diff options
Diffstat (limited to 'src/backends/test/Pooling2dTestImpl.hpp')
| -rw-r--r-- | src/backends/test/Pooling2dTestImpl.hpp | 215 |
1 files changed, 146 insertions, 69 deletions
diff --git a/src/backends/test/Pooling2dTestImpl.hpp b/src/backends/test/Pooling2dTestImpl.hpp index 90be2897e8..eea423275c 100644 --- a/src/backends/test/Pooling2dTestImpl.hpp +++ b/src/backends/test/Pooling2dTestImpl.hpp @@ -4,6 +4,7 @@ // #pragma once +#include <string> #include <armnn/ArmNN.hpp> #include <test/TensorHelpers.hpp> @@ -13,6 +14,8 @@ #include <backends/WorkloadFactory.hpp> #include <backends/WorkloadInfo.hpp> #include <algorithm> +#include "Permute.hpp" +#include <boost/numeric/conversion/cast.hpp> template<typename T> LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadFactory, @@ -22,9 +25,10 @@ LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadF const boost::multi_array<T, 4>& input, const boost::multi_array<T, 4>& outputExpected) { - const unsigned int channelsIndex = descriptor.m_DataLayout.GetChannelsIndex(); - const unsigned int heightIndex = descriptor.m_DataLayout.GetHeightIndex(); - const unsigned int widthIndex = descriptor.m_DataLayout.GetWidthIndex(); + const armnn::DataLayoutIndexed dataLayout = descriptor.m_DataLayout; + auto heightIndex = dataLayout.GetHeightIndex(); + auto widthIndex = dataLayout.GetWidthIndex(); + auto channelsIndex = dataLayout.GetChannelsIndex(); unsigned int inputHeight = boost::numeric_cast<unsigned int>(input.shape()[heightIndex]); unsigned int inputWidth = boost::numeric_cast<unsigned int>(input.shape()[widthIndex]); @@ -36,23 +40,10 @@ LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadF unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); - armnn::TensorShape inputTensorShape; - armnn::TensorShape outputTensorShape; - - switch (descriptor.m_DataLayout.GetDataLayout()) - { - case armnn::DataLayout::NHWC: - inputTensorShape = { inputBatchSize, inputHeight, inputWidth, inputChannels }; - outputTensorShape = { outputBatchSize, outputHeight, outputWidth, outputChannels }; - break; - case armnn::DataLayout::NCHW: - default: - inputTensorShape = { inputBatchSize, inputChannels, inputHeight, inputWidth }; - outputTensorShape = { outputBatchSize, outputChannels, outputHeight, outputWidth }; - } - - armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(inputBatchSize, inputChannels, inputHeight, + inputWidth, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(outputBatchSize, outputChannels, outputHeight, + outputWidth, dataLayout); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -70,7 +61,7 @@ LayerTestResult<T, 4> SimplePooling2dTestImpl(armnn::IWorkloadFactory& workloadF armnn::Pooling2dQueueDescriptor queueDescriptor; queueDescriptor.m_Parameters = descriptor; - queueDescriptor.m_Parameters.m_DataLayout = descriptor.m_DataLayout; + queueDescriptor.m_Parameters.m_DataLayout = dataLayout; armnn::WorkloadInfo workloadInfo; AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); @@ -234,26 +225,20 @@ LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkl } template<typename T> -LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - const armnn::TensorShape& inputTensorShape, - const armnn::TensorShape& outputTensorShape, - armnn::DataLayout dataLayout, - float qScale = 1.0f, - int32_t qOffset = 0) +LayerTestResult<T, 4> SimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + const armnn::DataLayoutIndexed& dataLayout = armnn::DataLayout::NCHW, + float qScale = 1.0f, + int32_t qOffset = 0) { armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; descriptor.m_DataLayout = dataLayout; - armnn::TensorInfo inputTensorInfo(inputTensorShape, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo(outputTensorShape, armnn::GetDataType<T>()); + armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(1, 2, 4, 4, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout); // Set quantization parameters if the requested type is a quantized type. if(armnn::IsQuantizedType<T>()) @@ -264,46 +249,111 @@ LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& outputTensorInfo.SetQuantizationOffset(qOffset); } - auto input = MakeTensor<T, 4>(inputTensorInfo, + std::vector<T> inputData( QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, - 1.0f, 2.0f, 3.0f, 4.0f, + 1.0f, 2.0f, 5.0f, 6.0f, + 3.0f, 4.0f, 7.0f, 8.0f, + 9.0f, 10.0f, 13.0f, 14.0f, + 11.0f, 12.0f, 15.0f, 16.0f, + + 17.0f, 18.0f, 21.0f, 22.0f, + 19.0f, 20.0f, 23.0f, 24.0f, + 25.0f, 26.0f, 29.0f, 30.0f, + 27.0f, 28.0f, 31.0f, 32.0f, })); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, + std::vector<T> outputData( QuantizedVector<T>(qScale, qOffset, { - 1.0f, 2.5f, 4.0f, - 