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Diffstat (limited to 'src/backends/test/Pooling2dTestImpl.hpp')
| -rw-r--r-- | src/backends/test/Pooling2dTestImpl.hpp | 1236 |
1 files changed, 0 insertions, 1236 deletions
diff --git a/src/backends/test/Pooling2dTestImpl.hpp b/src/backends/test/Pooling2dTestImpl.hpp deleted file mode 100644 index eea423275c..0000000000 --- a/src/backends/test/Pooling2dTestImpl.hpp +++ /dev/null @@ -1,1236 +0,0 @@ -// -// Copyright © 2017 Arm Ltd. All rights reserved. -// SPDX-License-Identifier: MIT -// -#pragma once - -#include <string> -#include <armnn/ArmNN.hpp> - -#include <test/TensorHelpers.hpp> -#include "QuantizeHelper.hpp" - -#include <backends/CpuTensorHandle.hpp> -#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, - armnn::Pooling2dDescriptor descriptor, - float qScale, - int32_t qOffset, - const boost::multi_array<T, 4>& input, - const boost::multi_array<T, 4>& outputExpected) -{ - 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]); - unsigned int inputChannels = boost::numeric_cast<unsigned int>(input.shape()[channelsIndex]); - unsigned int inputBatchSize = boost::numeric_cast<unsigned int>(input.shape()[0]); - - unsigned int outputHeight = boost::numeric_cast<unsigned int>(outputExpected.shape()[heightIndex]); - unsigned int outputWidth = boost::numeric_cast<unsigned int>(outputExpected.shape()[widthIndex]); - unsigned int outputChannels = boost::numeric_cast<unsigned int>(outputExpected.shape()[channelsIndex]); - unsigned int outputBatchSize = boost::numeric_cast<unsigned int>(outputExpected.shape()[0]); - - 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>()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - LayerTestResult<T, 4> result(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Pooling2dQueueDescriptor queueDescriptor; - queueDescriptor.m_Parameters = descriptor; - queueDescriptor.m_Parameters.m_DataLayout = dataLayout; - - armnn::WorkloadInfo workloadInfo; - AddInputToWorkload(queueDescriptor, workloadInfo, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(queueDescriptor, workloadInfo, outputTensorInfo, outputHandle.get()); - - // Don't execute if Pooling is not supported, as an exception will be raised. - armnn::BackendId backend = workloadFactory.GetBackendId(); - const size_t reasonIfUnsupportedMaxLen = 255; - char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; - result.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, - queueDescriptor.m_Parameters, - reasonIfUnsupported, reasonIfUnsupportedMaxLen); - if (!result.supported) - { - return result; - } - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(queueDescriptor, workloadInfo); - - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - - result.outputExpected = outputExpected; - - return result; -} - -// -// Tests max pooling with the following parameters: -// -// Pooling size: 3x3 -// Stride: (2,4) -// input size: 8x13 -// channels: 2 -// batch size: 2 -// -template<typename T> -LayerTestResult<T, 4> SimpleMaxPooling2dSize3x3Stride2x4TestCommon(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 4; - // forceNoPadding is mainly used for compatibility with ARM Compute. - // As of 16/05/2017, it errors if padX or padY are equal to or greater than the pool size. - descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; - descriptor.m_PadTop = descriptor.m_PadBottom = 0; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - unsigned int inputWidth = 8; - unsigned int inputHeight = 13; - unsigned int outputWidth = - (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / - descriptor.m_StrideX; - unsigned int outputHeight = - (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / - descriptor.m_StrideY; - unsigned int channels = 2; - unsigned int batchSize = 2; - - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); - - // 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<float> singleChannelData({ - 0.0f, 4.0f, 8.0f, 1.0f, 6.0f, 4.0f, 5.0f, 8.0f, - 1.0f, 1.0f, 6.0f, 0.0f, 3.0f, 7.0f, 4.0f, 7.0f, - 8.0f, 5.0f, 0.0f, 0.0f, 8.0f, 3.0f, 4.0f, 3.0f, - 8.0f, 2.0f, 5.0f, 4.0f, 1.0f, 9.0f, 2.0f, 0.0f, - 5.0f, 4.0f, 5.0f, 0.0f, 0.0f, 0.0f, 7.0f, 2.0f, - 1.0f, 2.0f, 6.0f, 2.0f, 7.0f, 9.0f, 5.0f, 2.0f, - 9.0f, 7.0f, 3.0f, 1.0f, 3.0f, 4.0f, 8.0f, 3.0f, - 1.0f, 0.0f, 0.0f, 5.0f, 5.0f, 4.0f, 2.0f, 0.0f, - 6.0f, 4.0f, 3.0f, 6.0f, 9.0f, 5.0f, 5.0f, 6.0f, - 8.0f, 7.0f, 9.0f, 6.0f, 1.0f, 4.0f, 1.0f, 9.0f, - 7.0f, 1.0f, 9.0f, 2.0f, 9.0f, 9.0f, 8.0f, 1.0f, - 4.0f, 4.0f, 5.0f, 