diff options
Diffstat (limited to 'src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp')
| -rw-r--r-- | src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp | 569 |
1 files changed, 426 insertions, 143 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp index a708afadc8..a241aea3f3 100644 --- a/src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp +++ b/src/backends/backendsCommon/test/layerTests/Pooling3dTestImpl.cpp @@ -29,6 +29,15 @@ namespace using namespace armnnUtils; +template<typename T> +void PermuteNCDHWToNDHWC(std::vector<T> &src, armnn::TensorInfo &srcInfo) +{ + const armnn::PermutationVector NCDHWToNDHWC = { 0, 4, 1, 2, 3 }; + std::vector<T> tmp(src.size()); + armnnUtils::Permute(srcInfo.GetShape(), NCDHWToNDHWC, src.data(), tmp.data(), sizeof(T)); + src = tmp; +} + template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 5> SimplePooling3dTestImpl( armnn::IWorkloadFactory& workloadFactory, @@ -137,6 +146,7 @@ LayerTestResult<T, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -153,6 +163,7 @@ LayerTestResult<T, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon( descriptor.m_PadFront = descriptor.m_PadBack = 0; descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; unsigned int inputWidth = 3; unsigned int inputHeight = 3; @@ -169,8 +180,10 @@ LayerTestResult<T, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon( unsigned int channels = 2; unsigned int batchSize = 2; - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputDepth, inputHeight, inputWidth }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputDepth, outputHeight, outputWidth }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( batchSize, channels, inputDepth, inputHeight, + inputWidth, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( batchSize, channels, outputDepth, outputHeight, + outputWidth, dataLayout, ArmnnType); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -239,6 +252,12 @@ LayerTestResult<T, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon( }, qScale, qOffset); + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC(input, inputTensorInfo); + PermuteNCDHWToNDHWC(outputExpected, outputTensorInfo); + } + return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); @@ -249,7 +268,7 @@ LayerTestResult<T, 5> SimpleMaxPooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - const armnn::DataLayout dataLayout = armnn::DataLayout::NCDHW, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -306,16 +325,10 @@ LayerTestResult<T, 5> SimpleMaxPooling3dTestCommon( }, qScale, qOffset)); - const armnn::PermutationVector NCDHWToNDHWC = { 0, 4, 1, 2, 3 }; if (dataLayout == armnn::DataLayout::NDHWC) { - std::vector<T> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCDHWToNDHWC, inputData.data(), tmp.data(), sizeof(T)); - inputData = tmp; - - std::vector<T> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCDHWToNDHWC, outputData.data(), tmp1.data(), sizeof(T)); - outputData = tmp1; + PermuteNCDHWToNDHWC(inputData, inputTensorInfo); + PermuteNCDHWToNDHWC(outputData, outputTensorInfo); } return SimplePooling3dTestImpl<ArmnnType>( @@ -328,6 +341,7 @@ LayerTestResult<T, 5> IgnorePaddingSimpleMaxPooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -342,9 +356,10 @@ LayerTestResult<T, 5> IgnorePaddingSimpleMaxPooling3dTestCommon( descriptor.m_PadFront = 1; descriptor.m_PadBack = 1; descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + descriptor.m_DataLayout = dataLayout; - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 4, 4, 4 , dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 3, 3, 3 , dataLayout, ArmnnType); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -395,6 +410,12 @@ LayerTestResult<T, 5> IgnorePaddingSimpleMaxPooling3dTestCommon( }, qScale, qOffset); + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC(input, inputTensorInfo); + PermuteNCDHWToNDHWC(outputExpected, outputTensorInfo); + } + return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); @@ -462,16 +483,10 @@ LayerTestResult<T, 5> SimpleAveragePooling3dTestCommon( }, qScale, qOffset)); - const armnn::PermutationVector NCDHWToNDHWC = { 0, 4, 1, 2, 3 }; if (dataLayout == armnn::DataLayout::NDHWC) { - std::vector<T> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCDHWToNDHWC, inputData.data(), tmp.data(), sizeof(T)); - inputData = tmp; - - std::vector<T> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCDHWToNDHWC, outputData.data(), tmp1.data(), sizeof(T)); - outputData = tmp1; + PermuteNCDHWToNDHWC(inputData, inputTensorInfo); + PermuteNCDHWToNDHWC(outputData, outputTensorInfo); } return SimplePooling3dTestImpl<ArmnnType>( @@ -484,6 +499,7 @@ LayerTestResult<T, 5> LargeTensorsAveragePooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -498,11 +514,12 @@ LayerTestResult<T, 5> LargeTensorsAveragePooling3dTestCommon( descriptor.m_PadFront = 50; descriptor.m_PadBack = 50; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; - armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60, 68 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13, 15 }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 5, 3, 52, 60, 68, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 