diff options
Diffstat (limited to 'src/armnn/test/CreateWorkload.hpp')
| -rw-r--r-- | src/armnn/test/CreateWorkload.hpp | 557 |
1 files changed, 283 insertions, 274 deletions
diff --git a/src/armnn/test/CreateWorkload.hpp b/src/armnn/test/CreateWorkload.hpp index 12623e62a0..581c621a16 100644 --- a/src/armnn/test/CreateWorkload.hpp +++ b/src/armnn/test/CreateWorkload.hpp @@ -11,6 +11,7 @@ #include <ResolveType.hpp> #include <armnnUtils/DataLayoutIndexed.hpp> +#include <armnn/utility/Assert.hpp> #include <armnn/utility/IgnoreUnused.hpp> #include <armnn/utility/PolymorphicDowncast.hpp> @@ -18,7 +19,7 @@ #include <backendsCommon/WorkloadData.hpp> #include <backendsCommon/WorkloadFactory.hpp> -#include <boost/test/unit_test.hpp> +#include <doctest/doctest.h> #include <utility> @@ -36,11 +37,11 @@ std::unique_ptr<Workload> MakeAndCheckWorkload(Layer& layer, const ModelOptions& modelOptions = {}) { std::unique_ptr<IWorkload> workload = layer.CreateWorkload(factory); - BOOST_TEST(workload.get() == PolymorphicDowncast<Workload*>(workload.get()), + CHECK_MESSAGE(workload.get() == PolymorphicDowncast<Workload*>(workload.get()), "Cannot convert to derived class"); std::string reasonIfUnsupported; layer.SetBackendId(factory.GetBackendId()); - BOOST_TEST(factory.IsLayerSupported(layer, layer.GetDataType(), reasonIfUnsupported, modelOptions)); + CHECK(factory.IsLayerSupported(layer, layer.GetDataType(), reasonIfUnsupported, modelOptions)); return std::unique_ptr<Workload>(static_cast<Workload*>(workload.release())); } @@ -90,11 +91,11 @@ std::unique_ptr<ActivationWorkload> CreateActivationWorkloadTest(armnn::IWorkloa auto workload = MakeAndCheckWorkload<ActivationWorkload>(*layer, factory); ActivationQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_A == 3.5f); - BOOST_TEST(queueDescriptor.m_Parameters.m_B == -10.0f); - BOOST_TEST((queueDescriptor.m_Parameters.m_Function == ActivationFunction::Abs)); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Parameters.m_A == 3.5f); + CHECK(queueDescriptor.m_Parameters.m_B == -10.0f); + CHECK((queueDescriptor.m_Parameters.m_Function == ActivationFunction::Abs)); // Returns so we can do extra, backend-specific tests. return workload; @@ -126,8 +127,8 @@ std::unique_ptr<WorkloadType> CreateElementwiseWorkloadTest(armnn::IWorkloadFact auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); DescriptorType queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 2); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -165,9 +166,9 @@ std::unique_ptr<WorkloadType> CreateSubtractionWithBlobWorkloadTest(armnn::IWork std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -179,14 +180,14 @@ std::unique_ptr<WorkloadType> CreateSubtractionWithBlobWorkloadTest(armnn::IWork const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>(); IgnoreUnused(queueDescBlobPtr); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 2); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); return workload; } @@ -223,9 +224,9 @@ std::unique_ptr<WorkloadType> CreateMultiplicationWithBlobWorkloadTest(armnn::IW std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -233,14 +234,14 @@ std::unique_ptr<WorkloadType> CreateMultiplicationWithBlobWorkloadTest(armnn::IW auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); DescriptorType queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 2); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>(); IgnoreUnused(queueDescBlobPtr); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -279,9 +280,9 @@ std::unique_ptr<WorkloadType> CreateAdditionWithBlobWorkloadTest(armnn::IWorkloa std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->template GetAdditionalInformation<ActivationDescriptor>(); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -292,11 +293,11 @@ std::unique_ptr<WorkloadType> CreateAdditionWithBlobWorkloadTest(armnn::IWorkloa const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.template GetAdditionalInformation<ActivationDescriptor>(); IgnoreUnused(queueDescBlobPtr); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 2); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); - BOOST_ASSERT( + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -324,8 +325,8 @@ std::unique_ptr<WorkloadType> CreateElementwiseUnaryWorkloadTest(armnn::IWorkloa auto workload = MakeAndCheckWorkload<WorkloadType>(*layer, factory); DescriptorType queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); return workload; } @@ -375,14 +376,14 @@ std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWorkload // Makes the workload and checks it. auto workload = MakeAndCheckWorkload<BatchNormalizationWorkloadType>(*layer, factory); BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_Eps == 0.05f); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK(queueDescriptor.m_Parameters.m_Eps == 0.05f); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); // Returns so we can do extra, backend-specific tests. return workload; @@ -429,9 +430,9 @@ std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWithBlob // Check that the additional information can be queried from the layer std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -450,20 +451,20 @@ std::unique_ptr<BatchNormalizationWorkloadType> CreateBatchNormalizationWithBlob BatchNormalizationQueueDescriptor queueDescriptor = workload->GetData(); const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>(); IgnoreUnused(queueDescBlobPtr); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); - BOOST_TEST(queueDescriptor.m_Parameters.m_Eps == 0.05f); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType))); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK(queueDescriptor.m_Parameters.m_Eps == 0.05f); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Mean->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Variance->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Gamma->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Beta->GetTensorInfo() == TensorInfo({3}, DataType))); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); // Returns so we can do extra, backend-specific tests. return workload; @@ -511,19 +512,19 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadTest(armnn::IW auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions); Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 4); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); - BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() == + CHECK(queueDescriptor.m_Parameters.m_StrideX == 2); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 4); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 3); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 3); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({2}, GetBiasDataType(DataType)))); // Returns so we can do extra, backend-specific tests. @@ -571,9 +572,9 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dFusedActivationWithBlo // Check that the additional information can be queried from the layer std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); @@ -592,25 +593,25 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dFusedActivationWithBlo Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>(); IgnoreUnused(queueDescBlobPtr); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 4); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); - BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() == + CHECK(queueDescriptor.m_Parameters.m_StrideX == 2); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 4); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 3); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 3); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({2}, GetBiasDataType(DataType)))); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -658,17 +659,17 @@ std::unique_ptr<Convolution2dWorkload> CreateConvolution2dWorkloadFastMathTest(a auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory, modelOptions); Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 0); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 0); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 0); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 0); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 1); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 1); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 0); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 0); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 0); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 0); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo(weightShape, DataType))); // Returns so we can do extra, backend-specific tests. return workload; @@ -760,17 +761,17 @@ std::unique_ptr<LstmWorkload> CreateLstmWorkloadTest(armnn::IWorkloadFactory& fa // make the workload and check it auto workload = MakeAndCheckWorkload<LstmWorkload>(*layer, factory); LstmQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_ActivationFunc == 4); - BOOST_TEST(queueDescriptor.m_Parameters.m_ClippingThresCell == 0.0f); - BOOST_TEST(queueDescriptor.m_Parameters.m_ClippingThresProj == 0.0f); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 3); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 4); + CHECK(queueDescriptor.m_Parameters.m_ActivationFunc == 4); + CHECK(queueDescriptor.m_Parameters.m_ClippingThresCell == 0.0f); + CHECK(queueDescriptor.m_Parameters.m_ClippingThresProj == 0.0f); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 4); - BOOST_TEST((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == TensorInfo({ numUnits, inputSize }, + CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == TensorInfo({ numUnits, inputSize }, DataType::Float32))); - BOOST_TEST((queueDescriptor.m_OutputGateBias->GetTensorInfo() == TensorInfo({ numUnits }, + CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == TensorInfo({ numUnits }, DataType::Float32))); - BOOST_TEST((queueDescriptor.m_CellBias->GetTensorInfo() == TensorInfo({ numUnits }, DataType::Float32))); + CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == TensorInfo({ numUnits }, DataType::Float32))); return workload; } @@ -891,24 +892,24 @@ std::unique_ptr<QuantizedLstmWorkload> CreateQuantizedLstmWorkloadTest(armnn::IW QuantizedLstmQueueDescriptor queueDescriptor = workload->GetData(); // Validate input/output sizes - BOOST_TEST(queueDescriptor.m_Inputs.size() == 3); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 2); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 2); // Validate weight tensor info - BOOST_TEST((queueDescriptor.m_InputToInputWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToInputWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToInputWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToInputWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_InputGateBias->GetTensorInfo() == biasInfo)); - BOOST_TEST((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo)); - BOOST_TEST((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo)); - BOOST_TEST((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_InputGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo)); return workload; } @@ -1054,22 +1055,22 @@ std::unique_ptr<QLstmWorkload> CreateQLstmWorkloadTest(armnn::IWorkloadFactory& // Create and check workload auto workload = MakeAndCheckWorkload<QLstmWorkload>(*layer, factory); QLstmQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_CellClip == 0.0f); - BOOST_TEST(queueDescriptor.m_Parameters.m_ProjectionClip == 0.0f); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 3); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 3); + CHECK(queueDescriptor.m_Parameters.m_CellClip == 0.0f); + CHECK(queueDescriptor.m_Parameters.m_ProjectionClip == 0.0f); + CHECK(queueDescriptor.m_Inputs.size() == 3); + CHECK(queueDescriptor.m_Outputs.size() == 3); - BOOST_TEST((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToForgetWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToCellWeights->GetTensorInfo() == inputWeightsInfo)); + CHECK((queueDescriptor.m_InputToOutputWeights->GetTensorInfo() == inputWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToForgetWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToCellWeights->GetTensorInfo() == recurrentWeightsInfo)); + CHECK((queueDescriptor.m_RecurrentToOutputWeights->GetTensorInfo() == recurrentWeightsInfo)); - BOOST_TEST((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo)); - BOOST_TEST((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo)); - BOOST_TEST((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_ForgetGateBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_CellBias->GetTensorInfo() == biasInfo)); + CHECK((queueDescriptor.m_OutputGateBias->GetTensorInfo() == biasInfo)); return