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, 13 insertions, 9 deletions

diff --git a/src/armnn/test/OptimizerTests.cpp b/src/armnn/test/OptimizerTests.cpp
index 3cea1b540e..750e6967ad 100644
--- a/src/armnn/test/OptimizerTests.cpp
+++ b/src/armnn/test/OptimizerTests.cpp
@@ -405,7 +405,9 @@ void CreateConvolution2dGraph(Graph &graph, const unsigned int* inputShape,
     armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32);
 
     std::vector<float> weightsVector(90);
-    armnn::ConstTensor weights(armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32), weightsVector);
+    armnn::ConstTensor weights(
+            armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32, 0.0f, 0, true),
+            weightsVector);
 
     Convolution2dDescriptor desc;
     desc.m_BiasEnabled = false;
@@ -455,7 +457,9 @@ void CreateDepthwiseConvolution2dGraph(Graph &graph, const unsigned int* inputSh
     armnn::TensorInfo outputInfo(4, outputShape, DataType::Float32);
 
     std::vector<float> weightsVector(18);
-    armnn::ConstTensor weights(armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32), weightsVector);
+    armnn::ConstTensor weights(
+            armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32, 0.0f, 0, true),
+            weightsVector);
 
     DepthwiseConvolution2dDescriptor desc;
     desc.m_BiasEnabled = false;
@@ -653,7 +657,7 @@ TEST_CASE("DetectionPostProcessValidateTensorShapes")
     armnn::TensorInfo boxEncodingsInfo({1, 10, 4}, DataType::QAsymmU8);
     armnn::TensorInfo scoresInfo({1, 10, 4}, DataType::QAsymmU8);
     std::vector<uint8_t> anchorsVector(40);
-    armnn::ConstTensor anchors(armnn::TensorInfo({10, 4}, armnn::DataType::QAsymmU8), anchorsVector);
+    armnn::ConstTensor anchors(armnn::TensorInfo({10, 4}, armnn::DataType::QAsymmU8, 0.0f, 0, true), anchorsVector);
 
     armnn::TensorInfo detectionBoxesInfo({1, 3, 4}, DataType::QAsymmU8);
     armnn::TensorInfo detectionScoresInfo({1, 3}, DataType::QAsymmU8);
@@ -833,16 +837,16 @@ TEST_CASE("OptimizeForExclusiveConnectionsFuseTest")
     TensorInfo outputInfo(4, outputDimensionSizes, DataType::Float32);
 
     std::vector<float> weightsVector = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 };
-    ConstTensor weights(TensorInfo(4, weightsDimensionSizes, DataType::Float32), weightsVector);
+    ConstTensor weights(TensorInfo(4, weightsDimensionSizes, DataType::Float32, 0.0f, 0, true), weightsVector);
 
     std::vector<float> betaVector     = { 0.1f };
     std::vector<float> gammaVector    = { 0.5f };
     std::vector<float> meanVector     = { 0 };
     std::vector<float> varianceVector = { 1 };
-    ConstTensor beta(TensorInfo(1, outputChannelSize, DataType::Float32), betaVector);
-    ConstTensor gamma(TensorInfo(1, outputChannelSize, DataType::Float32), gammaVector);
-    ConstTensor mean(TensorInfo(1, outputChannelSize, DataType::Float32), meanVector);
-    ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32), varianceVector);
+    ConstTensor beta(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), betaVector);
+    ConstTensor gamma(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), gammaVector);
+    ConstTensor mean(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), meanVector);
+    ConstTensor variance(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), varianceVector);
 
     // Define the network
     Graph graph;
@@ -863,7 +867,7 @@ TEST_CASE("OptimizeForExclusiveConnectionsFuseTest")
     if (convolution2dDescriptor.m_BiasEnabled)
     {
         std::vector<float> biasVector = { 11 };
-        ConstTensor bias(TensorInfo(1, outputChannelSize, DataType::Float32), biasVector);
+        ConstTensor bias(TensorInfo(1, outputChannelSize, DataType::Float32, 0.0f, 0, true), biasVector);
         conv->m_Bias = std::make_unique<ScopedTensorHandle>(bias);
     }