diff options
Diffstat (limited to 'src/backends/backendsCommon/test/LayerTests.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/LayerTests.hpp | 364 |
1 files changed, 262 insertions, 102 deletions
diff --git a/src/backends/backendsCommon/test/LayerTests.hpp b/src/backends/backendsCommon/test/LayerTests.hpp index 0fe5d09cd4..df33aa192e 100644 --- a/src/backends/backendsCommon/test/LayerTests.hpp +++ b/src/backends/backendsCommon/test/LayerTests.hpp @@ -539,6 +539,11 @@ LayerTestResult<T, 4> SimpleReshapeTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> Reshape5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult<float, 4> SimpleFloorTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); @@ -712,6 +717,10 @@ LayerTestResult<float, 4> AdditionTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +LayerTestResult<float, 5> Addition5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult<float, 4> AdditionBroadcast1ElementTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); @@ -764,6 +773,10 @@ LayerTestResult<float, 4> MultiplicationTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +LayerTestResult<float, 5> Multiplication5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); + LayerTestResult<float, 4> MultiplicationBroadcast1ElementTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); @@ -2084,6 +2097,37 @@ LayerTestResult<int16_t, 4> QuantizeClampInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager); +template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +std::vector<T> ConvertToDataType(const std::vector<float>& input, + const armnn::TensorInfo& inputTensorInfo) +{ + std::vector<T> output(input.size()); + auto outputTensorInfo = inputTensorInfo; + outputTensorInfo.SetDataType(ArmnnType); + + std::unique_ptr<armnn::Encoder<float>> pOutputEncoder = armnn::MakeEncoder<float>(outputTensorInfo, output.data()); + armnn::Encoder<float>& rOutputEncoder = *pOutputEncoder; + + for (auto it = input.begin(); it != input.end(); ++it) + { + rOutputEncoder.Set(*it); + ++rOutputEncoder; + } + return output; +} + +// Utility method to convert a single value to the correct type +template <typename T> +T ConvertToDataType(const float& value, + const armnn::TensorInfo& tensorInfo) +{ + std::vector<T> output(1); + std::unique_ptr<armnn::Encoder<float>> pEncoder = armnn::MakeEncoder<float>(tensorInfo, output.data()); + armnn::Encoder<float>& rEncoder = *pEncoder; + rEncoder.Set(value); + return output[0]; +} + template<typename T, typename B> LayerTestResult<T, 2> SimpleFullyConnectedTestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -2130,35 +2174,75 @@ LayerTestResult<T, 2> SimpleFullyConnectedTestImpl( return result; } -template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -std::vector<T> ConvertToDataType(const std::vector<float>& input, - const armnn::TensorInfo& inputTensorInfo) +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 2> FullyConnectedTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + bool biasEnabled) { - std::vector<T> output(input.size()); - auto outputTensorInfo = inputTensorInfo; - outputTensorInfo.SetDataType(ArmnnType); + constexpr static unsigned int inputWidth = 3u; + constexpr static unsigned int inputHeight = 2u; + constexpr static unsigned int inputChannels = 1u; - std::unique_ptr<armnn::Encoder<float>> pOutputEncoder = armnn::MakeEncoder<float>(outputTensorInfo, output.data()); - armnn::Encoder<float>& rOutputEncoder = *pOutputEncoder; + constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels; - for (auto it = input.begin(); it != input.end(); ++it) + constexpr static unsigned int outputChannels = 2u; + + armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType); + inputTensorInfo.SetQuantizationScale(0.1f); + inputTensorInfo.SetQuantizationOffset(63); + + armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, ArmnnType); + outputTensorInfo.SetQuantizationScale(5.f); + outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); + + armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, ArmnnType); + weightsDesc.SetQuantizationScale(0.2f); + weightsDesc.SetQuantizationOffset(93); + + armnn::TensorInfo