9.0f, 2.0f, 6.0f, 6.0f, 4.0f, - 3.0f, 5.0f, 4.0f, 0.0f, 1.0f, 5.0f, 9.0f, 7.0f, - }); - - // Constructs input data. - std::vector<float> inputData; - auto negator = [](float f) { return -f; }; - - // First image (two channels where the second channel is the negative of the first one). - inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); - std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); - - // Second image (same as first image). - inputData.insert(inputData.end(), singleChannelData.begin(), singleChannelData.end()); - std::transform(singleChannelData.begin(), singleChannelData.end(), std::back_inserter(inputData), negator); - - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); - - // These were calculated manually. - auto shape(GetTensorShapeAsArray<4>(outputTensorInfo)); - boost::multi_array<T, 4> outputExpected(shape); - if (forceNoPadding) - { - outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 8.0f, 8.0f, 8.0f, - 9.0f, 7.0f, 9.0f, - 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, -3.0f, - -1.0f, 0.0f, 0.0f, - -1.0f, -1.0f, -1.0f, - - 8.0f, 8.0f, 8.0f, - 9.0f, 7.0f, 9.0f, - 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, -3.0f, - -1.0f, 0.0f, 0.0f, - -1.0f, -1.0f, -1.0f - })); - } - else - { - outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, - 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, - 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, - 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f, - - 0.0f, 8.0f, 8.0f, 8.0f, 8.0f, 8.0f, - 0.0f, 9.0f, 7.0f, 9.0f, 9.0f, 3.0f, - 0.0f, 8.0f, 9.0f, 9.0f, 9.0f, 9.0f, - - 0.0f, 0.0f, 0.0f, 0.0f,-3.0f, 0.0f, - 0.0f,-1.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f,-1.0f,-1.0f,-1.0f,-1.0f, 0.0f - })); - } - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -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::Max; - 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 = 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>()) - { - inputTensorInfo.SetQuantizationScale(qScale); - inputTensorInfo.SetQuantizationOffset(qOffset); - outputTensorInfo.SetQuantizationScale(qScale); - outputTensorInfo.SetQuantizationOffset(qOffset); - } - - std::vector<T> inputData( - QuantizedVector<T>(qScale, qOffset, { - 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, - })); - - std::vector<T> outputData( - QuantizedVector<T>(qScale, qOffset, { - 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> SimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - 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_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 = 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>()) - { - 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; - - 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> LargeTensorsAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 100; - descriptor.m_StrideX = descriptor.m_StrideY = 5; - descriptor.m_PadLeft = 50; - descriptor.m_PadRight = 50; - descriptor.m_PadTop = 50; - descriptor.m_PadBottom = 50; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13 }, armnn::GetDataType<T>()); - - // 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> inputVec; - - for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) - { - inputVec.push_back(1); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, inputVec); - - std::vector<T> outputVec; - - for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) - { - outputVec.push_back(1); - } - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, outputVec); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> SimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - armnn::DataLayoutIndexed dataLayout = armnn::DataLayout::NCHW, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - 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 = 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, 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, - })); - - std::vector<T> outputData( - QuantizedVector<T>(qScale, qOffset, { - 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); -} - -template<typename T> -LayerTestResult<T, 4> L2Pooling2dSize3Stride1TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 2.0f, - 1.0f, 2.0f, 2.0f, 1.0f, - 5.0f, 4.0f, 1.0f, 5.0f, - 2.0f, 1.0f, 5.0f, 2.