5, 3, 11, 13, 14, dataLayout, ArmnnType); - // Set quantization parameters if the requested type is a quantized type. + // Set quantization parameters if the requested type is a quantized type.armnnUtils::GetTensorInfo( if(armnn::IsQuantizedType<T>()) { inputTensorInfo.SetQuantizationScale(qScale); @@ -535,6 +552,7 @@ LayerTestResult<T, 5> IgnorePaddingSimpleAveragePooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -549,9 +567,11 @@ LayerTestResult<T, 5> IgnorePaddingSimpleAveragePooling3dTestCommon( descriptor.m_PadFront = 1; descriptor.m_PadBack = 1; descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + descriptor.m_DataLayout = dataLayout; - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType); + + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo ( 1, 1, 4, 4, 4, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 3, 3, 3, dataLayout, ArmnnType); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -602,6 +622,12 @@ LayerTestResult<T, 5> IgnorePaddingSimpleAveragePooling3dTestCommon( }, qScale, qOffset); + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC(input, inputTensorInfo); + PermuteNCDHWToNDHWC(outputExpected, outputTensorInfo); + } + return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); @@ -612,7 +638,7 @@ LayerTestResult<T, 5> SimpleL2Pooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, - armnn::DataLayout dataLayout = armnn::DataLayout::NCDHW, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -669,16 +695,10 @@ LayerTestResult<T, 5> SimpleL2Pooling3dTestCommon( }, qScale, qOffset)); - const armnn::PermutationVector NCDHWToNDHWC = { 0, 4, 1, 2, 3 }; if (dataLayout == armnn::DataLayout::NDHWC) { - std::vector<T> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCDHWToNDHWC, inputData.data(), tmp.data(), sizeof(T)); - inputData = tmp; - - std::vector<T> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCDHWToNDHWC, outputData.data(), tmp1.data(), sizeof(T)); - outputData = tmp1; + PermuteNCDHWToNDHWC(inputData, inputTensorInfo); + PermuteNCDHWToNDHWC(outputData, outputTensorInfo); } return SimplePooling3dTestImpl<ArmnnType>( @@ -691,6 +711,7 @@ LayerTestResult<T, 5> IgnorePaddingSimpleL2Pooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -705,9 +726,10 @@ LayerTestResult<T, 5> IgnorePaddingSimpleL2Pooling3dTestCommon( descriptor.m_PadFront = 1; descriptor.m_PadBack = 1; descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; + descriptor.m_DataLayout = dataLayout; - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 4, 4, 4, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 3, 3, 3, dataLayout,ArmnnType); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -794,52 +816,105 @@ LayerTestResult<T, 5> IgnorePaddingSimpleL2Pooling3dTestCommon( }, qScale, qOffset); + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC(input, inputTensorInfo); + PermuteNCDHWToNDHWC(outputExpected, outputTensorInfo); + } + return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); } template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolTestCommon( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 1, 3, 1, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 2, 2, 1, dataLayout, ArmnnType); + + armnn::Pooling3dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; + descriptor.m_PoolWidth = 1; + descriptor.m_PoolHeight = 2; + descriptor.m_PoolDepth = 3; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 2; + descriptor.m_StrideZ = 1; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 2; + descriptor.m_PadBottom = 0; + descriptor.m_PadFront = 1; + descriptor.m_PadBack = 2; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; + + // Construct input data. + auto input = QuantizedVector<T>( { 1.0f, 3.0f, 4.0f, }, qScale, qOffset); + + // These were calculated manually. + auto outputExpected = QuantizedVector<T>( { 0.0f, 3.0f, 0.0f, 3.0f, }, qScale, qOffset); + + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC<T>(input, inputTensorInfo); + PermuteNCDHWToNDHWC<T>(outputExpected, outputTensorInfo); + } + + return SimplePooling3dTestImpl<ArmnnType>( + workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, + input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 5> AsymmetricNonSquareMaxPooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { - armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3, 1 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2, 1 }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 1, 3, 1, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 2, 2, 1, dataLayout, ArmnnType); armnn::Pooling3dDescriptor descriptor; descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; descriptor.m_PoolWidth = 1; descriptor.m_PoolHeight = 2; descriptor.m_PoolDepth = 3; - descriptor.m_StrideX = 0; + descriptor.m_StrideX = 1; descriptor.m_StrideY = 2; descriptor.m_StrideZ = 1; descriptor.m_PadLeft = 0; descriptor.m_PadRight = 0; - descriptor.m_PadTop = 2; + descriptor.m_PadTop = 1; descriptor.m_PadBottom = 0; descriptor.m_PadFront = 1; descriptor.m_PadBack = 2; descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; // Construct input data. - auto input = QuantizedVector<T>( - { - 