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Diffstat (limited to 'src/armnnSerializer/test/SerializerTests.cpp')
-rw-r--r--src/armnnSerializer/test/SerializerTests.cpp50
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);
generated by cgit v1.2.1 (git 2.23.0) at 2024-05-17 06:00:30 +0000