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 3.0f, 3.0f, - 3.0f, 3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> L2Pooling2dSize3Stride3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 3; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 3.0f, 3.0f, 3.0f, - 3.0f, 3.0f, 3.0f, - 3.0f, 3.0f, 3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> L2Pooling2dSize3Stride4TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 4; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 2.0f, 1.0f, 5.0f, 0.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 0.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 0.0f, 5.0f, 4.0f, 1.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 3.0f, 3.0f, - 3.0f, 3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> L2Pooling2dSize7TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 7; - descriptor.m_StrideX = descriptor.m_StrideY = 7; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 7, 7 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 1.0f, 0.0f, 2.0f, 0.0f, 3.0f, 0.0f, 4.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 5.0f, 0.0f, 6.0f, 0.0f, 7.0f, 0.0f, - 8.0f, 0.0f, 9.0f, 0.0f, 10.0f, 0.0f, 5.0f, - 0.0f, 5.0f, 0.0f, 2.0f, 0.0f, 1.0f, 1.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, 0.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> L2Pooling2dSize9TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 9; - descriptor.m_StrideX = descriptor.m_StrideY = 9; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 9, 9 }, armnn::GetDataType<T>()); - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, 2.0f, 1.0f, 5.0f, - 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, 1.0f, 2.0f, 2.0f, - 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, 5.0f, 4.0f, 1.0f, - })); - - armnn::TensorInfo outputTensorInfo({ 1, 1, 1, 1 }, armnn::GetDataType<T>()); - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> AsymmetricNonSquarePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::TensorInfo inputTensorInfo({ 1, 1, 1, 3 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); - - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = 2; - descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 2; - descriptor.m_PadRight = 0; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 2; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - // Construct input data. - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 1.0f, 3.0f, 4.0f, - })); - - // These were calculated manually. - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 0.0f, 3.0f, 0.0f, 3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> ComparePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - armnn::IWorkloadFactory& refWorkloadFactory, - armnn::PoolingAlgorithm poolingType, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - const unsigned int inputWidth = 16; - const unsigned int inputHeight = 32; - const unsigned int channelCount = 2; - const unsigned int batchSize = 5; - - const unsigned int poolSize = 3; - const unsigned int strideX = 2; - const unsigned int strideY = 4; - const unsigned int padX = 0; - const unsigned int padY = 0; - - const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; - const unsigned int outputHeight = (inputHeight + 2 * padY + strideY - poolSize) / strideY; - - armnn::TensorInfo inputTensorInfo; - armnn::TensorInfo outputTensorInfo; - - unsigned int inputShape[] = { batchSize, channelCount, inputHeight, inputWidth }; - unsigned int outputShape[] = { batchSize, channelCount, outputHeight, outputWidth }; - - inputTensorInfo = armnn::TensorInfo(4, inputShape, armnn::GetDataType<T>()); - outputTensorInfo = armnn::TensorInfo(4, outputShape, armnn::GetDataType<T>()); - - // 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); - } - - boost::multi_array<T, 4> input = MakeRandomTensor<T, 4>(inputTensorInfo, 81715); - - LayerTestResult<T, 4> comparisonResult(outputTensorInfo); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::Pooling2dQueueDescriptor data; - armnn::WorkloadInfo info; - AddInputToWorkload(data, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(data, info, outputTensorInfo, outputHandle.get()); - data.m_Parameters.m_PoolType = poolingType; - data.m_Parameters.m_PoolWidth = poolSize; - data.m_Parameters.m_PoolHeight = poolSize; - data.m_Parameters.m_StrideX = strideX; - data.m_Parameters.m_StrideY = strideY; - data.m_Parameters.m_PadLeft = padX; - data.m_Parameters.m_PadRight = padX; - data.m_Parameters.m_PadTop = padY; - data.m_Parameters.m_PadBottom = padY; - data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - - std::unique_ptr<armnn::ITensorHandle> outputHandleRef = refWorkloadFactory.CreateTensorHandle(outputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> inputHandleRef = refWorkloadFactory.CreateTensorHandle(inputTensorInfo); - - // Don't execute if Pooling is not supported, as an exception will be raised. - armnn::BackendId backend = workloadFactory.GetBackendId(); - const size_t reasonIfUnsupportedMaxLen = 255; - char reasonIfUnsupported[reasonIfUnsupportedMaxLen+1]; - comparisonResult.supported = armnn::IsPooling2dSupported(backend, inputTensorInfo, outputTensorInfo, - data.m_Parameters, - reasonIfUnsupported, reasonIfUnsupportedMaxLen); - if (!comparisonResult.supported) - { - return comparisonResult; - } - - armnn::Pooling2dQueueDescriptor refData = data; - armnn::WorkloadInfo refInfo = info; - SetWorkloadInput(refData, refInfo, 0, inputTensorInfo, inputHandleRef.get()); - SetWorkloadOutput(refData, refInfo, 0, outputTensorInfo, outputHandleRef.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreatePooling2d(data, info); - std::unique_ptr<armnn::IWorkload> workloadRef = refWorkloadFactory.CreatePooling2d(refData, refInfo); - - outputHandleRef->Allocate(); - inputHandleRef->Allocate(); - inputHandle->Allocate(); - outputHandle->Allocate(); - - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - CopyDataToITensorHandle(inputHandleRef.get(), &input[0][0][0][0]); - - workload->Execute(); - workloadRef->Execute(); - - CopyDataFromITensorHandle(&comparisonResult.output[0][0][0][0], outputHandle.get()); - CopyDataFromITensorHandle(&comparisonResult.outputExpected[0][0][0][0], outputHandleRef.get()); - - return comparisonResult; -} - -// -// Tests max pooling with the following parameters: -// -// Pooling size: 2x2 -// Stride: (2,2) -// input size: 4x4 -// channels: 1 -// batch size: 1 -// -template<typename T> -LayerTestResult<T, 4> SimpleMaxPooling2dSize2x2Stride2x2TestCommon(armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 2; - descriptor.m_PadLeft = descriptor.m_PadRight = forceNoPadding ? 0 : 3; - descriptor.m_PadTop = descriptor.m_PadBottom = 0; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; - - unsigned int inputWidth = 4; - unsigned int inputHeight = 4; - unsigned int outputWidth = - (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / - descriptor.m_StrideX; - unsigned int outputHeight = - (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / - descriptor.m_StrideY; - unsigned int channels = 1; - unsigned int batchSize = 1; - - std::vector<float> inputData = { - 510.0f, 222.0f, 780.0f, 654.0f, - 141.0f, 276.0f, 15.0f, 546.0f, - 303.0f, 618.0f, 582.0f, 339.0f, - 438.0f, 564.0f, 573.0f, 402.0f - }; - - // Note that left and right edges will be 0.f, due to the 2x2 max pooling only accessing zeros here. - std::vector<float> expectedOutputDataWithPadding = { - 0.0f, 510.0f, 780.0f, 654.0f, 0.0f, - 0.0f, 438.0f, 618.0f, 402.0f, 0.0f - }; - - std::vector<float> expectedOutputDataNoPadding = { - 510.0f, 780.0f, - 618.0f, 582.0f - }; - - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); - - // Scale and offset should match input - we're just calculating maximum values. - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : - QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -// -// Tests max pooling with the following parameters: -// -// Pooling size: 3x2 -// Stride: (2,2) -// input size: 3x2 -// channels: 1 -// batch size: 1 -// -template<typename T> -LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3x2Stride2x2TestCommon( - armnn::IWorkloadFactory& workloadFactory, - bool forceNoPadding, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = 3; - descriptor.m_PoolHeight = 2; - descriptor.m_StrideX = 2; - descriptor.m_StrideY = 2; - descriptor.m_PadLeft = (forceNoPadding) ? 0 : 1; - descriptor.m_PadRight = descriptor.m_PadLeft; - descriptor.m_PadTop = 0; - descriptor.m_PadBottom = 0; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - unsigned int inputWidth = 3; - unsigned int inputHeight = 2; - unsigned int outputWidth = - (inputWidth + descriptor.m_PadLeft + descriptor.m_PadRight + descriptor.m_StrideX - descriptor.m_PoolWidth) / - descriptor.m_StrideX; - unsigned int outputHeight = - (inputHeight + descriptor.m_PadTop + descriptor.m_PadBottom + descriptor.m_StrideY - descriptor.m_PoolHeight) / - descriptor.m_StrideY; - unsigned int channels = 1; - unsigned int batchSize = 1; - - std::vector<float> inputData = { - 3.0f, 6.0f, 9.0f, - 12.0f, 15.0f, 18.0f, - }; - - std::vector<float> expectedOutputDataWithPadding = { - 6.0f, 8.0f, - }; - - std::vector<float> expectedOutputDataNoPadding = { - 