| // |
| // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| |
| #include "Pooling3dTestImpl.hpp" |
| |
| #include <armnnUtils/QuantizeHelper.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <armnnUtils/TensorUtils.hpp> |
| #include <armnnUtils/DataLayoutIndexed.hpp> |
| #include <armnnUtils/Permute.hpp> |
| |
| #include <armnn/utility/IgnoreUnused.hpp> |
| #include <armnn/utility/NumericCast.hpp> |
| |
| #include <armnn/BackendHelper.hpp> |
| #include <backendsCommon/WorkloadInfo.hpp> |
| |
| #include <armnnTestUtils/TensorCopyUtils.hpp> |
| #include <armnnTestUtils/WorkloadTestUtils.hpp> |
| |
| #include <armnnTestUtils/TensorHelpers.hpp> |
| |
| namespace |
| { |
| |
| using namespace armnnUtils; |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 5> SimplePooling3dTestImpl( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| armnn::Pooling3dDescriptor descriptor, |
| float qScale, |
| int32_t qOffset, |
| const std::vector<T>& input, |
| const std::vector<T>& outputExpected, |
| const armnn::TensorShape& inputShape, |
| const armnn::TensorShape& outputShape) |
| { |
| IgnoreUnused(memoryManager); |
| const armnn::DataLayout dataLayout = descriptor.m_DataLayout; |
| const armnnUtils::DataLayoutIndexed dimensionIndices = dataLayout; |
| auto heightIndex = dimensionIndices.GetHeightIndex(); |
| auto widthIndex = dimensionIndices.GetWidthIndex(); |
| auto depthIndex = dimensionIndices.GetDepthIndex(); |
| auto channelsIndex = dimensionIndices.GetChannelsIndex(); |
| |
| unsigned int inputDepth = armnn::numeric_cast<unsigned int>(inputShape[depthIndex]); |
| unsigned int inputHeight = armnn::numeric_cast<unsigned int>(inputShape[heightIndex]); |
| unsigned int inputWidth = armnn::numeric_cast<unsigned int>(inputShape[widthIndex]); |
| unsigned int inputChannels = armnn::numeric_cast<unsigned int>(inputShape[channelsIndex]); |
| unsigned int inputBatchSize = armnn::numeric_cast<unsigned int>(inputShape[0]); |
| |
| unsigned int outputDepth = armnn::numeric_cast<unsigned int>(outputShape[depthIndex]); |
| unsigned int outputHeight = armnn::numeric_cast<unsigned int>(outputShape[heightIndex]); |
| unsigned int outputWidth = armnn::numeric_cast<unsigned int>(outputShape[widthIndex]); |
| unsigned int outputChannels = armnn::numeric_cast<unsigned int>(outputShape[channelsIndex]); |
| unsigned int outputBatchSize = armnn::numeric_cast<unsigned int>(outputShape[0]); |
| |
| armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo( |
| inputBatchSize, inputChannels, inputDepth, inputHeight, inputWidth, dataLayout, ArmnnType); |
| |
| armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo( |
| outputBatchSize, outputChannels, outputDepth, outputHeight, outputWidth, dataLayout, ArmnnType); |
| |
| // 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, 5> result(outputTensorInfo); |
| std::vector<T> actualOutput(outputTensorInfo.GetNumElements()); |
| |
| std::unique_ptr<armnn::ITensorHandle> inputHandle = tensorHandleFactory.CreateTensorHandle(inputTensorInfo); |
| std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| |
| armnn::Pooling3dQueueDescriptor 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(); |
| std::string reasonIfUnsupported; |
| armnn::LayerSupportHandle handle = armnn::GetILayerSupportByBackendId(backend); |
| result.m_Supported = handle.IsPooling3dSupported(inputTensorInfo, |
| outputTensorInfo, |
| queueDescriptor.m_Parameters, |
| reasonIfUnsupported); |
| if (!result.m_Supported) |
| { |
| return result; |
| } |
| |
| std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateWorkload(armnn::LayerType::Pooling3d, |
| queueDescriptor, |
| workloadInfo); |
| |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| |
| workload->Execute(); |
| |
| CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| |
| result.m_ActualData = actualOutput; |
| result.m_ExpectedData = outputExpected; |
| |
| return result; |
| } |
| |
| // |
| // Tests max pooling with the following parameters: |
| // |
| // Pooling size: 2x2x2 |
| // Stride: (1,1,1) |
| // input size: 3x3x3 |
| // channels: 2 |
| // batch size: 2 |
| // |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| descriptor.m_PoolWidth = 2; |
