diff options
Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp | 534 |
1 files changed, 11 insertions, 523 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp index a2b477cc2d..438828fe11 100644 --- a/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/TransposeConvolution2dTestImpl.hpp @@ -5,553 +5,41 @@ #pragma once -#include <armnn/ArmNN.hpp> +#include "LayerTestResult.hpp" -#include <Permute.hpp> -#include <QuantizeHelper.hpp> #include <ResolveType.hpp> -#include <backendsCommon/CpuTensorHandle.hpp> - -#include <backendsCommon/test/DataLayoutUtils.hpp> -#include <backendsCommon/test/TensorCopyUtils.hpp> -#include <backendsCommon/test/WorkloadTestUtils.hpp> - -#include <reference/RefWorkloadFactory.hpp> - -#include <boost/test/unit_test.hpp> - -#include <string> -#include <utility> -#include <vector> - -namespace -{ - -template<typename T> -using TensorData = std::pair<armnn::TensorInfo, std::vector<T>>; - -template<typename T> -void VerifyInputTensorData(const TensorData<T>& data, const std::string& tensorName) -{ - if (data.first.GetNumElements() > data.second.size()) - { - throw armnn::InvalidArgumentException("Size of data too small for " + tensorName + ": expected " + - std::to_string(data.first.GetNumElements()) + "but got " + std::to_string(data.second.size())); - } -} - -template<typename T, typename BT> -void TransposeConvolution2dTestImpl(armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::TransposeConvolution2dDescriptor& descriptor, - const TensorData<T>& input, - TensorData<T>& output, - const TensorData<T>& weights, - const armnn::Optional<TensorData<BT>>& biases) -{ - using namespace armnn; - - VerifyInputTensorData(input, "input"); - VerifyInputTensorData(weights, "biases"); - - if (descriptor.m_BiasEnabled) - { - if (!biases.has_value()) - { - throw InvalidArgumentException("Bias enabled but no bias data provided"); - } - VerifyInputTensorData(biases.value(), "biases"); - } - - // set up weights - ScopedCpuTensorHandle weightsTensor(weights.first); - - TransposeConvolution2dQueueDescriptor queueDescriptor; - queueDescriptor.m_Parameters = descriptor; - queueDescriptor.m_Weight = &weightsTensor; - - AllocateAndCopyDataToITensorHandle(&weightsTensor, weights.second.data()); - - std::unique_ptr<ScopedCpuTensorHandle> biasesTensor; - if (descriptor.m_BiasEnabled) - { - // set up biases - biasesTensor = std::make_unique<ScopedCpuTensorHandle>(biases.value().first); - queueDescriptor.m_Bias = biasesTensor.get(); - - AllocateAndCopyDataToITensorHandle(biasesTensor.get(), biases.value().second.data()); - } - - // set up input and output handles - std::unique_ptr<ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(input.first); - std::unique_ptr<ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(output.first); - - // set up workload - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(queueDescriptor, workloadInfo, input.first, inputHandle.get()); - AddOutputToWorkload(queueDescriptor, workloadInfo, output.first, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = - workloadFactory.CreateTransposeConvolution2d(queueDescriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), input.second.data()); - - ExecuteWorkload(*workload, memoryManager); - - // copy output - output.second = std::vector<T>(output.first.GetNumElements(), 0.0f); - CopyDataFromITensorHandle(output.second.data(), outputHandle.get()); -} - -template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T, 4> TransposeConvolution2dTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::TransposeConvolution2dDescriptor& descriptor, - armnn::TensorInfo& inputInfo, - const std::vector<float>& inputData, - armnn::TensorInfo& outputInfo, - const std::vector<float>& expectedOutputData, - armnn::TensorInfo& weightsInfo, - const