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author | Cathal Corbett <cathal.corbett@arm.com> | 2021-10-22 11:12:07 +0100 |
---|---|---|
committer | David Monahan <david.monahan@arm.com> | 2021-11-08 19:05:11 +0000 |
commit | 5b8093c17044e8eaaaa42d96ba4902dee5791be4 (patch) | |
tree | 7f49f91e76f171041fe51c2c078b9271aa220b48 /src/armnnSerializer/test/SerializerTests.cpp | |
parent | d69cb904415621b066599dc20164bdb71558dc14 (diff) | |
download | armnn-5b8093c17044e8eaaaa42d96ba4902dee5791be4.tar.gz |
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
Diffstat (limited to 'src/armnnSerializer/test/SerializerTests.cpp')
-rw-r--r-- | src/armnnSerializer/test/SerializerTests.cpp | 50 |
1 files changed, 25 insertions, 25 deletions
diff --git a/src/armnnSerializer/test/SerializerTests.cpp b/src/armnnSerializer/test/SerializerTests.cpp index e32b90837d..f4e25998d9 100644 --- a/src/armnnSerializer/test/SerializerTests.cpp +++ b/src/armnnSerializer/test/SerializerTests.cpp @@ -98,10 +98,10 @@ TEST_CASE("SerializeBatchNormalization") const armnn::TensorInfo inputInfo ({ 1, 3, 3, 1 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo meanInfo({1}, armnn::DataType::Float32); - const armnn::TensorInfo varianceInfo({1}, armnn::DataType::Float32); - const armnn::TensorInfo betaInfo({1}, armnn::DataType::Float32); - const armnn::TensorInfo gammaInfo({1}, armnn::DataType::Float32); + const armnn::TensorInfo meanInfo({1}, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo varianceInfo({1}, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo betaInfo({1}, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo gammaInfo({1}, armnn::DataType::Float32, 0.0f, 0, true); armnn::BatchNormalizationDescriptor descriptor; descriptor.m_Eps = 0.0010000000475f; @@ -307,7 +307,7 @@ TEST_CASE("SerializeConstant") }; const std::string layerName("constant"); - const armnn::TensorInfo info({ 2, 3 }, armnn::DataType::Float32); + const armnn::TensorInfo info({ 2, 3 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements()); armnn::ConstTensor constTensor(info, constantData); @@ -339,8 +339,8 @@ TEST_CASE("SerializeConvolution2d") const armnn::TensorInfo inputInfo ({ 1, 5, 5, 1 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); armnn::ConstTensor weights(weightsInfo, weightsData); @@ -395,10 +395,10 @@ TEST_CASE("SerializeConvolution2dWithPerAxisParams") const std::vector<float> quantScales{ 0.75f, 0.65f, 0.85f }; constexpr unsigned int quantDimension = 0; - const TensorInfo kernelInfo({ 3, 1, 1, 2 }, DataType::QSymmS8, quantScales, quantDimension); + const TensorInfo kernelInfo({ 3, 1, 1, 2 }, DataType::QSymmS8, quantScales, quantDimension, true); const std::vector<float> biasQuantScales{ 0.25f, 0.50f, 0.75f }; - const TensorInfo biasInfo({ 3 }, DataType::Signed32, biasQuantScales, quantDimension); + const TensorInfo biasInfo({ 3 }, DataType::Signed32, biasQuantScales, quantDimension, true); std::vector<int8_t> kernelData = GenerateRandomData<int8_t>(kernelInfo.GetNumElements()); armnn::ConstTensor weights(kernelInfo, kernelData); @@ -445,8 +445,8 @@ TEST_CASE("SerializeConvolution3d") const armnn::TensorInfo inputInfo ({ 1, 5, 5, 5, 1 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 1, 2, 2, 2, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 3, 3, 3, 1, 1 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); armnn::ConstTensor weights(weightsInfo, weightsData); @@ -530,8 +530,8 @@ TEST_CASE("SerializeDepthwiseConvolution2d") const armnn::TensorInfo inputInfo ({ 1, 5, 5, 3 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 1, 3, 3, 3 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); armnn::ConstTensor weights(weightsInfo, weightsData); @@ -585,11 +585,11 @@ TEST_CASE("SerializeDepthwiseConvolution2dWithPerAxisParams") const