diff options
Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp | 195 |
1 files changed, 94 insertions, 101 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp index 6d83b1ca99..54052073a9 100644 --- a/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/ActivationTestImpl.cpp @@ -17,8 +17,6 @@ #include <test/TensorHelpers.hpp> -#include <boost/multi_array.hpp> - #include <algorithm> template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> @@ -58,9 +56,7 @@ LayerTestResult<T, 4> BoundedReLuTestCommon( outputTensorInfo.SetQuantizationOffset(outputOffset); } - LayerTestResult<T, 4> result(inputTensorInfo); - - auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); @@ -80,15 +76,16 @@ LayerTestResult<T, 4> BoundedReLuTestCommon( inputHandle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), inputData.data()); workload->Execute(); - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputExpectedData); + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - return result; + return LayerTestResult<T, 4>(actualOutput, + outputExpectedData, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); } LayerTestResult<float, 4> BoundedReLuUpperAndLowerBoundTest( @@ -245,7 +242,7 @@ struct BoundedReLuRandomInputTestTraits } }; -boost::multi_array<float, 4> BoundedReLuRandomInputTest( +std::vector<float> BoundedReLuRandomInputTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, @@ -257,11 +254,10 @@ boost::multi_array<float, 4> BoundedReLuRandomInputTest( const armnn::TensorInfo inputTensorInfo = BoundedReLuRandomInputTestTraits::GetInputTensorInfo(); const armnn::TensorInfo outputTensorInfo = BoundedReLuRandomInputTestTraits::GetOutputTensorInfo(); - boost::multi_array<float, 4> output(GetTensorShapeAsArray<4>(outputTensorInfo)); - // Min/max random values passed to MakeRandomTensor are purposely outside of the ReLu // range [lowerBound, upperBound]. - auto input = MakeRandomTensor<float, 4>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); + std::vector<float> input = MakeRandomTensor<float>(inputTensorInfo, 4605828, lowerBound - 5.0f, upperBound * 2.0f); + std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); @@ -278,13 +274,13 @@ boost::multi_array<float, 4> BoundedReLuRandomInputTest( inputHandle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), input.data()); workload->Execute(); - CopyDataFromITensorHandle(&output[0][0][0][0], outputHandle.get()); + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - return output; + return actualOutput; } } // namespace @@ -305,16 +301,16 @@ LayerTestResult<float, 4> CompareBoundedReLuTest( activationDescriptor.m_A = upperBound; activationDescriptor.m_B = lowerBound; - result.output = BoundedReLuRandomInputTest( + result.m_ActualData = BoundedReLuRandomInputTest( workloadFactory, memoryManager, tensorHandleFactory, 0.0f, upperBound, activationDescriptor); - result.outputExpected = BoundedReLuRandomInputTest( + result.m_ExpectedData = BoundedReLuRandomInputTest( refWorkloadFactory, nullptr, refTensorHandleFactory, 0.0f, upperBound, activationDescriptor); return result; } template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T,4> ConstantLinearActivationTestCommon( +LayerTestResult<T, 4> ConstantLinearActivationTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, @@ -344,7 +340,6 @@ LayerTestResult<T,4> ConstantLinearActivationTestCommon( outputTensorInfo.SetQuantizationOffset(qOffset); } - LayerTestResult<T, 4> ret(outputTensorInfo); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); @@ -362,17 +357,20 @@ LayerTestResult<T,4> ConstantLinearActivationTestCommon( inputHandle->Allocate(); outputHandle->Allocate(); - boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 7123561); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + std::vector<T> input = MakeRandomTensor<T>(inputTensorInfo, 7123561); + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); - workload->Execute(); + CopyDataToITensorHandle(inputHandle.get(), input.data()); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); + workload->Execute(); - // Ensure output equals input. - ret.outputExpected = input; + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - return ret; + // Use input as ExpectedData as tensor doesn't change. + return LayerTestResult<T, 4>(actualOutput, + input, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); } LayerTestResult<float, 4> ConstantLinearActivationTest( @@ -441,9 +439,11 @@ LayerTestResult<T, 4> SimpleActivationTest( outputTensorInfo.SetQuantizationOffset(outOffset); } - LayerTestResult<T, 4> result(inputTensorInfo); + std::vector<T> input = armnnUtils::QuantizedVector<T>(inputData, scale, offset); - auto input = MakeTensor<T, 4>(inputTensorInfo, armnnUtils::QuantizedVector<T>(inputData, scale, offset)); + // Calculated outputExpected manually. + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); + std::vector<T> outputExpected = armnnUtils::QuantizedVector<T>(outputExpectedData, outScale, outOffset); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); @@ -463,17 +463,16 @@ LayerTestResult<T, 