From 5b8093c17044e8eaaaa42d96ba4902dee5791be4 Mon Sep 17 00:00:00 2001 From: Cathal Corbett Date: Fri, 22 Oct 2021 11:12:07 +0100 Subject: 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. * 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 Change-Id: I44e440dd0422c366d31bbdbc77ad2b4db0bde148 --- src/armnn/test/OptimizerTests.cpp | 22 +++++++++++++--------- 1 file changed, 13 insertions(+), 9 deletions(-) (limited to 'src/armnn/test/OptimizerTests.cpp') 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 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 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 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 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 betaVector = { 0.1f }; std::vector gammaVector = { 0.5f }; std::vector meanVector = { 0 }; std::vector 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 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(bias); } -- cgit v1.2.1