1.0f, 3.0f, 4.0f, - }, - qScale, qOffset); + auto input = QuantizedVector<T>( { 1.0f, 3.0f, 4.0f, }, qScale, qOffset); // These were calculated manually. - auto outputExpected = QuantizedVector<T>( - { - 0.0f, 3.0f, 0.0f, 3.0f, - }, - qScale, qOffset); + auto outputExpected = QuantizedVector<T>( { 1.0f, 4.0f, 1.0f, 4.0f, }, qScale, qOffset); + + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC<T>(input, inputTensorInfo); + PermuteNCDHWToNDHWC<T>(outputExpected, outputTensorInfo); + } return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, @@ -847,22 +922,23 @@ LayerTestResult<T, 5> AsymmetricNonSquareMaxPooling3dTestCommon( } template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T, 5> AsymmetricNonSquareAveragePooling3dTestCommon( +LayerTestResult<T, 5> AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { - armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3, 1 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2, 1 }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 1, 3, 1, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 2, 2, 1, dataLayout, ArmnnType); armnn::Pooling3dDescriptor descriptor; descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; descriptor.m_PoolWidth = 1; descriptor.m_PoolHeight = 2; descriptor.m_PoolDepth = 3; - descriptor.m_StrideX = 0; + descriptor.m_StrideX = 1; descriptor.m_StrideY = 2; descriptor.m_StrideZ = 1; descriptor.m_PadLeft = 0; @@ -873,36 +949,81 @@ LayerTestResult<T, 5> AsymmetricNonSquareAveragePooling3dTestCommon( descriptor.m_PadBack = 2; descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; // Construct input data. - auto input = QuantizedVector<T>( - { - 1.0f, 3.0f, 4.0f, - }, - qScale, qOffset); + auto input = QuantizedVector<T>({ 1.0f, 3.0f, 4.0f, }, qScale, qOffset); // These were calculated manually. - auto outputExpected = QuantizedVector<T>( - { - 0.0f, 2.0f, 0.0f, 2.0f, - }, - qScale, qOffset); + auto outputExpected = QuantizedVector<T>( { 0.0f, 2.0f, 0.0f, 2.0f, }, qScale, qOffset); + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC<T>(input, inputTensorInfo); + PermuteNCDHWToNDHWC<T>(outputExpected, outputTensorInfo); + } return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); } template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> -LayerTestResult<T, 5> AsymmetricNonSquareL2Pooling3dTestCommon( +LayerTestResult<T, 5> AsymmetricNonSquareAveragePooling3dTestCommon( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 1, 3, 1, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 2, 2, 1, dataLayout, ArmnnType); + + armnn::Pooling3dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = 1; + descriptor.m_PoolHeight = 2; + descriptor.m_PoolDepth = 3; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 2; + descriptor.m_StrideZ = 1; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 0; + descriptor.m_PadFront = 1; + descriptor.m_PadBack = 2; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; + + // Construct input data. + auto input = QuantizedVector<T>( { 1.0f, 3.0f, 4.0f, }, qScale, qOffset); + + // These were calculated manually. + auto outputExpected = QuantizedVector<T>( { 1.0f, 3.5f, 1.0f, 3.5f, }, qScale, qOffset); + + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC<T>(input, inputTensorInfo); + PermuteNCDHWToNDHWC<T>(outputExpected, outputTensorInfo); + } + return SimplePooling3dTestImpl<ArmnnType>( + workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, + input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { - armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3, 1 }, ArmnnType); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2, 1 }, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 1, 3, 1, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 2, 2, 1, dataLayout, ArmnnType); armnn::Pooling3dDescriptor descriptor; descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; @@ -920,20 +1041,19 @@ LayerTestResult<T, 5> AsymmetricNonSquareL2Pooling3dTestCommon( descriptor.m_PadBack = 2; descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; // Construct input data. - auto input = QuantizedVector<T>( - { - 1.0f, 3.0f, 4.0f, - }, - qScale, qOffset); + auto input = QuantizedVector<T>( { 1.0f, 3.0f, 4.0f, }, qScale, qOffset); // These were calculated manually. - auto outputExpected = QuantizedVector<T>( - { - 0.0f, 2.2360679775f, 0.0f, 2.2360679775f, - }, - qScale, qOffset); + auto outputExpected = QuantizedVector<T>( { 0.0f, 2.2360679775f, 0.0f, 2.2360679775f, }, qScale, qOffset); + + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC<T>(input, inputTensorInfo); + PermuteNCDHWToNDHWC<T>(outputExpected, outputTensorInfo); + } return SimplePooling3dTestImpl<ArmnnType>( workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, @@ -941,6 +1061,53 @@ LayerTestResult<T, 5> AsymmetricNonSquareL2Pooling3dTestCommon( } template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, 5> AsymmetricNonSquareL2Pooling3dTestCommon( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout, + float qScale = 1.0f, + int32_t qOffset = 0) +{ + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 1, 3, 1, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( 