std::vector<float>& weightsData, - armnn::TensorInfo& biasesInfo, - const std::vector<float>& biasesData) -{ - using namespace armnn; - - // set up quantization parameters - if (armnn::IsQuantizedType<T>()) - { - constexpr float qScale = 0.50f; - constexpr int32_t qOffset = 10; - - inputInfo.SetQuantizationScale(qScale); - inputInfo.SetQuantizationOffset(qOffset); - - outputInfo.SetQuantizationScale(qScale); - outputInfo.SetQuantizationOffset(qOffset); - - weightsInfo.SetQuantizationScale(qScale); - weightsInfo.SetQuantizationOffset(qOffset); - - biasesInfo.SetQuantizationScale(qScale * qScale); - biasesInfo.SetQuantizationOffset(0); - } - - // set up input - TensorData<T> input = - { - inputInfo, - armnnUtils::QuantizedVector<T>(inputData, inputInfo.GetQuantizationScale(), inputInfo.GetQuantizationOffset()) - }; - - // set up weights - TensorData<T> weights = - { - weightsInfo, - armnnUtils::QuantizedVector<T>(weightsData, - weightsInfo.GetQuantizationScale(), - weightsInfo.GetQuantizationOffset()) - }; - - // set up biases - using BT = armnn::ResolveType<ArmnnBType>; - Optional<TensorData<BT>> optionalBiases; - if (descriptor.m_BiasEnabled) - { - TensorData<BT> biases = - { - biasesInfo, - armnnUtils::QuantizedVector<BT>(biasesData, - biasesInfo.GetQuantizationScale(), - biasesInfo.GetQuantizationOffset()) - }; - - optionalBiases = Optional<TensorData<BT>>(biases); - } - - // set up output - TensorData<T> output = { outputInfo, {} }; - - // execute test - TransposeConvolution2dTestImpl(workloadFactory, - memoryManager, - descriptor, - input, - output, - weights, - optionalBiases); - - // construct result object - LayerTestResult<T, 4> testResult(outputInfo); - testResult.output = MakeTensor<T, 4>(outputInfo, output.second); - testResult.outputExpected = MakeTensor<T, 4>(outputInfo, - armnnUtils::QuantizedVector<T>(expectedOutputData, - outputInfo.GetQuantizationScale(), - outputInfo.GetQuantizationOffset())); - - return testResult; -} - -template<typename T> -void SwizzleData(armnn::TensorInfo& inputInfo, - std::vector<T>& inputData, - armnn::TensorInfo& outputInfo, - std::vector<T>& outputData, - armnn::TensorInfo& weightsInfo, - std::vector<T>& weightsData) -{ - PermuteTensorNchwToNhwc<T>(inputInfo, inputData); - PermuteTensorNchwToNhwc<T>(outputInfo, outputData); - PermuteTensorNchwToNhwc<T>(weightsInfo, weightsData); -} - -} // anonymous namespace +#include <backendsCommon/IBackendInternal.hpp> +#include <backendsCommon/WorkloadFactory.hpp> template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleTransposeConvolution2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled, - const armnn::DataLayout layout) -{ - using namespace armnn; - - constexpr unsigned int batches = 1u; - constexpr unsigned int channels = 1u; - - constexpr unsigned int wInput = 3u; - constexpr unsigned int hInput = wInput; - - constexpr unsigned int wOutput = 5u; - constexpr unsigned int hOutput = wOutput; - - constexpr unsigned int wWeights = 3u; - constexpr unsigned int hWeights = wWeights; - - TensorShape inputShape = { batches, channels, hInput, wInput }; - TensorShape outputShape = { batches, channels, hOutput, wOutput }; - TensorShape weightsShape = { batches, channels, hWeights, wWeights }; - - TensorInfo inputInfo(inputShape, ArmnnType); - TensorInfo outputInfo(outputShape, ArmnnType); - TensorInfo weightsInfo(weightsShape, ArmnnType); - TensorInfo biasesInfo({ channels }, ArmnnBType); - - std::vector<float> inputData = - { - 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f - }; - - std::vector<float> weightsData = - { - 1.f, 2.f, 3.f, - 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f - }; - - std::vector<float> biasesData = { 1.f }; - - std::vector<float> expectedOutputData = - { - 1.f, 3.f, 6.f, 5.f, 3.f, - 5.f, 12.f, 