| descriptor.m_PoolHeight = 2; |
| descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = 1; |
| descriptor.m_StrideY = 1; |
| descriptor.m_StrideZ = 1; |
| descriptor.m_PadLeft = descriptor.m_PadRight = 0; |
| descriptor.m_PadTop = descriptor.m_PadBottom = 0; |
| descriptor.m_PadFront = descriptor.m_PadBack = 0; |
| descriptor.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| |
| unsigned int inputWidth = 3; |
| unsigned int inputHeight = 3; |
| unsigned int inputDepth = 3; |
| 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 outputDepth = |
| (inputDepth + descriptor.m_PadFront + descriptor.m_PadBack + descriptor.m_StrideZ - descriptor.m_PoolDepth) / |
| descriptor.m_StrideZ; |
| 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); |
| |
| // 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({ |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| 1.0f, 1.0f, 1.0f, |
| }); |
| |
| // 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 = QuantizedVector<T>(inputData, qScale, qOffset); |
| |
| // These were calculated manually. |
| std::vector<T> outputExpected = QuantizedVector<T>( |
| { |
| 1.0f, 1.0f, |
| 1.0f, 1.0f, |
| |
| 1.0f, 1.0f, |
| 1.0f, 1.0f, |
| |
| -1.0f, -1.0f, |
| -1.0f, -1.0f, |
| |
| -1.0f, -1.0f, |
| -1.0f, -1.0f, |
| |
| |
| 1.0f, 1.0f, |
| 1.0f, 1.0f, |
| |
| 1.0f, 1.0f, |
| 1.0f, 1.0f, |
| |
| -1.0f, -1.0f, |
| -1.0f, -1.0f, |
| |
| -1.0f, -1.0f, |
| -1.0f, -1.0f, |
| }, |
| qScale, qOffset); |
| |
| 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> SimpleMaxPooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout = armnn::DataLayout::NCDHW, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 2; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| descriptor.m_DataLayout = dataLayout; |
| |
| armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo(1, 1, 4, 4, 4, dataLayout, ArmnnType); |
| armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(1, 1, 2, 2, 2, dataLayout, ArmnnType); |
| |
| // 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>({ |
| 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, |
| |
| 33.0f, 34.0f, 37.0f, 38.0f, |
| 35.0f, 36.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 45.0f, 46.0f, |
| 43.0f, 44.0f, 47.0f, 48.0f, |
| |
| 49.0f, 50.0f, 53.0f, 54.0f, |
| 51.0f, 52.0f, 55.0f, 56.0f, |
| 57.0f, 58.0f, 61.0f, 62.0f, |
| 59.0f, 60.0f, 63.0f, 64.0f, |
| }, |
| qScale, qOffset)); |
| |
| std::vector<T> outputData( |
| QuantizedVector<T>({ |
| 20.0f, 24.0f, |
| 28.0f, 32.0f, |
| |
| 52.0f, 56.0f, |
| 60.0f, 64.0f, |
| }, |
| 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; |
| } |
| |
| return SimplePooling3dTestImpl<ArmnnType>( |
| workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, |
| inputData, outputData, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 5> IgnorePaddingSimpleMaxPooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::Max; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 2; |
| descriptor.m_PadLeft = 1; |
| descriptor.m_PadRight = 1; |
| descriptor.m_PadTop = 1; |
| descriptor.m_PadBottom = 1; |
| descriptor.m_PadFront = 1; |
| descriptor.m_PadBack = 1; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| |
| armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType); |
| armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType); |
| |
| // 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 = QuantizedVector<T>( |
| { |
| -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, 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, 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, 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, |
| }, |
| qScale, qOffset); |
| |
| auto outputExpected = QuantizedVector<T>( |
| { |
| -1.0f, 3.0f, 4.0f, |
| 1.0f, 3.0f, 4.0f, |
| 1.0f, 2.0f, -4.0f, |
| |
| -1.0f, 3.0f, 4.0f, |
| 1.0f, 3.0f, 4.0f, |
| 1.0f, 2.0f, -4.0f, |
| |
| -1.0f, 3.0f, 4.0f, |
| 1.0f, 3.0f, 4.0f, |
| 1.0f, 2.0f, -4.0f, |
| }, |
| qScale, qOffset); |
| |
| 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> SimpleAveragePooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| armnn::DataLayout dataLayout = armnn::DataLayout::NCDHW, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 2; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| descriptor.m_DataLayout = dataLayout; |