std::vector<float> quantScales{ 0.75f, 0.80f, 0.90f, 0.95f }; const unsigned int quantDimension = 0; - TensorInfo kernelInfo({ 2, 2, 2, 2 }, DataType::QSymmS8, quantScales, quantDimension); + TensorInfo kernelInfo({ 2, 2, 2, 2 }, DataType::QSymmS8, quantScales, quantDimension, true); const std::vector<float> biasQuantScales{ 0.25f, 0.35f, 0.45f, 0.55f }; constexpr unsigned int biasQuantDimension = 0; - TensorInfo biasInfo({ 4 }, DataType::Signed32, biasQuantScales, biasQuantDimension); + TensorInfo biasInfo({ 4 }, DataType::Signed32, biasQuantScales, biasQuantDimension, true); std::vector<int8_t> kernelData = GenerateRandomData<int8_t>(kernelInfo.GetNumElements()); armnn::ConstTensor weights(kernelInfo, kernelData); @@ -685,7 +685,7 @@ TEST_CASE("SerializeDeserializeDetectionPostProcess") descriptor.m_ScaleH = 5.0; descriptor.m_ScaleW = 5.0; - const armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32); + const armnn::TensorInfo anchorsInfo({ 6, 4 }, armnn::DataType::Float32, 0.0f, 0, true); const std::vector<float> anchorsData({ 0.5f, 0.5f, 1.0f, 1.0f, 0.5f, 0.5f, 1.0f, 1.0f, @@ -913,8 +913,8 @@ TEST_CASE("SerializeFullyConnected") const armnn::TensorInfo inputInfo ({ 2, 5, 1, 1 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 2, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 5, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 5, 3 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements()); armnn::ConstTensor weights(weightsInfo, weightsData); @@ -1003,8 +1003,8 @@ TEST_CASE("SerializeFullyConnectedWeightsAndBiasesAsConstantLayers") const armnn::TensorInfo inputInfo ({ 2, 5, 1, 1 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 2, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 5, 3 }, armnn::DataType::Float32); - const armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 5, 3 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 3 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); std::vector<float> biasesData = GenerateRandomData<float>(biasesInfo.GetNumElements()); @@ -1077,7 +1077,7 @@ TEST_CASE("SerializeGather") const std::string layerName("gather"); armnn::TensorInfo paramsInfo({ 8 }, armnn::DataType::QAsymmU8); armnn::TensorInfo outputInfo({ 3 }, armnn::DataType::QAsymmU8); - const armnn::TensorInfo indicesInfo({ 3 }, armnn::DataType::Signed32); + const armnn::TensorInfo indicesInfo({ 3 }, armnn::DataType::Signed32, 0.0f, 0, true); GatherDescriptor descriptor; descriptor.m_Axis = 1; @@ -2447,7 +2447,7 @@ TEST_CASE("SerializeSwitch") }; const std::string layerName("switch"); - const armnn::TensorInfo info({ 1, 4 }, armnn::DataType::Float32); + const armnn::TensorInfo info({ 1, 4 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements()); armnn::ConstTensor constTensor(info, constantData); @@ -2509,8 +2509,8 @@ TEST_CASE("SerializeTransposeConvolution2d") const armnn::TensorInfo inputInfo ({ 1, 7, 7, 1 }, armnn::DataType::Float32); const armnn::TensorInfo outputInfo({ 1, 9, 9, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32); - const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32); + const armnn::TensorInfo weightsInfo({ 1, 3, 3, 1 }, armnn::DataType::Float32, 0.0f, 0, true); + const armnn::TensorInfo biasesInfo ({ 1 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> weightsData = GenerateRandomData<float>(weightsInfo.GetNumElements()); armnn::ConstTensor weights(weightsInfo, weightsData); @@ -2594,7 +2594,7 @@ TEST_CASE("SerializeDeserializeNonLinearNetwork") }; const std::string layerName("constant"); - const armnn::TensorInfo info({ 2, 3 }, armnn::DataType::Float32); + const armnn::TensorInfo info({ 2, 3 }, armnn::DataType::Float32, 0.0f, 0, true); std::vector<float> constantData = GenerateRandomData<float>(info.GetNumElements()); armnn::ConstTensor constTensor(info, constantData); |