21.f, 16.f, 9.f, - 12.f, 27.f, 45.f, 33.f, 18.f, - 11.f, 24.f, 39.f, 28.f, 15.f, - 7.f, 15.f, 24.f, 17.f, 9.f - }; - - if (biasEnabled) - { - // apply bias to expected output data - std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(), - [&](float f) -> float { return f + biasesData[0]; }); - } - - TransposeConvolution2dDescriptor descriptor; - descriptor.m_StrideX = 1; - descriptor.m_StrideY = 1; - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_DataLayout = layout; - - // swizzle data if needed - if (layout == armnn::DataLayout::NHWC) - { - SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData); - } - - return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory, - memoryManager, - descriptor, - inputInfo, - inputData, - outputInfo, - expectedOutputData, - weightsInfo, - weightsData, - biasesInfo, - biasesData); -} + const armnn::DataLayout layout); template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> PaddedTransposeConvolution2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled, - const armnn::DataLayout layout) -{ - using namespace armnn; - - constexpr unsigned int batches = 1u; - constexpr unsigned int channels = 1u; - - constexpr unsigned int wInput = 4u; - constexpr unsigned int hInput = wInput; - - constexpr unsigned int wOutput = 2u; - constexpr unsigned int hOutput = wOutput; - - constexpr unsigned int wWeights = 3u; - constexpr unsigned int hWeights = wWeights; - - TensorShape inputShape = { batches, channels, hInput, wInput }; - TensorShape outputShape = { batches, channels, hOutput, wOutput }; - TensorShape weightsShape = { batches, channels, hWeights, wWeights }; - - TensorInfo inputInfo(inputShape, ArmnnType); - TensorInfo outputInfo(outputShape, ArmnnType); - TensorInfo weightsInfo(weightsShape, ArmnnType); - TensorInfo biasesInfo({ channels }, ArmnnBType); - - std::vector<float> inputData = - { - 1.f, 3.f, 2.f, 1.f, - 1.f, 3.f, 3.f, 1.f, - 2.f, 1.f, 1.f, 3.f, - 3.f, 2.f, 3.f, 3.f - }; - - std::vector<float> weightsData = - { - 1.f, 2.f, 3.f, - 0.f, 1.f, 0.f, - 2.f, 1.f, 2.f - }; - - std::vector<float> biasesData = { 1.f }; - - std::vector<float> expectedOutputData = - { - 21.f, 21.f, - 28.f, 27.f - }; - - if (biasEnabled) - { - // apply bias to expected output data - std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(), - [&](float f) -> float { return f + biasesData[0]; }); - } - - TransposeConvolution2dDescriptor descriptor; - descriptor.m_PadLeft = 2; - descriptor.m_PadRight = 2; - descriptor.m_PadTop = 2; - descriptor.m_PadBottom = 2; - descriptor.m_StrideX = 1; - descriptor.m_StrideY = 1; - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_DataLayout = layout; - - // swizzle data if needed - if (layout == armnn::DataLayout::NHWC) - { - SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData); - } - - return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory, - memoryManager, - descriptor, - inputInfo, - inputData, - outputInfo, - expectedOutputData, - weightsInfo, - weightsData, - biasesInfo, - biasesData); -} + const armnn::DataLayout layout); template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> StridedTransposeConvolution2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, bool biasEnabled, - const armnn::DataLayout layout) -{ - using namespace armnn; - - constexpr unsigned int batches = 1u; - constexpr unsigned int channels = 1u; - - constexpr unsigned int wInput = 3u; - constexpr unsigned int hInput = wInput; - - constexpr unsigned int wOutput = 7u; - constexpr unsigned int hOutput = wOutput; - - constexpr unsigned int wWeights = 3u; - constexpr unsigned int hWeights = wWeights; - - TensorShape inputShape = { batches, channels, hInput, wInput }; - TensorShape outputShape = { batches, channels, hOutput, wOutput }; - TensorShape weightsShape = { batches, channels, hWeights, wWeights }; - - TensorInfo inputInfo(inputShape, ArmnnType); - TensorInfo outputInfo(outputShape, ArmnnType); - TensorInfo weightsInfo(weightsShape, ArmnnType); - TensorInfo biasesInfo({ channels }, ArmnnBType); - - std::vector<float> inputData = - { - 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f, - 1.f, 1.f, 1.f - }; - - std::vector<float> weightsData = - { - 1.f, 2.f, 3.f, - 4.f, 5.f, 6.f, - 7.f, 8.f, 9.f - }; - - std::vector<float> biasesData = { 1.f }; - - std::vector<float> expectedOutputData = - { - 1.f, 2.f, 4.f, 2.f, 4.f, 2.f, 3.f, - 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f, - 8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f, - 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f, - 8.f, 10.f, 20.f, 10.f, 20.f, 10.f, 12.f, - 4.f, 5.f, 10.f, 5.f, 10.f, 5.f, 6.f, - 7.f, 8.f, 16.f, 8.f, 16.f, 8.f, 9.f - }; - - if (biasEnabled) - { - // apply bias to expected output data - std::transform(expectedOutputData.begin(), expectedOutputData.end(), expectedOutputData.begin(), - [&](float f) -> float { return f + biasesData[0]; }); - } - - TransposeConvolution2dDescriptor descriptor; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 2; - descriptor.m_BiasEnabled = biasEnabled; - descriptor.m_DataLayout = layout; - - // swizzle data if needed - if (layout == armnn::DataLayout::NHWC) - { - SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData); - } - - return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory, - memoryManager, - descriptor, - inputInfo, - inputData, - outputInfo, - expectedOutputData, - weightsInfo, - weightsData, - biasesInfo, - biasesData); -} + const armnn::DataLayout layout); template<armnn::DataType ArmnnType, armnn::DataType ArmnnBType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> MultiChannelTransposeConvolution2dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout layout) -{ - using namespace armnn; - - TensorShape inputShape = { 1, 1, 2, 2 }; - TensorShape outputShape = { 1, 2, 5, 5 }; + const armnn::DataLayout layout); - // OIHW for NCHW; OHWI for NHWC - TensorShape weightsShape = { 2, 1, 3, 3 }; - TensorShape biasesShape = { 2 }; - - TensorInfo inputInfo(inputShape, ArmnnType); - TensorInfo outputInfo(outputShape, ArmnnType); - TensorInfo weightsInfo(weightsShape, ArmnnType); - TensorInfo biasesInfo(biasesShape, ArmnnBType); - - std::vector<float> inputData = - { - 1.f, 2.f, - 3.f, 4.f, - }; - - std::vector<float> weightsData = - { - 1.f, 3.f, 5.f, - 7.f, 9.f, 11.f, - 13.f, 15.f, 17.f, - - 2.f, 4.f, 6.f, - 8.f, 10.f, 12.f, - 14.f, 16.f, 18.f - }; - - std::vector<float> biasesData = { -1.5f, -2.0f }; - - std::vector<float> expectedOutputData = - { - -0.5f, 1.5f, 5.5f, 4.5f, 8.5f, - 5.5f, 7.5f, 23.5f, 16.5f, 20.5f, - 14.5f, 22.5f, 60.5f, 40.5f, 52.5f, - 19.5f, 25.5f, 59.5f, 34.5f, 42.5f, - 37.5f, 43.5f, 101.5f, 58.5f, 66.5f, - - 0.0f, 2.0f, 8.0f, 6.0f, 10.0f, - 6.0f, 8.0f, 26.0f, 18.0f, 22.0f, - 18.0f, 26.0f, 70.0f, 46.0f, 58.0f, - 22.0f, 28.0f, 66.0f, 38.0f, 46.0f, - 40.0f, 46.0f, 108.0f, 62.0f, 70.0f, - }; - - TransposeConvolution2dDescriptor descriptor; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 2; - descriptor.m_BiasEnabled = true; - descriptor.m_DataLayout = layout; - - // swizzle data if needed - if (layout == armnn::DataLayout::NHWC) - { - SwizzleData(inputInfo, inputData, outputInfo, expectedOutputData, weightsInfo, weightsData); - } - - return TransposeConvolution2dTest<ArmnnType, ArmnnBType>(workloadFactory, - memoryManager, - descriptor, - inputInfo, - inputData, - outputInfo, - expectedOutputData, - weightsInfo, - weightsData, - biasesInfo, - biasesData); -} +LayerTestResult<uint8_t, 4> TransposeConvolution2dPerAxisQuantTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout layout); |