1, 1, 2, 2, 1, dataLayout, ArmnnType); + + armnn::Pooling3dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; + descriptor.m_PoolWidth = 1; + descriptor.m_PoolHeight = 2; + descriptor.m_PoolDepth = 3; + descriptor.m_StrideX = 1; + descriptor.m_StrideY = 2; + descriptor.m_StrideZ = 1; + descriptor.m_PadLeft = 0; + descriptor.m_PadRight = 0; + descriptor.m_PadTop = 1; + descriptor.m_PadBottom = 0; + descriptor.m_PadFront = 1; + descriptor.m_PadBack = 2; + descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; + + // Construct input data. + auto input = QuantizedVector<T>( { 1.0f, 3.0f, 4.0f, }, qScale, qOffset); + + // These were calculated manually. + auto outputExpected = QuantizedVector<T>( { 1.0f, 3.53553390593f, 1.0f, 3.53553390593f, }, qScale, qOffset); + + if (dataLayout == armnn::DataLayout::NDHWC) + { + PermuteNCDHWToNDHWC<T>(input, inputTensorInfo); + PermuteNCDHWToNDHWC<T>(outputExpected, outputTensorInfo); + } + + return SimplePooling3dTestImpl<ArmnnType>( + workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, + input, outputExpected, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); +} + +template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 5> ComparePooling3dTestCommon( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, @@ -948,6 +1115,7 @@ LayerTestResult<T, 5> ComparePooling3dTestCommon( const armnn::ITensorHandleFactory& tensorHandleFactory, const armnn::ITensorHandleFactory& refTensorHandleFactory, armnn::PoolingAlgorithm poolingType, + const armnn::DataLayout dataLayout, float qScale = 1.0f, int32_t qOffset = 0) { @@ -970,14 +1138,10 @@ LayerTestResult<T, 5> ComparePooling3dTestCommon( const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; const unsigned int outputDepth = (inputDepth + 2 * padZ + strideZ - poolSize) / strideZ; - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth, inputDepth }; - unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth, outputDepth }; - - inputTensorInfo = armnn::TensorInfo(5, inputShape, ArmnnType); - outputTensorInfo = armnn::TensorInfo(5, outputShape, ArmnnType); + armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo(batchSize, channelCount, inputDepth, inputHeight, + inputWidth, dataLayout, ArmnnType); + armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(batchSize, channelCount, outputDepth, outputHeight, + outputWidth, dataLayout, ArmnnType); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -991,7 +1155,6 @@ LayerTestResult<T, 5> ComparePooling3dTestCommon( std::vector<T> input = MakeRandomTensor<T>(inputTensorInfo, 81715); std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); std::vector<T> expectedOutput(outputTensorInfo.GetNumElements()); - LayerTestResult<T, 5> comparisonResult(outputTensorInfo); std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); @@ -1015,8 +1178,10 @@ LayerTestResult<T, 5> ComparePooling3dTestCommon( data.m_Parameters.m_PadFront = padZ; data.m_Parameters.m_PadBack = padZ; data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; + data.m_Parameters.m_DataLayout = dataLayout; - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refTensorHandleFactory.CreateTensorHandle(outputTensorInfo); + std::unique_ptr<armnn::ITensorHandle> outputHandleRef = + refTensorHandleFactory.CreateTensorHandle(outputTensorInfo); std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refTensorHandleFactory.CreateTensorHandle(inputTensorInfo); // Don't execute if Pooling is not supported, as an exception will be raised. @@ -1062,34 +1227,36 @@ LayerTestResult<T, 5> ComparePooling3dTestCommon( return comparisonResult; } - } // anonymous namespace LayerTestResult<float, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1Uint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory, 0.1f, 128); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout, 0.1f, 128); } LayerTestResult<int16_t, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1Int16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> SimpleMaxPooling3dTest( @@ -1125,28 +1292,31 @@ LayerTestResult<int16_t, 5> SimpleMaxPooling3dInt16Test( LayerTestResult<float, 5> IgnorePaddingSimpleMaxPooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> IgnorePaddingSimpleMaxPooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5); + workloadFactory, memoryManager, tensorHandleFactory,dataLayout, 1.0f, -5); } LayerTestResult<int16_t, 5> IgnorePaddingSimpleMaxPooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> SimpleAveragePooling3dTest( @@ -1212,163 +1382,271 @@ LayerTestResult<int16_t, 5> SimpleL2Pooling3dInt16Test( LayerTestResult<float, 5> LargeTensorsAveragePooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return LargeTensorsAveragePooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> LargeTensorsAveragePooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return LargeTensorsAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory, 0.5, -1); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout, 0.5, -1); } LayerTestResult<int16_t, 5> LargeTensorsAveragePooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return LargeTensorsAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> IgnorePaddingSimpleAveragePooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> IgnorePaddingSimpleAveragePooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout, 1.0f, -5); } LayerTestResult<int16_t, 5> IgnorePaddingSimpleAveragePooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> IgnorePaddingSimpleL2Pooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> IgnorePaddingSimpleL2Pooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout, 1.0f, -5); } LayerTestResult<int16_t, 5> IgnorePaddingSimpleL2Pooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> AsymmetricNonSquareMaxPooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> AsymmetricNonSquareMaxPooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<int16_t, 5> AsymmetricNonSquareMaxPooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<float, 5> AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<uint8_t, 5> AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::QAsymmU8>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<int16_t, 5> AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolInt16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareMaxPooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::QSymmS16>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> AsymmetricNonSquareAveragePooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> AsymmetricNonSquareAveragePooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<int16_t, 5> AsymmetricNonSquareAveragePooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<float, 5> AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<uint8_t, 5> AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::QAsymmU8>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<int16_t, 5> AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolInt16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareAveragePooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::QSymmS16>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> AsymmetricNonSquareL2Pooling3dTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<uint8_t, 5> AsymmetricNonSquareL2Pooling3dUint8Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<int16_t, 5> AsymmetricNonSquareL2Pooling3dInt16Test( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::ITensorHandleFactory& tensorHandleFactory) + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) { return AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, tensorHandleFactory); + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<float, 5> AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::Float32>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<uint8_t, 5> AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolUint8Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::QAsymmU8>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); +} + +LayerTestResult<int16_t, 5> AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolInt16Test( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::ITensorHandleFactory& tensorHandleFactory, + const armnn::DataLayout dataLayout) +{ + return AsymmetricNonSquareL2Pooling3dWithPaddingOnlyPoolTestCommon<armnn::DataType::QSymmS16>( + workloadFactory, memoryManager, tensorHandleFactory, dataLayout); } LayerTestResult<float, 5> ComparePooling3dTest( @@ -1377,10 +1655,12 @@ LayerTestResult<float, 5> ComparePooling3dTest( armnn::IWorkloadFactory& refWorkloadFactory, const armnn::ITensorHandleFactory& tensorHandleFactory, const armnn::ITensorHandleFactory& refTensorHandleFactory, - armnn::PoolingAlgorithm poolingType) + armnn::PoolingAlgorithm poolingType, + const armnn::DataLayout dataLayout) { return ComparePooling3dTestCommon<armnn::DataType::Float32>( - workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, poolingType); + workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, + poolingType, dataLayout); } LayerTestResult<uint8_t, 5> ComparePooling3dUint8Test( @@ -1389,11 +1669,12 @@ LayerTestResult<uint8_t, 5> ComparePooling3dUint8Test( armnn::IWorkloadFactory& refWorkloadFactory, const armnn::ITensorHandleFactory& tensorHandleFactory, const armnn::ITensorHandleFactory& refTensorHandleFactory, - armnn::PoolingAlgorithm poolingType) + armnn::PoolingAlgorithm poolingType, + const armnn::DataLayout dataLayout) { return ComparePooling3dTestCommon<armnn::DataType::QAsymmU8>( workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, - poolingType, 0.1f, 128); + poolingType, dataLayout, 0.1f, 128); } LayerTestResult<int16_t, 5> ComparePooling3dInt16Test( @@ -1402,8 +1683,10 @@ LayerTestResult<int16_t, 5> ComparePooling3dInt16Test( armnn::IWorkloadFactory& refWorkloadFactory, const armnn::ITensorHandleFactory& tensorHandleFactory, const armnn::ITensorHandleFactory& refTensorHandleFactory, - armnn::PoolingAlgorithm poolingType) + armnn::PoolingAlgorithm poolingType, + const armnn::DataLayout dataLayout) { return ComparePooling3dTestCommon<armnn::DataType::QSymmS16>( - workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, poolingType); + workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, + poolingType, dataLayout); }
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