| |
| armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo(1, 1, 4, 4, 4, dataLayout, ArmnnType); |
| armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(1, 1, 2, 2, 2, dataLayout, ArmnnType); |
| |
| // 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>({ |
| 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, |
| |
| 33.0f, 34.0f, 37.0f, 38.0f, |
| 35.0f, 36.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 45.0f, 46.0f, |
| 43.0f, 44.0f, 47.0f, 48.0f, |
| |
| 49.0f, 50.0f, 53.0f, 54.0f, |
| 51.0f, 52.0f, 55.0f, 56.0f, |
| 57.0f, 58.0f, 61.0f, 62.0f, |
| 59.0f, 60.0f, 63.0f, 64.0f, |
| }, |
| qScale, qOffset)); |
| |
| std::vector<T> outputData( |
| QuantizedVector<T>({ |
| 10.5f, 14.5f, |
| 18.5f, 22.5f, |
| |
| 42.5f, 46.5f, |
| 50.5f, 54.5f, |
| }, |
| 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; |
| } |
| |
| return SimplePooling3dTestImpl<ArmnnType>( |
| workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, |
| inputData, outputData, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 5> LargeTensorsAveragePooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 100; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 5; |
| descriptor.m_PadLeft = 50; |
| descriptor.m_PadRight = 50; |
| descriptor.m_PadTop = 50; |
| descriptor.m_PadBottom = 50; |
| descriptor.m_PadFront = 50; |
| descriptor.m_PadBack = 50; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| |
| armnn::TensorInfo inputTensorInfo({ 5, 3, 52, 60, 68 }, ArmnnType); |
| armnn::TensorInfo outputTensorInfo({ 5, 3, 11, 13, 15 }, ArmnnType); |
| |
| // 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> input; |
| |
| for (unsigned int i = 0 ; i < inputTensorInfo.GetShape().GetNumElements(); ++i) |
| { |
| input.push_back(1); |
| } |
| |
| std::vector<T> outputExpected; |
| |
| for (unsigned int i = 0 ; i < outputTensorInfo.GetShape().GetNumElements(); ++i) |
| { |
| outputExpected.push_back(1); |
| } |
| |
| 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> IgnorePaddingSimpleAveragePooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::Average; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 2; |
| descriptor.m_PadLeft = 1; |
| descriptor.m_PadRight = 1; |
| descriptor.m_PadTop = 1; |
| descriptor.m_PadBottom = 1; |
| descriptor.m_PadFront = 1; |
| descriptor.m_PadBack = 1; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| |
| armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType); |
| armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType); |
| |
| // 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 = QuantizedVector<T>( |
| { |
| 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, |
| |
| 24.0f, 40.0f, 64.0f, 80.0f, |
| 24.0f, 40.0f, 64.0f, 80.0f, |
| 24.0f, 40.0f, 64.0f, 80.0f, |
| 24.0f, 40.0f, 64.0f, 80.0f, |
| |
| 36.0f, 60.0f, 96.0f, 120.0f, |
| 36.0f, 60.0f, 96.0f, 120.0f, |
| 36.0f, 60.0f, 96.0f, 120.0f, |
| 36.0f, 60.0f, 96.0f, 120.0f, |
| |
| 48.0f, 80.0f, 128.0f, 160.0f, |
| 48.0f, 80.0f, 128.0f, 160.0f, |
| 48.0f, 80.0f, 128.0f, 160.0f, |
| 48.0f, 80.0f, 128.0f, 160.0f, |
| }, |
| qScale, qOffset); |
| |
| auto outputExpected = QuantizedVector<T>( |
| { |
| 1.5f, 6.5f, 5.0f, |
| 3.0f, 13.0f, 10.0f, |
| 1.5f, 6.5f, 5.0f, |
| |
| 7.5f, 32.5f, 25.0f, |
| 15.0f, 65.0f, 50.0f, |
| 7.5f, 32.5f, 25.0f, |
| |
| 6.0f, 26.0f, 20.0f, |
| 12.0f, 52.0f, 40.0f, |
| 6.0f, 26.0f, 20.0f, |
| }, |
| qScale, qOffset); |
| |
| 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> SimpleL2Pooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| armnn::DataLayout dataLayout = armnn::DataLayout::NCDHW, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 2; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::Exclude; |
| descriptor.m_DataLayout = dataLayout; |
| |
| armnn::TensorInfo inputTensorInfo = armnnUtils::GetTensorInfo(1, 1, 4, 4, 4, dataLayout, ArmnnType); |
| armnn::TensorInfo outputTensorInfo = armnnUtils::GetTensorInfo(1, 1, 2, 2, 2, dataLayout, ArmnnType); |
| |
| // 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>({ |
| 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, |
| |
| 33.0f, 34.0f, 37.0f, 38.0f, |
