// // Copyright © 2017 Arm Ltd. All rights reserved. // SPDX-License-Identifier: MIT // #include "WorkloadTestUtils.hpp" #include #include #include #include #include #include using namespace armnn; BOOST_AUTO_TEST_SUITE(WorkloadInfoValidation) BOOST_AUTO_TEST_CASE(QueueDescriptor_Validate_WrongNumOfInputsOutputs) { InputQueueDescriptor invalidData; WorkloadInfo invalidInfo; //Invalid argument exception is expected, because no inputs and no outputs were defined. BOOST_CHECK_THROW(RefWorkloadFactory().CreateInput(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(RefPooling2dFloat32Workload_Validate_WrongDimTensor) { armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int inputShape[] = {2, 3, 4}; // <- Invalid - input tensor has to be 4D. unsigned int outputShape[] = {2, 3, 4, 5}; outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); inputTensorInfo = armnn::TensorInfo(3, inputShape, armnn::DataType::Float32); Pooling2dQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); // Invalid argument exception is expected, input tensor has to be 4D. BOOST_CHECK_THROW(RefPooling2dFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(SoftmaxQueueDescriptor_Validate_WrongInputHeight) { unsigned int inputHeight = 1; unsigned int inputWidth = 1; unsigned int inputChannels = 4; unsigned int inputNum = 2; unsigned int outputChannels = inputChannels; unsigned int outputHeight = inputHeight + 1; //Makes data invalid - Softmax expects height and width to be 1. unsigned int outputWidth = inputWidth; unsigned int outputNum = inputNum; armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); SoftmaxQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); //Invalid argument exception is expected, because height != 1. BOOST_CHECK_THROW(RefSoftmaxFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(FullyConnectedQueueDescriptor_Validate_RequiredDataMissing) { unsigned int inputWidth = 1; unsigned int inputHeight = 1; unsigned int inputChannels = 5; unsigned int inputNum = 2; unsigned int outputWidth = 1; unsigned int outputHeight = 1; unsigned int outputChannels = 3; unsigned int outputNum = 2; // Define the tensor descriptors. armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; armnn::TensorInfo weightsDesc; armnn::TensorInfo biasesDesc; unsigned int inputShape[] = { inputNum, inputChannels, inputHeight, inputWidth }; unsigned int outputShape[] = { outputNum, outputChannels, outputHeight, outputWidth }; unsigned int weightsShape[] = { 1, 1, inputChannels, outputChannels }; unsigned int biasShape[] = { 1, outputChannels, outputHeight, outputWidth }; inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); weightsDesc = armnn::TensorInfo(4, weightsShape, armnn::DataType::Float32); biasesDesc = armnn::TensorInfo(4, biasShape, armnn::DataType::Float32); FullyConnectedQueueDescriptor invalidData; WorkloadInfo invalidInfo; ScopedCpuTensorHandle weightTensor(weightsDesc); ScopedCpuTensorHandle biasTensor(biasesDesc); AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); invalidData.m_Weight = &weightTensor; invalidData.m_Bias = &biasTensor; invalidData.m_Parameters.m_BiasEnabled = true; invalidData.m_Parameters.m_TransposeWeightMatrix = false; //Invalid argument exception is expected, because not all required fields have been provided. //In particular inputsData[0], outputsData[0] and weightsData can not be null. BOOST_CHECK_THROW(RefFullyConnectedFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(NormalizationQueueDescriptor_Validate_WrongInputHeight) { constexpr unsigned int inputNum = 5; constexpr unsigned int inputHeight = 32; constexpr unsigned int inputWidth = 24; constexpr unsigned int inputChannels = 3; constexpr unsigned int outputNum = inputNum; constexpr unsigned int outputChannels = inputChannels; constexpr unsigned int outputHeight = inputHeight + 1; //Makes data invalid - normalization requires. //Input and output to have the same dimensions. constexpr unsigned int outputWidth = inputWidth; armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); armnn::NormalizationAlgorithmMethod normMethod = armnn::NormalizationAlgorithmMethod::LocalBrightness; armnn::NormalizationAlgorithmChannel normChannel = armnn::NormalizationAlgorithmChannel::Across; float alpha = 1.f; float beta = 1.f; float kappa = 1.f; uint32_t normSize = 5; NormalizationQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); invalidData.m_Parameters.m_NormChannelType = normChannel; invalidData.m_Parameters.m_NormMethodType = normMethod; invalidData.m_Parameters.m_NormSize = normSize; invalidData.m_Parameters.m_Alpha = alpha; invalidData.m_Parameters.m_Beta = beta; invalidData.m_Parameters.m_K = kappa; //Invalid argument exception is expected, because input height != output height. BOOST_CHECK_THROW(RefNormalizationFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(SplitterQueueDescriptor_Validate_WrongWindow) { constexpr unsigned int inputNum = 1; constexpr unsigned int inputHeight = 32; constexpr unsigned int inputWidth = 24; constexpr unsigned int inputChannels = 3; constexpr unsigned int outputNum = inputNum; constexpr unsigned int outputChannels = inputChannels; constexpr unsigned int outputHeight = 18; constexpr unsigned int outputWidth = inputWidth; armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); SplitterQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); // Invalid, since it has only 3 dimensions while the input tensor is 4d. std::vector wOrigin = {0, 0, 0}; armnn::SplitterQueueDescriptor::ViewOrigin window(wOrigin); invalidData.m_ViewOrigins.push_back(window); BOOST_TEST_INFO("Invalid argument exception is expected, because split window dimensionality does not " "match input."); BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); // Invalid, since window extends past the boundary of input tensor. std::vector wOrigin3 = {0, 0, 15, 0}; armnn::SplitterQueueDescriptor::ViewOrigin window3(wOrigin3); invalidData.m_ViewOrigins[0] = window3; BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ outputHeight > inputHeight"); BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); std::vector wOrigin4 = {0, 0, 0, 0}; armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4); invalidData.m_ViewOrigins[0] = window4; std::vector wOrigin5 = {1, 16, 20, 2}; armnn::SplitterQueueDescriptor::ViewOrigin window5(wOrigin4); invalidData.m_ViewOrigins.push_back(window5); BOOST_TEST_INFO("Invalid exception due to number of split windows not matching number of outputs."); BOOST_CHECK_THROW(RefSplitterFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(MergerQueueDescriptor_Validate_WrongWindow) { constexpr unsigned int inputNum = 1; constexpr unsigned int inputChannels = 3; constexpr unsigned int inputHeight = 32; constexpr unsigned int inputWidth = 24; constexpr unsigned int outputNum = 1; constexpr unsigned int outputChannels = 3; constexpr unsigned int outputHeight = 32; constexpr unsigned int outputWidth = 24; armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int inputShape[] = {inputNum, inputChannels, inputHeight, inputWidth}; unsigned int outputShape[] = {outputNum, outputChannels, outputHeight, outputWidth}; inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); MergerQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); // Invalid, since it has only 3 dimensions while the input tensor is 4d. std::vector wOrigin = {0, 0, 0}; armnn::MergerQueueDescriptor::ViewOrigin window(wOrigin); invalidData.m_ViewOrigins.push_back(window); BOOST_TEST_INFO("Invalid argument exception is expected, because merge window dimensionality does not " "match input."); BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); // Invalid, since window extends past the boundary of output tensor. std::vector wOrigin3 = {0, 0, 15, 0}; armnn::MergerQueueDescriptor::ViewOrigin window3(wOrigin3); invalidData.m_ViewOrigins[0] = window3; BOOST_TEST_INFO("Invalid argument exception is expected (wOrigin3[2]+ inputHeight > outputHeight"); BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); std::vector wOrigin4 = {0, 0, 0, 0}; armnn::MergerQueueDescriptor::ViewOrigin window4(wOrigin4); invalidData.m_ViewOrigins[0] = window4; std::vector wOrigin5 = {1, 16, 20, 2}; armnn::MergerQueueDescriptor::ViewOrigin window5(wOrigin4); invalidData.m_ViewOrigins.push_back(window5); BOOST_TEST_INFO("Invalid exception due to number of merge windows not matching number of inputs."); BOOST_CHECK_THROW(RefMergerFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputNumbers) { armnn::TensorInfo input1TensorInfo; armnn::TensorInfo input2TensorInfo; armnn::TensorInfo input3TensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int shape[] = {1, 1, 1, 