4> SimpleActivationTest( inputHandle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), input.data()); workload->Execute(); - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - // Calculated manually. - result.outputExpected = - MakeTensor<T, 4>(outputTensorInfo, armnnUtils::QuantizedVector<T>(outputExpectedData, outScale, outOffset)); + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - return result; + return LayerTestResult<T, 4>(actualOutput, + outputExpected, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); } template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> @@ -497,8 +496,8 @@ LayerTestResult<T, 4> SimpleSigmoidTestCommon( { return 1.0f / (1.0f + std::exp(-value)); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> m_OutputExpected(inputData.size()); + std::transform(inputData.begin(), inputData.end(), m_OutputExpected.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -511,7 +510,7 @@ LayerTestResult<T, 4> SimpleSigmoidTestCommon( inputData, 1.f / 256.f, 0, - outputExpectedData); + m_OutputExpected); } LayerTestResult<float, 4> SimpleSigmoidTest( @@ -561,8 +560,8 @@ LayerTestResult<T, 4> ReLuTestCommon( { return std::fmax(0.0f, value); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> outputExpected(inputData.size()); + std::transform(inputData.begin(), inputData.end(), outputExpected.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -575,7 +574,7 @@ LayerTestResult<T, 4> ReLuTestCommon( inputData, qScale, qOffset, - outputExpectedData); + outputExpected); } LayerTestResult<int16_t, 4> ReLuInt16Test( @@ -625,8 +624,8 @@ LayerTestResult<T, 4> BoundedReLuTestCommon( { return std::min(a, std::max(b, value)); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> outputExpected(inputData.size()); + std::transform(inputData.begin(), inputData.end(), outputExpected.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -639,7 +638,7 @@ LayerTestResult<T, 4> BoundedReLuTestCommon( inputData, qScale, qOffset, - outputExpectedData); + outputExpected); } LayerTestResult<int16_t, 4> BoundedReLuInt16Test( @@ -672,8 +671,8 @@ LayerTestResult<T, 4> SoftReLuTestCommon( { return std::log(1.0f + std::exp(value)); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> outputExpected(inputData.size()); + std::transform(inputData.begin(), inputData.end(), outputExpected.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -686,7 +685,7 @@ LayerTestResult<T, 4> SoftReLuTestCommon( inputData, qScale, qOffset, - outputExpectedData); + outputExpected); } LayerTestResult<float, 4> SoftReLuTest( @@ -735,8 +734,8 @@ LayerTestResult<T, 4> LeakyReLuTestCommon( { return value > 0.0f ? value : (value * a); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> outputExpected(inputData.size()); + std::transform(inputData.begin(), inputData.end(), outputExpected.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -749,7 +748,7 @@ LayerTestResult<T, 4> LeakyReLuTestCommon( inputData, qScale, qOffset, - outputExpectedData); + outputExpected); } LayerTestResult<float, 4> LeakyReLuTest( @@ -797,8 +796,8 @@ LayerTestResult<T, 4> AbsTestCommon( { return std::abs(value); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> outputExpected(inputData.size()); + std::transform(inputData.begin(), inputData.end(), outputExpected.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -811,7 +810,7 @@ LayerTestResult<T, 4> AbsTestCommon( inputData, qScale, qOffset, - outputExpectedData); + outputExpected); } LayerTestResult<float, 4> AbsTest( @@ -856,17 +855,15 @@ LayerTestResult<float, 5> SqrtNNTest( { return std::sqrt(value); }; - std::vector<float> outputExpectedData(inputDataSize); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> expectedOutput(inputDataSize); + std::transform(inputData.begin(), inputData.end(), expectedOutput.begin(), f); armnn::TensorInfo inputTensorInfo( { 1u, 2u, 3u, 4u, 5u }, armnn::DataType::Float32); armnn::TensorInfo outputTensorInfo( { 1u, 2u, 3u, 4u, 5u }, armnn::DataType::Float32); - LayerTestResult<float, 5> result(inputTensorInfo); - - auto input = MakeTensor<float, 5>(inputTensorInfo, inputData); + std::vector<float> actualOutput(outputTensorInfo.GetNumElements()); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); @@ -883,16 +880,16 @@ LayerTestResult<float, 5> SqrtNNTest( inputHandle->Allocate(); outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), inputData.data()); workload->Execute(); - CopyDataFromITensorHandle(&result.output[0][0][0][0][0], outputHandle.get()); - - // Calculated manually. - result.outputExpected = MakeTensor<float, 5>(outputTensorInfo, outputExpectedData); + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); - return result; + return LayerTestResult<float, 5>(actualOutput, + expectedOutput, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); }; template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> @@ -915,8 +912,8 @@ LayerTestResult<T, 4> SqrtTestCommon( { return std::sqrt(value); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> expectedOutput(inputData.size()); + std::transform(inputData.begin(), inputData.end(), expectedOutput.