workload; } @@ -1112,19 +1113,19 @@ std::unique_ptr<Convolution2dWorkload> CreateDirectConvolution2dWorkloadTest(arm auto workload = MakeAndCheckWorkload<Convolution2dWorkload>(*layer, factory); Convolution2dQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == true); - - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({2, 3, 3, 3}, + CHECK(queueDescriptor.m_Parameters.m_StrideX == 1); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 1); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 1); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 1); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({2, 3, 3, 3}, DataType, inputsQScale))); - BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({2}, GetBiasDataType(DataType), inputsQScale))); // Returns so we can do extra, backend-specific tests. @@ -1169,18 +1170,18 @@ std::unique_ptr<DepthwiseConvolution2dFloat32Workload> CreateDepthwiseConvolutio auto workload = MakeAndCheckWorkload<DepthwiseConvolution2dFloat32Workload>(*layer, factory); DepthwiseConvolution2dQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == false); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({1, 2, 4, 4}, DataType))); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 1); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 1); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 1); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 2); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 2); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == false); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({1, 2, 4, 4}, DataType))); // Returns so we can do extra, backend-specific tests. return workload; @@ -1218,13 +1219,13 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWorkloadTest(armnn:: auto workload = MakeAndCheckWorkload<FullyConnectedWorkload>(*layer, factory); FullyConnectedQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == true); - BOOST_TEST(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true); + CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({7, 20}, DataType, inputsQScale))); - BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({7}, GetBiasDataType(DataType), inputsQScale))); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({7, 20}, DataType, inputsQScale))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({7}, GetBiasDataType(DataType), inputsQScale))); // Returns so we can do extra, backend-specific tests. return workload; @@ -1259,9 +1260,9 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest // Check that the additional information can be queried from the layer std::shared_ptr<ActivationDescriptor> activationDescPtr = layer->GetAdditionalInformation<ActivationDescriptor>(); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); - BOOST_ASSERT(static_cast<ActivationFunction>(activationDescPtr->m_Function) == + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(activationDescPtr->m_B) == 5.0f); + ARMNN_ASSERT(static_cast<ActivationFunction>(activationDescPtr->m_Function) == armnn::ActivationFunction::BoundedReLu); // Creates extra layers. @@ -1281,18 +1282,18 @@ std::unique_ptr<FullyConnectedWorkload> CreateFullyConnectedWithBlobWorkloadTest const ActivationDescriptor* queueDescBlobPtr = queueDescriptor.GetAdditionalInformation<ActivationDescriptor>(); IgnoreUnused(queueDescBlobPtr); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); - BOOST_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); - BOOST_ASSERT( + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_A) == 10.0f); + ARMNN_ASSERT(static_cast<float>(queueDescBlobPtr->m_B) == 5.0f); + ARMNN_ASSERT( static_cast<ActivationFunction>(queueDescBlobPtr->m_Function) == armnn::ActivationFunction::BoundedReLu ); - BOOST_TEST(queueDescriptor.m_Parameters.m_BiasEnabled == true); - BOOST_TEST(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); - BOOST_TEST((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({7, 20}, DataType, inputsQScale))); - BOOST_TEST((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({7}, GetBiasDataType(DataType), inputsQScale))); + CHECK(queueDescriptor.m_Parameters.m_BiasEnabled == true); + CHECK(queueDescriptor.m_Parameters.m_TransposeWeightMatrix == true); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Weight->GetTensorInfo() == TensorInfo({7, 20}, DataType, inputsQScale))); + CHECK((queueDescriptor.m_Bias->GetTensorInfo() == TensorInfo({7}, GetBiasDataType(DataType), inputsQScale))); // Returns so we can do extra, backend-specific tests. return workload; @@ -1336,16 +1337,16 @@ std::unique_ptr<NormalizationWorkload> CreateNormalizationWorkloadTest(armnn::IW auto workload = MakeAndCheckWorkload<NormalizationWorkload>(*layer, factory); NormalizationQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST((queueDescriptor.m_Parameters.m_NormChannelType == NormalizationAlgorithmChannel::Across)); - BOOST_TEST((queueDescriptor.m_Parameters.m_NormMethodType == NormalizationAlgorithmMethod::LocalBrightness)); - BOOST_TEST(queueDescriptor.m_Parameters.m_NormSize == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_Alpha == 0.5f); - BOOST_TEST(queueDescriptor.m_Parameters.m_Beta == -1.0f); - BOOST_TEST(queueDescriptor.m_Parameters.m_K == 0.2f); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK((queueDescriptor.m_Parameters.m_NormChannelType == NormalizationAlgorithmChannel::Across)); + CHECK((queueDescriptor.m_Parameters.m_NormMethodType == NormalizationAlgorithmMethod::LocalBrightness)); + CHECK(queueDescriptor.m_Parameters.m_NormSize == 3); + CHECK(queueDescriptor.m_Parameters.m_Alpha == 0.5f); + CHECK(queueDescriptor.m_Parameters.m_Beta == -1.0f); + CHECK(queueDescriptor.m_Parameters.m_K == 0.2f); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -1388,20 +1389,20 @@ std::unique_ptr<Pooling2dWorkload> CreatePooling2dWorkloadTest(armnn::IWorkloadF auto workload = MakeAndCheckWorkload<Pooling2dWorkload>(*layer, factory); Pooling2dQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST((queueDescriptor.m_Parameters.m_PoolType == PoolingAlgorithm::Average)); - BOOST_TEST((queueDescriptor.m_Parameters.m_OutputShapeRounding == OutputShapeRounding::Floor)); - BOOST_TEST(queueDescriptor.m_Parameters.m_PoolWidth == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PoolHeight == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideX == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_StrideY == 3); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadLeft == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadRight == 2); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadTop == 1); - BOOST_TEST(queueDescriptor.m_Parameters.m_PadBottom == 1); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Parameters.m_PoolType == PoolingAlgorithm::Average)); + CHECK((queueDescriptor.m_Parameters.m_OutputShapeRounding == OutputShapeRounding::Floor)); + CHECK(queueDescriptor.m_Parameters.m_PoolWidth == 3); + CHECK(queueDescriptor.m_Parameters.m_PoolHeight == 3); + CHECK(queueDescriptor.m_Parameters.m_StrideX == 2); + CHECK(queueDescriptor.m_Parameters.m_StrideY == 3); + CHECK(queueDescriptor.m_Parameters.m_PadLeft == 2); + CHECK(queueDescriptor.m_Parameters.m_PadRight == 2); + CHECK(queueDescriptor.m_Parameters.m_PadTop == 1); + CHECK(queueDescriptor.m_Parameters.m_PadBottom == 1); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Return so we can do extra, backend-specific tests return workload; @@ -1445,8 +1446,8 @@ std::unique_ptr<SoftmaxWorkload> CreateSoftmaxWorkloadTest(armnn::IWorkloadFacto auto workload = MakeAndCheckWorkload<SoftmaxWorkload>(*layer, factory); SoftmaxQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Return so we can do extra, backend-specific tests. return workload; @@ -1494,19 +1495,19 @@ std::unique_ptr<SplitterWorkload> auto workload = MakeAndCheckWorkload<SplitterWorkload>(*layer, factory); SplitterQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 3); - BOOST_TEST(queueDescriptor.m_ViewOrigins.size() == 3); - - BOOST_TEST(queueDescriptor.m_ViewOrigins[0].m_Origin[0] == 0); - BOOST_TEST(queueDescriptor.m_ViewOrigins[1].m_Origin[0] == 1); - BOOST_TEST(queueDescriptor.m_ViewOrigins[2].m_Origin[0] == 3); - BOOST_TEST(queueDescriptor.m_ViewOrigins[0].m_Origin[1] == 0); - BOOST_TEST(queueDescriptor.m_ViewOrigins[1].m_Origin[1] == 0); - BOOST_TEST(queueDescriptor.m_ViewOrigins[2].m_Origin[1] == 