1}; input1TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); input2TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); input3TensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, shape, armnn::DataType::Float32); AdditionQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); // Too few inputs. BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); // Correct. BOOST_CHECK_NO_THROW(RefAdditionWorkload(invalidData, invalidInfo)); AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr); // Too many inputs. BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(AdditionQueueDescriptor_Validate_InputShapes) { armnn::TensorInfo input1TensorInfo; armnn::TensorInfo input2TensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int shape1[] = {1, 1, 2, 1}; unsigned int shape2[] = {1, 1, 3, 2}; // Incompatible shapes even with broadcasting. { input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); input2TensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); AdditionQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); } // Output size not compatible with input sizes. { input1TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); input2TensorInfo = armnn::TensorInfo(4, shape1, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, shape2, armnn::DataType::Float32); AdditionQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); // Output differs. BOOST_CHECK_THROW(RefAdditionWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); } } BOOST_AUTO_TEST_CASE(MultiplicationQueueDescriptor_Validate_InputTensorDimensionMismatch) { armnn::TensorInfo input0TensorInfo; armnn::TensorInfo input1TensorInfo; armnn::TensorInfo outputTensorInfo; constexpr unsigned int input0Shape[] = { 2, 2, 4, 4 }; constexpr std::size_t dimensionCount = std::extent::value; // Checks dimension consistency for input tensors. for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) { unsigned int input1Shape[dimensionCount]; for (unsigned int i = 0; i < dimensionCount; ++i) { input1Shape[i] = input0Shape[i]; } ++input1Shape[dimIndex]; input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); input1TensorInfo = armnn::TensorInfo(dimensionCount, input1Shape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); MultiplicationQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); BOOST_CHECK_THROW(RefMultiplicationWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); } // Checks dimension consistency for input and output tensors. for (unsigned int dimIndex = 0; dimIndex < dimensionCount; ++dimIndex) { unsigned int outputShape[dimensionCount]; for (unsigned int i = 0; i < dimensionCount; ++i) { outputShape[i] = input0Shape[i]; } ++outputShape[dimIndex]; input0TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); input1TensorInfo = armnn::TensorInfo(dimensionCount, input0Shape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(dimensionCount, outputShape, armnn::DataType::Float32); MultiplicationQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, input0TensorInfo, nullptr); AddInputToWorkload(invalidData, invalidInfo, input1TensorInfo, nullptr); BOOST_CHECK_THROW(RefMultiplicationWorkload(invalidData, invalidInfo), armnn::InvalidArgumentException); } } BOOST_AUTO_TEST_CASE(ReshapeQueueDescriptor_Validate_MismatchingNumElements) { armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; // The input and output shapes should have the same number of elements, but these don't. unsigned int inputShape[] = { 1, 1, 2, 3 }; unsigned int outputShape[] = { 1, 1, 1, 2 }; inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::DataType::Float32); ReshapeQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); // InvalidArgumentException is expected, because the number of elements don't match. BOOST_CHECK_THROW(RefReshapeFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_CASE(LstmQueueDescriptor_Validate) { armnn::TensorInfo inputTensorInfo; armnn::TensorInfo outputTensorInfo; unsigned int inputShape[] = { 1, 2 }; unsigned int outputShape[] = { 1 }; inputTensorInfo = armnn::TensorInfo(2, inputShape, armnn::DataType::Float32); outputTensorInfo = armnn::TensorInfo(1, outputShape, armnn::DataType::Float32); LstmQueueDescriptor invalidData; WorkloadInfo invalidInfo; AddInputToWorkload(invalidData, invalidInfo, inputTensorInfo, nullptr); AddOutputToWorkload(invalidData, invalidInfo, outputTensorInfo, nullptr); BOOST_CHECK_THROW(invalidData.Validate(invalidInfo), armnn::InvalidArgumentException); } BOOST_AUTO_TEST_SUITE_END()