1.0f, 2.5f, 4.0f, - 1.0f, 2.5f, 4.0f, + 4.0f, 8.0f, + 12.0f, 16.0f, + + 20.0f, 24.0f, + 28.0f, 32.0f, })); + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector<T> tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); + + auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); + return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); } template<typename T> -LayerTestResult<T, 4> SimpleAveragePooling2dTest(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) +LayerTestResult<T, 4> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, + float qScale = 1.0f, + int32_t qOffset = 0) { - const armnn::TensorShape inputTensorShape { 1, 1, 4, 4 }; - const armnn::TensorShape outputTensorShape { 1, 1, 3, 3 }; + armnn::Pooling2dDescriptor descriptor; + descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; + descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; + descriptor.m_StrideX = descriptor.m_StrideY = 2; + descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; - return SimpleAveragePooling2dTestCommon<T>(workloadFactory, inputTensorShape, outputTensorShape, - armnn::DataLayout::NCHW, qScale, qOffset); -} + armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(1, 2, 4, 4, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout); -template<typename T> -LayerTestResult<T, 4> SimpleAveragePooling2dNhwcTest(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - const armnn::TensorShape inputTensorShape { 1, 4, 4, 1 }; - const armnn::TensorShape outputTensorShape { 1, 3, 3, 1 }; + // Set quantization parameters if the requested type is a quantized type. + if(armnn::IsQuantizedType<T>()) + { + inputTensorInfo.SetQuantizationScale(qScale); + inputTensorInfo.SetQuantizationOffset(qOffset); + outputTensorInfo.SetQuantizationScale(qScale); + outputTensorInfo.SetQuantizationOffset(qOffset); + } + + std::vector<T> inputData( + QuantizedVector<T>(qScale, qOffset, { + 2.0f, 2.0f, 6.0f, 6.0f, + 4.0f, 4.0f, 8.0f, 8.0f, + 10.0f, 12.0f, 14.0f, 16.0f, + 10.0f, 12.0f, 16.0f, 14.0f, + + 18.0f, 20.0f, 24.0f, 22.0f, + 20.0f, 18.0f, 22.0f, 24.0f, + 26.0f, 28.0f, 0.0f, 0.0f, + 26.0f, 28.0f, 0.0f, 0.0f, + })); + + std::vector<T> outputData( + QuantizedVector<T>(qScale, qOffset, { + 3.0f, 7.0f, + 11.0f, 15.0f, + + 19.0f, 23.0f, + 27.0f, 0.0f, + })); + + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; - return SimpleAveragePooling2dTestCommon<T>(workloadFactory, inputTensorShape, outputTensorShape, - armnn::DataLayout::NHWC, qScale, qOffset); + std::vector<T> tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); + + auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); + + return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); } template<typename T> @@ -356,6 +406,7 @@ LayerTestResult<T, 4> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFac template<typename T> LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, + armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, float qScale = 1.0f, int32_t qOffset = 0) { @@ -364,23 +415,49 @@ LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workl descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; descriptor.m_StrideX = descriptor.m_StrideY = 2; descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; + descriptor.m_DataLayout = dataLayout; - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, + armnn::TensorInfo inputTensorInfo = GetTensorInfo<T>(1, 2, 4, 4, dataLayout); + armnn::TensorInfo outputTensorInfo = GetTensorInfo<T>(1, 2, 2, 2, dataLayout); + + std::vector<T> inputData( QuantizedVector<T>(qScale, qOffset, { - 1.0f, 7.0f, 1.0f, 7.0f, - 1.0f, 7.0f, 1.0f, 7.0f, - 1.0f, 7.0f, 1.0f, 7.0f, - 1.0f, 7.0f, 1.0f, 7.0f, + 1.0f, 7.0f, 5.0f, 5.0f, + 1.0f, 7.0f, 5.0f, 5.0f, + 3.0f, 3.0f, 1.0f, 1.0f, + 3.0f, 3.0f, 1.0f, 1.0f, + + 1.0f, 7.0f, 0.0f, 0.0f, + 1.0f, 7.0f, 2.0f, 0.0f, + 0.0f, 2.0f, 1.0f, 1.0f, + 0.0f, 0.0f, 1.0f, 1.0f, })); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, + std::vector<T> outputData( QuantizedVector<T>(qScale, qOffset, { 5.0f, 5.0f, - 5.0f, 5.0f, + 3.0f, 1.0f, + + 5.0f, 1.0f, + 1.0f, 1.0f, })); + const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; + if (dataLayout.GetDataLayout() == armnn::DataLayout::NHWC) + { + std::vector<T> tmp(inputData.size()); + armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data()); + inputData = tmp; + + std::vector<T> tmp1(outputData.size()); + armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data()); + outputData = tmp1; + } + + auto input = MakeTensor<T, 4>(inputTensorInfo, inputData); + + auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputData); + return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); } |