0); - BOOST_TEST(queueDescriptor.m_ViewOrigins[0].m_Origin[2] == 0); - BOOST_TEST(queueDescriptor.m_ViewOrigins[1].m_Origin[2] == 0); - BOOST_TEST(queueDescriptor.m_ViewOrigins[2].m_Origin[2] == 0); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 3); + CHECK(queueDescriptor.m_ViewOrigins.size() == 3); + + CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[0] == 0); + CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[0] == 1); + CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[0] == 3); + CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[1] == 0); + CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[1] == 0); + CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[1] == 0); + CHECK(queueDescriptor.m_ViewOrigins[0].m_Origin[2] == 0); + CHECK(queueDescriptor.m_ViewOrigins[1].m_Origin[2] == 0); + CHECK(queueDescriptor.m_ViewOrigins[2].m_Origin[2] == 0); // Returns so we can do extra, backend-specific tests. return workload; @@ -1536,8 +1537,9 @@ std::pair<std::unique_ptr<SplitterWorkload>, std::unique_ptr<ConcatWorkload>> splitterViews.SetViewOriginCoord(1, 2, 0); splitterViews.SetViewOriginCoord(1, 3, 0); + // create splitter layer Layer* const splitter = graph.AddLayer<SplitterLayer>(splitterViews, "splitter"); - BOOST_TEST_CHECKPOINT("created splitter layer"); + CHECK(splitter); armnn::OriginsDescriptor concatViews(2); concatViews.SetViewOriginCoord(0, 0, 0); @@ -1550,28 +1552,31 @@ std::pair<std::unique_ptr<SplitterWorkload>, std::unique_ptr<ConcatWorkload>> concatViews.SetViewOriginCoord(1, 2, 0); concatViews.SetViewOriginCoord(1, 3, 0); + // create concat layer Layer* const concat = graph.AddLayer<ConcatLayer>(concatViews, "concat"); - BOOST_TEST_CHECKPOINT("created concat layer"); + CHECK(concat); Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); // Adds connections. + // connect input to splitter Connect(input, splitter, inputTensorInfo, 0, 0); - BOOST_TEST_CHECKPOINT("connect input to splitter"); + // connect splitter[0] to concat[1] Connect(splitter, concat, splitTensorInfo1, 0, 1); // The splitter & concat are connected up. - BOOST_TEST_CHECKPOINT("connect splitter[0] to concat[1]"); + // connect splitter[1] to concat[0] Connect(splitter, concat, splitTensorInfo2, 1, 0); // So that the outputs are flipped round. - BOOST_TEST_CHECKPOINT("connect splitter[1] to concat[0]"); + // connect concat to output Connect(concat, output, inputTensorInfo, 0, 0); - BOOST_TEST_CHECKPOINT("connect concat to output"); + // created tensor handles CreateTensorHandles(graph, factory); - BOOST_TEST_CHECKPOINT("created tensor handles"); + // created splitter workload auto workloadSplitter = MakeAndCheckWorkload<SplitterWorkload>(*splitter, factory); - BOOST_TEST_CHECKPOINT("created splitter workload"); + CHECK(workloadSplitter); + // created concat workload auto workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory); - BOOST_TEST_CHECKPOINT("created concat workload"); + CHECK(workloadConcat); return {std::move(workloadSplitter), std::move(workloadConcat)}; } @@ -1691,9 +1696,9 @@ std::unique_ptr<ResizeWorkload> CreateResizeBilinearWorkloadTest(armnn::IWorkloa auto workload = MakeAndCheckWorkload<ResizeWorkload>(*layer, factory); auto queueDescriptor = workload->GetData(); - BOOST_CHECK(queueDescriptor.m_Inputs.size() == 1); - BOOST_CHECK(queueDescriptor.m_Outputs.size() == 1); - BOOST_CHECK(queueDescriptor.m_Parameters.m_DataLayout == dataLayout); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Parameters.m_DataLayout == dataLayout); // Returns so we can do extra, backend-specific tests. return workload; @@ -1722,8 +1727,8 @@ std::unique_ptr<BatchToSpaceNdWorkload> CreateBatchToSpaceNdWorkloadTest(armnn:: auto workload = MakeAndCheckWorkload<BatchToSpaceNdWorkload>(*layer, factory); BatchToSpaceNdQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); return workload; } @@ -1756,8 +1761,8 @@ std::unique_ptr<LogSoftmaxWorkload> CreateLogSoftmaxWorkloadTest(armnn::IWorkloa auto workload = MakeAndCheckWorkload<LogSoftmaxWorkload>(*layer, factory); LogSoftmaxQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Return so we can do extra, backend-specific tests. return workload; @@ -1793,9 +1798,9 @@ std::unique_ptr<L2NormalizationWorkload> CreateL2NormalizationWorkloadTest(armnn