10.5f, - }; - - armnn::TensorInfo inputTensorInfo({ batchSize, channels, inputHeight, inputWidth }, armnn::GetDataType<T>()); - - // Scale and offset should match input - we're just calculating average values. - armnn::TensorInfo outputTensorInfo({ batchSize, channels, outputHeight, outputWidth }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, QuantizedVector<T>(qScale, qOffset, inputData)); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - forceNoPadding ? QuantizedVector<T>(qScale, qOffset, expectedOutputDataNoPadding) : - QuantizedVector<T>(qScale, qOffset, expectedOutputDataWithPadding)); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingSimpleMaxPooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - 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::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - 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, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - -1.0f, 3.0f, 4.0f, - 1.0f, 3.0f, 4.0f, - 1.0f, 2.0f, -4.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingMaxPooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - 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, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - -1.0f, 3.0f, 4.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 4.0f, - 2.0f, 2.0f, 2.0f, -3.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - 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_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 12.0f, 20.0f, 32.0f, 40.0f, - 12.0f, 20.0f, 32.0f, 40.0f, - 12.0f, 20.0f, 32.0f, 40.0f, - 12.0f, 20.0f, 32.0f, 40.0f, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 3.0f, 13.0f, 10.0f, - 6.0f, 26.0f, 20.0f, - 3.0f, 13.0f, 10.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingSimpleAveragePooling2dNoPaddingTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 2; - descriptor.m_PadLeft = 0; - descriptor.m_PadRight = 0; - descriptor.m_PadTop = 0; - descriptor.m_PadBottom = 0; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Ceiling; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4}, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 2, 2 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - 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, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 3.5f, - 2.0f, 3.5f - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingAveragePooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 9.0f, 27.0f, 18.0f, 36.0f, - 18.0f, 9.0f, 18.0f, 9.0f, - 27.0f, 18.0f, 9.0f, 27.0f, - 9.0f, 27.0f, 9.0f, 18.0f, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 7.0f, 11.0f, 13.0f, 9.0f, - 12.0f, 17.0f, 19.0f, 13.0f, - 12.0f, 16.0f, 16.0f, 10.0f, - 9.0f, 11.0f, 12.0f, 7.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingSimpleL2Pooling2dTestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - 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::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 2.0f, 4.0f, 8.0f, 16.0f, - 4.0f, 2.0f, 2.0f, 4.0f, - 8.0f, 2.0f, 4.0f, 2.0f, - 16.0f, 2.0f, 2.0f, 8.0f, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 1.0f, 4.4721f, 8.0f, - 4.4721f, 2.6457f, 2.236f, - 8.0f, 1.4142f, 4.0f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} - -template<typename T> -LayerTestResult<T, 4> IgnorePaddingL2Pooling2dSize3TestCommon(armnn::IWorkloadFactory& workloadFactory, - float qScale = 1.0f, - int32_t qOffset = 0) -{ - armnn::Pooling2dDescriptor descriptor; - descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; - descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; - descriptor.m_StrideX = descriptor.m_StrideY = 1; - descriptor.m_PadLeft = 1; - descriptor.m_PadRight = 1; - descriptor.m_PadTop = 1; - descriptor.m_PadBottom = 1; - descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; - - armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - armnn::TensorInfo outputTensorInfo({ 1, 1, 4, 4 }, armnn::GetDataType<T>()); - - // 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); - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - 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, - })); - - auto outputExpected = MakeTensor<T, 4>(outputTensorInfo, - QuantizedVector<T>(qScale, qOffset, { - 1.0540f, 1.7638f, 2.5385f, 2.3570f, - 1.2909f, 2.1602f, 3.1091f, 2.8867f, - 1.2909f, 2.1602f, 3.1091f, 2.8867f, - 1.0540f, 1.7638f, 2.5385f, 2.3570f, - })); - - return SimplePooling2dTestImpl<T>(workloadFactory, descriptor, qScale, qOffset, input, outputExpected); -} |