biasesDesc({ outputChannels }, GetBiasTypeFromWeightsType(weightsDesc.GetDataType()).value()); + biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); + biasesDesc.SetQuantizationOffset(0); + + LayerTestResult<T, 2> result(outputTensorInfo); + + auto input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>( + { + -1.2f, 6.1f, -3.5f, + 18.8f, -5.5f, 2.9f + }, + inputTensorInfo)); + + auto weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>( + { + -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, + 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f + }, + weightsDesc)); + + auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500}); + + result = SimpleFullyConnectedTestImpl<T>( + workloadFactory, + memoryManager, + inputTensorInfo, outputTensorInfo, + weightsDesc, biasesDesc, + weights, bias, input, + biasEnabled, true + ); + + if (biasEnabled) { - rOutputEncoder.Set(*it); - ++rOutputEncoder; + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, + ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo)); + } + else + { + result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, + ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo)); } - return output; -} -// Utility method to convert a single value to the correct type -template <typename T> -T ConvertToDataType(const float& value, - const armnn::TensorInfo& tensorInfo) -{ - std::vector<T> output(1); - std::unique_ptr<armnn::Encoder<float>> pEncoder = armnn::MakeEncoder<float>(tensorInfo, output.data()); - armnn::Encoder<float>& rEncoder = *pEncoder; - rEncoder.Set(value); - return output[0]; + return result; } template<armnn::DataType ArmnnType, typename T> @@ -2354,8 +2438,8 @@ LayerTestResult<T, 2> RsqrtNegativeTest( inputValues, expectedOutputValues); } -template<typename T> -LayerTestResult<T, 4> SimpleReshapeTestImpl( +template<typename T, size_t NumDims> +LayerTestResult<T, NumDims> SimpleReshapeTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::TensorInfo inputTensorInfo, @@ -2363,10 +2447,10 @@ LayerTestResult<T, 4> SimpleReshapeTestImpl( const std::vector<T>& inputData, const std::vector<T>& outputExpectedData) { - auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); + auto input = MakeTensor<T, NumDims>(inputTensorInfo, inputData); - LayerTestResult<T, 4> ret(outputTensorInfo); - ret.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData); + LayerTestResult<T, NumDims> ret(outputTensorInfo); + ret.outputExpected = MakeTensor<T, NumDims>(outputTensorInfo, outputExpectedData); std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); @@ -2381,87 +2465,16 @@ LayerTestResult<T, 4> SimpleReshapeTestImpl( inputHandle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), input.origin()); workload->Execute(); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(ret.output.origin(), outputHandle.get()); return ret; } template<armnn::DataType ArmnnType, typename T> -LayerTestResult<T, 2> FullyConnectedTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - bool biasEnabled) -{ - constexpr static unsigned int inputWidth = 3u; - constexpr static unsigned int inputHeight = 2u; - constexpr static unsigned int inputChannels = 1u; - - constexpr static unsigned int inputSize = inputWidth * inputHeight * inputChannels; - - constexpr static unsigned int outputChannels = 2u; - - armnn::TensorInfo inputTensorInfo({ 1, inputChannels, inputHeight, inputWidth }, ArmnnType); - inputTensorInfo.SetQuantizationScale(0.1f); - inputTensorInfo.SetQuantizationOffset(63); - - armnn::TensorInfo outputTensorInfo({ 1, outputChannels }, ArmnnType); - outputTensorInfo.SetQuantizationScale(5.f); - outputTensorInfo.SetQuantizationOffset(biasEnabled ? -50 : 10); - - armnn::TensorInfo weightsDesc({ outputChannels, inputSize }, ArmnnType); - weightsDesc.SetQuantizationScale(0.2f); - weightsDesc.SetQuantizationOffset(93); - - armnn::TensorInfo biasesDesc({ outputChannels }, GetBiasTypeFromWeightsType(weightsDesc.GetDataType()).value()); - biasesDesc.SetQuantizationScale(inputTensorInfo.GetQuantizationScale() * weightsDesc.GetQuantizationScale()); - biasesDesc.SetQuantizationOffset(0); - - LayerTestResult<T, 2> result(outputTensorInfo); - - auto input = MakeTensor<T, 4>(inputTensorInfo, ConvertToDataType<ArmnnType>( - { - -1.2f, 6.1f, -3.5f, - 18.8f, -5.5f, 2.9f - }, - inputTensorInfo)); - - auto weights = MakeTensor<T, 2>(weightsDesc, ConvertToDataType<ArmnnType>( - { - -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, - 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f - }, - weightsDesc)); - - auto bias = MakeTensor<int32_t, 1>(biasesDesc, std::vector<int32_t>{9250, 67500}); - - result = SimpleFullyConnectedTestImpl<T>( - workloadFactory, - memoryManager, - inputTensorInfo, outputTensorInfo, - weightsDesc, biasesDesc, - weights, bias, input, - biasEnabled, true - ); - - if (biasEnabled) - { - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, - ConvertToDataType<ArmnnType>({80.f, 1460.f}, outputTensorInfo)); - } - else - { - result.outputExpected = MakeTensor<T, 2>(outputTensorInfo, - ConvertToDataType<ArmnnType>({-107.04f, 110.f}, outputTensorInfo)); - } - - return result; -} - -template<armnn::DataType ArmnnType, typename T> LayerTestResult<T, 4> SimpleReshapeTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) @@ -2509,7 +2522,69 @@ LayerTestResult<T, 4> SimpleReshapeTest( }, outputTensorInfo); - return SimpleReshapeTestImpl<T>( + return SimpleReshapeTestImpl<T, 4>( + workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 5> Reshape5dTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + armnn::TensorInfo inputTensorInfo; + armnn::TensorInfo outputTensorInfo; + + unsigned int inputShape[] = { 2, 2, 8, 1, 1 }; + unsigned int outputShape[] = { 2, 2, 2, 2, 2 }; + + inputTensorInfo = armnn::TensorInfo(5, inputShape, ArmnnType); + inputTensorInfo.SetQuantizationScale(1.0f); + outputTensorInfo = armnn::TensorInfo(5, outputShape, ArmnnType); + outputTensorInfo.SetQuantizationScale(1.0f); + + auto input = ConvertToDataType<ArmnnType>( + { + 0.0f, 1.0f, 2.0f, 3.0f, 4.0f, 5.0f, 6.0f, 7.0f, + 8.0f, 9.0f, 10.0f, 11.0f, 12.0f, 13.0f, 14.0f, 15.0f, + + 16.0f, 17.0f, 18.0f, 19.0f, 20.0f, 21.0f, 22.0f, 23.0f, + 24.0f, 25.0f, 26.0f, 27.0f, 28.0f, 29.0f, 30.0f, 31.0f, + }, + inputTensorInfo); + + auto outputExpected = ConvertToDataType<ArmnnType>( + { + 0.0f, 1.0f, + 2.0f, 3.0f, + + 4.0f, 5.0f, + 6.0f, 7.0f, + + + 8.0f, 9.0f, + 10.0f, 11.0f, + + 12.0f, 13.0f, + 14.0f, 15.0f, + + + + 16.0f, 17.0f, + 18.0f, 19.0f, + + 20.0f, 21.0f, + 22.0f, 23.0f, + + + 24.0f, 25.0f, + 26.0f, 27.0f, + + 28.0f, 29.0f, + 30.0f, 31.0f, + }, + outputTensorInfo); + + return SimpleReshapeTestImpl<T, 5>( workloadFactory, memoryManager, inputTensorInfo, outputTensorInfo, input, outputExpected); } @@ -4746,3 +4821,88 @@ LayerTestResult<T, 3> Stack3dOutput1Axis3InputTest( outputExpectedData ); } + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> Stack5dOutputTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager) +{ + armnn::TensorInfo inputTensorInfo ({ 2, 2, 2, 3 }, ArmnnType); + armnn::TensorInfo outputTensorInfo({ 2, 2, 2, 2, 3 }, ArmnnType); + + std::vector<std::vector<T>> inputData; + + inputData.push_back( + { + 1, 2, 3, + 4, 5, 6, + + 7, 8, 9, + 10, 11, 12, + + + 13, 14, 15, + 16, 17, 18, + + 19, 20, 21, + 22, 23, 24 + }); + + inputData.push_back( + { + 25, 26, 27, + 28, 29, 30, + + 31, 32, 33, + 34, 35, 36, + + + 37, 38, 39, + 40, 41, 42, + + 43, 44, 45, + 46, 47, 48 + }); + + std::vector<T> outputExpectedData = + { + 1, 2, 3, + 4, 5, 6, + + 7, 8, 9, + 10, 11, 12, + + + 25, 26, 27, + 28, 29, 30, + + 31, 32, 33, + 34, 35, 36, + + + + 13, 14, 15, + 16, 17, 18, + + 19, 20, 21, + 22, 23, 24, + + + 37, 38, 39, + 40, 41, 42, + + 43, 44, 45, + 46, 47, 48 + + }; + + return StackTestHelper<ArmnnType, T, 5>( + workloadFactory, + memoryManager, + inputTensorInfo, + outputTensorInfo, + 1U, + inputData, + outputExpectedData + ); +} |