auto workload = MakeAndCheckWorkload<L2NormalizationWorkload>(*layer, factory); L2NormalizationQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK((queueDescriptor.m_Parameters.m_DataLayout == dataLayout)); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -1826,8 +1831,8 @@ std::unique_ptr<ReshapeWorkload> CreateReshapeWorkloadTest(armnn::IWorkloadFacto auto workload = MakeAndCheckWorkload<ReshapeWorkload>(*layer, factory); ReshapeQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -1855,8 +1860,8 @@ std::unique_ptr<ConvertFp16ToFp32Float32Workload> CreateConvertFp16ToFp32Workloa auto workload = MakeAndCheckWorkload<ConvertFp16ToFp32Float32Workload>(*layer, factory); ConvertFp16ToFp32QueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -1884,8 +1889,8 @@ std::unique_ptr<ConvertFp32ToFp16Float16Workload> CreateConvertFp32ToFp16Workloa auto workload = MakeAndCheckWorkload<ConvertFp32ToFp16Float16Workload>(*layer, factory); ConvertFp32ToFp16QueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -1915,10 +1920,10 @@ std::unique_ptr<MeanWorkload> CreateMeanWorkloadTest(armnn::IWorkloadFactory& fa auto workload = MakeAndCheckWorkload<MeanWorkload>(*layer, factory); MeanQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Parameters.m_Axis == descriptor.m_Axis); - BOOST_TEST(queueDescriptor.m_Parameters.m_KeepDims == descriptor.m_KeepDims); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Parameters.m_Axis == descriptor.m_Axis); + CHECK(queueDescriptor.m_Parameters.m_KeepDims == descriptor.m_KeepDims); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -1944,24 +1949,26 @@ std::unique_ptr<ConcatWorkload> CreateConcatWorkloadTest(armnn::IWorkloadFactory inputShapes.end(), concatAxis); + // create concat layer Layer* const concat = graph.AddLayer<ConcatLayer>(descriptor, "concat"); - BOOST_TEST_CHECKPOINT("created concat layer"); + CHECK(concat); Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); // Adds connections. + // connect input0 to concat Connect(input0, concat, inputTensorInfo, 0, 0); - BOOST_TEST_CHECKPOINT("connect input0 to concat"); + // connect input1 to concat Connect(input1, concat, inputTensorInfo, 0, 1); - BOOST_TEST_CHECKPOINT("connect input1 to concat"); + // connect concat to output Connect(concat, output, outputTensorInfo, 0, 0); - BOOST_TEST_CHECKPOINT("connect concat to output"); + // create tensor handles CreateTensorHandles(graph, factory); - BOOST_TEST_CHECKPOINT("created tensor handles"); + // create concat workload auto workloadConcat = MakeAndCheckWorkload<ConcatWorkload>(*concat, factory); - BOOST_TEST_CHECKPOINT("created concat workload"); + CHECK(workloadConcat); return workloadConcat; } @@ -1979,7 +1986,7 @@ std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> Cre // Add an input layer armnn::IConnectableLayer* const inputLayer = net->AddInputLayer(0, "input layer"); - BOOST_TEST(inputLayer); + CHECK(inputLayer); // ArmNN weights tensor shape is OIHW (out channels, in channels, height, width) for NCHW // ArmNN weights tensor shape is OHWI (out channels, height, width, in channels) for NHWC @@ -2035,11 +2042,11 @@ std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> Cre convLayerName.c_str()); } - BOOST_TEST(convLayer); + CHECK(convLayer); // Add an output layer armnn::IConnectableLayer* const outputLayer = net->AddOutputLayer(0, "output layer"); - BOOST_TEST(outputLayer); + CHECK(outputLayer); // set the tensors in the network (NHWC format) TensorInfo inputTensorInfo(TensorShape({ 1, 16, 16, 16 }), dataType); @@ -2070,7 +2077,7 @@ std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> Cre armnn::OptimizerOptions optimizerOptions; armnn::IOptimizedNetworkPtr optimizedNet = armnn::Optimize(*net, backends, runtime->GetDeviceSpec(), optimizerOptions); - BOOST_CHECK(optimizedNet != nullptr); + CHECK(optimizedNet != nullptr); // Find the PreCompiled layer in the optimised graph armnn::Graph& optimisedGraph = GetGraphForTesting(optimizedNet.get()); @@ -2082,7 +2089,7 @@ std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> Cre preCompiledLayer = layer; } } - BOOST_CHECK(preCompiledLayer != nullptr); + CHECK(preCompiledLayer != nullptr); // Create the TensorHandles. CreateTensorHandles(optimisedGraph, factory); @@ -2091,8 +2098,8 @@ std::pair<armnn::IOptimizedNetworkPtr, std::unique_ptr<PreCompiledWorkload>> Cre auto workload = MakeAndCheckWorkload<PreCompiledWorkload>(*preCompiledLayer, factory); PreCompiledQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns the workload so we can do extra, backend-specific tests. // NOTE: We need to return the optimised network as well, otherwise it gets @@ -2107,21 +2114,23 @@ std::unique_ptr<ConstantWorkload> CreateConstantWorkloadTest(armnn::IWorkloadFac { armnn::TensorInfo outputTensorInfo(outputShape, DataType); + // create constant layer auto constant = graph.AddLayer<ConstantLayer>("constant"); + CHECK(constant); constant->m_LayerOutput = std::make_unique<ScopedTensorHandle>(outputTensorInfo); - BOOST_TEST_CHECKPOINT("created constant layer"); Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); // Adds connections. + // connect constant to output Connect(constant, output, outputTensorInfo, 0, 0); - BOOST_TEST_CHECKPOINT("connect constant to output"); + // create tensor handles CreateTensorHandles(graph, factory); - BOOST_TEST_CHECKPOINT("created tensor handles"); + // create Constant workload" auto workloadConstant = MakeAndCheckWorkload<ConstantWorkload>(*constant, factory); - BOOST_TEST_CHECKPOINT("created Constant workload"); + CHECK(workloadConstant); return workloadConstant; } @@ -2136,15 +2145,15 @@ std::unique_ptr<PreluWorkload> CreatePreluWorkloadTest(armnn::IWorkloadFactory& { // Creates the PReLU layer Layer* const layer = graph.AddLayer<PreluLayer>("prelu"); - BOOST_CHECK(layer != nullptr); + CHECK(layer != nullptr); // Creates extra layers Layer* const input = graph.AddLayer<InputLayer> (0, "input"); Layer* const alpha = graph.AddLayer<InputLayer> (1, "alpha"); Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); - BOOST_CHECK(input != nullptr); - BOOST_CHECK(alpha != nullptr); - BOOST_CHECK(output != nullptr); + CHECK(input != nullptr); + CHECK(alpha != nullptr); + CHECK(output != nullptr); // Connects up armnn::TensorInfo inputTensorInfo (inputShape, dataType); @@ -2159,8 +2168,8 @@ std::unique_ptr<PreluWorkload> CreatePreluWorkloadTest(armnn::IWorkloadFactory& auto workload = MakeAndCheckWorkload<PreluWorkload>(*layer, factory); PreluQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 2); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 2); + CHECK(queueDescriptor.m_Outputs.size() == 1); // Returns so we can do extra, backend-specific tests. return workload; @@ -2191,8 +2200,8 @@ std::unique_ptr<SpaceToDepthWorkload> CreateSpaceToDepthWorkloadTest(armnn::IWor auto workload = MakeAndCheckWorkload<SpaceToDepthWorkload>(*layer, factory); SpaceToDepthQueueDescriptor queueDescriptor = workload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == 1); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == 1); + CHECK(queueDescriptor.m_Outputs.size() == 1); return workload; } @@ -2211,7 +2220,7 @@ std::unique_ptr<StackWorkload> CreateStackWorkloadTest(armnn::IWorkloadFactory& // Constructs the Stack layer. armnn::StackDescriptor descriptor(axis, numInputs, inputShape); Layer* const stackLayer = graph.AddLayer<StackLayer>(descriptor, "stack"); - BOOST_CHECK(stackLayer != nullptr); + CHECK(stackLayer != nullptr); // Constructs layer inputs and output. std::vector<Layer*> inputs; @@ -2221,10 +2230,10 @@ std::unique_ptr<StackWorkload> CreateStackWorkloadTest(armnn::IWorkloadFactory& static_cast<int>(i), ("input" + std::to_string(i)).c_str() )); - BOOST_CHECK(inputs[i] != nullptr); + CHECK(inputs[i] != nullptr); } Layer* const output = graph.AddLayer<OutputLayer>(0, "output"); - BOOST_CHECK(output != nullptr); + CHECK(output != nullptr); // Adds connections. for (unsigned int i=0; i<numInputs; ++i) @@ -2237,8 +2246,8 @@ std::unique_ptr<StackWorkload> CreateStackWorkloadTest(armnn::IWorkloadFactory& auto stackWorkload = MakeAndCheckWorkload<StackWorkload>(*stackLayer, factory); StackQueueDescriptor queueDescriptor = stackWorkload->GetData(); - BOOST_TEST(queueDescriptor.m_Inputs.size() == numInputs); - BOOST_TEST(queueDescriptor.m_Outputs.size() == 1); + CHECK(queueDescriptor.m_Inputs.size() == numInputs); + CHECK(queueDescriptor.m_Outputs.size() == 1); return stackWorkload; } |