IVGCVSW-1946: Remove armnn/src from the include paths
Change-Id: I663a0a0fccb43ee960ec070121a59df9db0bb04e
diff --git a/src/backends/backendsCommon/test/WorkloadDataValidation.cpp b/src/backends/backendsCommon/test/WorkloadDataValidation.cpp
new file mode 100644
index 0000000..3664d56
--- /dev/null
+++ b/src/backends/backendsCommon/test/WorkloadDataValidation.cpp
@@ -0,0 +1,474 @@
+//
+// Copyright © 2017 Arm Ltd. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+
+#include "WorkloadTestUtils.hpp"
+
+#include <armnn/Exceptions.hpp>
+
+#include <backendsCommon/CpuTensorHandle.hpp>
+#include <backendsCommon/Workload.hpp>
+
+#include <reference/workloads/RefWorkloads.hpp>
+#include <reference/RefWorkloadFactory.hpp>
+
+#include <boost/test/unit_test.hpp>
+
+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<unsigned int> 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<unsigned int> 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<unsigned int> wOrigin4 = {0, 0, 0, 0};
+ armnn::SplitterQueueDescriptor::ViewOrigin window4(wOrigin4);
+ invalidData.m_ViewOrigins[0] = window4;
+
+ std::vector<unsigned int> 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<unsigned int> 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<unsigned int> 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<unsigned int> wOrigin4 = {0, 0, 0, 0};
+ armnn::MergerQueueDescriptor::ViewOrigin window4(wOrigin4);
+ invalidData.m_ViewOrigins[0] = window4;
+
+ std::vector<unsigned int> 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(RefAdditionFloat32Workload(invalidData, invalidInfo), armnn::InvalidArgumentException);
+
+ AddInputToWorkload(invalidData, invalidInfo, input2TensorInfo, nullptr);
+
+ // Correct.
+ BOOST_CHECK_NO_THROW(RefAdditionFloat32Workload(invalidData, invalidInfo));
+
+ AddInputToWorkload(invalidData, invalidInfo, input3TensorInfo, nullptr);
+
+ // Too many inputs.
+ BOOST_CHECK_THROW(RefAdditionFloat32Workload(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(RefAdditionFloat32Workload(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(RefAdditionFloat32Workload(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<decltype(input0Shape)>::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(RefMultiplicationFloat32Workload(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(RefMultiplicationFloat32Workload(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()