to different values to the golden data just in case the inference fails.
         std::vector<float> optimizedData(300, -std::numeric_limits<float>::infinity());