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -929,7 +926,7 @@ LayerTestResult<T, 4> SqrtTestCommon( inputData, qScale, qOffset, - outputExpectedData); + expectedOutput); } LayerTestResult<float, 4> SqrtTest( @@ -976,8 +973,8 @@ LayerTestResult<T, 4> SquareTestCommon( { return std::pow(value,2); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> expectedOutput(inputData.size()); + std::transform(inputData.begin(), inputData.end(), expectedOutput.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -990,7 +987,7 @@ LayerTestResult<T, 4> SquareTestCommon( inputData, qScale, qOffset, - outputExpectedData); + expectedOutput); } LayerTestResult<float, 4> SquareTest( @@ -1040,8 +1037,8 @@ LayerTestResult<T, 4> TanhTestCommon( { return a * tanhf(b * value); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> expectedOutput(inputData.size()); + std::transform(inputData.begin(), inputData.end(), expectedOutput.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -1054,7 +1051,7 @@ LayerTestResult<T, 4> TanhTestCommon( inputData, qScale, qOffset, - outputExpectedData); + expectedOutput); } LayerTestResult<float, 4> TanhTest( @@ -1104,8 +1101,8 @@ LayerTestResult<T, 4> EluTestCommon( { return (value >= 0) ? value : a * (expf(value) - 1); }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> expectedOutput(inputData.size()); + std::transform(inputData.begin(), inputData.end(), expectedOutput.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -1118,7 +1115,7 @@ LayerTestResult<T, 4> EluTestCommon( inputData, qScale, qOffset, - outputExpectedData); + expectedOutput); } LayerTestResult<float, 4> EluTest( @@ -1172,8 +1169,8 @@ LayerTestResult<T, 4> HardSwishTestCommon( float result = hardSwish_step1 / 6; return result; }; - std::vector<float> outputExpectedData(inputData.size()); - std::transform(inputData.begin(), inputData.end(), outputExpectedData.begin(), f); + std::vector<float> expectedOutput(inputData.size()); + std::transform(inputData.begin(), inputData.end(), expectedOutput.begin(), f); return SimpleActivationTest<ArmnnType>(workloadFactory, memoryManager, @@ -1186,7 +1183,7 @@ LayerTestResult<T, 4> HardSwishTestCommon( inputData, qScale, qOffset, - outputExpectedData); + expectedOutput); } LayerTestResult<float, 4> HardSwishTest( @@ -1216,7 +1213,7 @@ LayerTestResult<int16_t, 4> HardSwishInt16Test( template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T,4> CompareActivationTestImpl( +LayerTestResult<T, 4> CompareActivationTestImpl( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::IWorkloadFactory& refWorkloadFactory, @@ -1258,17 +1255,9 @@ LayerTestResult<T,4> CompareActivationTestImpl( minVal = 0.f; } - boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 21453, minVal, 10.f); - - - LayerTestResult<T,4> ret(outputTensorInfo); - auto boostArrayExtents = boost::extents - [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(batchSize)] - [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(channels)] - [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(height)] - [armnn::numeric_cast<boost::multi_array_types::extent_gen::index>(width)]; - ret.output.resize(boostArrayExtents); - ret.outputExpected.resize(boostArrayExtents); + std::vector<T> input = MakeRandomTensor<T>(inputTensorInfo, 21453, minVal, 10.f); + std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); + std::vector<T> expectedOutput(outputTensorInfo.GetNumElements()); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); @@ -1299,19 +1288,23 @@ LayerTestResult<T,4> CompareActivationTestImpl( inputHandleRef->Allocate(); outputHandleRef->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); + CopyDataToITensorHandle(inputHandle.get(), input.data()); + CopyDataToITensorHandle(inputHandleRef.get(), input.data()); workload->Execute(); workloadRef->Execute(); - CopyDataFromITensorHandle(&ret.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&ret.outputExpected[0][0][0][0], outputHandleRef.get()); + CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); + CopyDataFromITensorHandle(expectedOutput.data(), outputHandleRef.get()); + + return LayerTestResult<T, 4>(actualOutput, + expectedOutput, + outputHandle->GetShape(), + outputTensorInfo.GetShape()); - return ret; } -LayerTestResult<float,4> CompareActivationTest( +LayerTestResult<float, 4> CompareActivationTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::IWorkloadFactory& refWorkloadFactory, @@ -1325,7 +1318,7 @@ LayerTestResult<float,4> CompareActivationTest( refTensorHandleFactory, f, batchSize); } -LayerTestResult<uint8_t,4> CompareActivationUint8Test( +LayerTestResult<uint8_t, 4> CompareActivationUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::IWorkloadFactory& refWorkloadFactory, @@ -1338,7 +1331,7 @@ LayerTestResult<uint8_t,4> CompareActivationUint8Test( tensorHandleFactory, refTensorHandleFactory, f, 5, 0.1f, 50); } -LayerTestResult<int16_t,4> CompareActivationInt16Test( +LayerTestResult<int16_t, 4> CompareActivationInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, armnn::IWorkloadFactory& refWorkloadFactory, |