| 35.0f, 36.0f, 39.0f, 40.0f, |
| 41.0f, 42.0f, 45.0f, 46.0f, |
| 43.0f, 44.0f, 47.0f, 48.0f, |
| |
| 49.0f, 50.0f, 53.0f, 54.0f, |
| 51.0f, 52.0f, 55.0f, 56.0f, |
| 57.0f, 58.0f, 61.0f, 62.0f, |
| 59.0f, 60.0f, 63.0f, 64.0f, |
| }, |
| qScale, qOffset)); |
| |
| std::vector<T> outputData( |
| QuantizedVector<T>({ |
| 13.2476412995f, 16.5981926727f, |
| 20.1866292382f, 23.9060661758f, |
| |
| 43.2608367926f, 47.1963981677f, |
| 51.1419592898f, 55.0953718564f, |
| }, |
| 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; |
| } |
| |
| return SimplePooling3dTestImpl<ArmnnType>( |
| workloadFactory, memoryManager, tensorHandleFactory, descriptor, qScale, qOffset, |
| inputData, outputData, inputTensorInfo.GetShape(), outputTensorInfo.GetShape()); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| LayerTestResult<T, 5> IgnorePaddingSimpleL2Pooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| armnn::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = descriptor.m_PoolDepth = 2; |
| descriptor.m_StrideX = descriptor.m_StrideY = descriptor.m_StrideZ = 2; |
| descriptor.m_PadLeft = 1; |
| descriptor.m_PadRight = 1; |
| descriptor.m_PadTop = 1; |
| descriptor.m_PadBottom = 1; |
| descriptor.m_PadFront = 1; |
| descriptor.m_PadBack = 1; |
| descriptor.m_PaddingMethod = armnn::PaddingMethod::IgnoreValue; |
| |
| armnn::TensorInfo inputTensorInfo({ 1, 1, 4, 4, 4 }, ArmnnType); |
| armnn::TensorInfo outputTensorInfo({ 1, 1, 3, 3, 3 }, ArmnnType); |
| |
| // 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 = QuantizedVector<T>( |
| { |
| 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, |
| |
| 2.0f, 3.0f, 4.0f, 5.0f, |
| 2.0f, 3.0f, 4.0f, 5.0f, |
| 2.0f, 3.0f, 4.0f, 5.0f, |
| 2.0f, 3.0f, 4.0f, 5.0f, |
| |
| 3.0f, 4.0f, 5.0f, 6.0f, |
| 3.0f, 4.0f, 5.0f, 6.0f, |
| 3.0f, 4.0f, 5.0f, 6.0f, |
| 3.0f, 4.0f, 5.0f, 6.0f, |
| |
| 4.0f, 5.0f, 6.0f, 7.0f, |
| 4.0f, 5.0f, 6.0f, 7.0f, |
| 4.0f, 5.0f, 6.0f, 7.0f, |
| 4.0f, 5.0f, 6.0f, 7.0f, |
| }, |
| qScale, qOffset); |
| |
| float v111 = float(sqrt(pow(1,2)/8.0f)); |
| float v112 = float(sqrt((pow(2,2)+pow(3,2))/8.0f)); |
| float v113 = float(sqrt(pow(4,2)/8)); |
| |
| float v121 = float(sqrt((2*pow(1,2))/8.0f)); |
| float v122 = float(sqrt((2*pow(2,2)+2*pow(3,2))/8.0f)); |
| float v123 = float(sqrt((2*pow(4,2))/8.0f)); |
| |
| float v131 = v111; |
| float v132 = v112; |
| float v133 = v113; |
| |
| float v211 = float(sqrt((pow(2,2)+pow(3,2))/8.0f)); |
| float v212 = float(sqrt((pow(3,2)+2*pow(4,2)+pow(5,2))/8.0f)); |
| float v213 = float(sqrt((pow(5,2)+pow(6,2))/8.0f)); |
| |
| float v221 = float(sqrt((2*pow(2,2)+2*pow(3,2))/8.0f)); |
| float v222 = float(sqrt((2*pow(3,2)+4*pow(4,2)+2*pow(5,2))/8.0f)); |
| float v223 = float(sqrt((2*pow(5,2)+2*pow(6,2))/8.0f)); |
| |
| float v231 = v211; |
| float v232 = v212; |
| float v233 = v213; |
| |
| float v311 = float(sqrt(pow(4,2)/8.0f)); |
| float v312 = float(sqrt((pow(5,2)+pow(6,2))/8.0f)); |
| float v313 = float(sqrt(pow(7,2)/8)); |
| |
| float v321 = float(sqrt((2*pow(4,2))/8.0f)); |
| float v322 = float(sqrt((2*pow(5,2)+2*pow(6,2))/8.0f)); |
| float v323 = float(sqrt((2*pow(7,2))/8.0f)); |
| |
| float v331 = v311; |
| float v332 = v312; |
| float v333 = v313; |
| |
| auto outputExpected = QuantizedVector<T>( |
| { |
| v111, v112, v113, |
| v121, v122, v123, |
| v131, v132, v133, |
| |
| v211, v212, v213, |
| v221, v222, v223, |
| v231, v232, v233, |
| |
| v311, v312, v313, |
| v321, v322, v323, |
| v331, v332, v333, |
| }, |
| qScale, qOffset); |
| |
| 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, |
| 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::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_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; |
| |
| // 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); |
| |
| 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> AsymmetricNonSquareAveragePooling3dTestCommon( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 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::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_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; |
| |
| // 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, 2.0f, 0.0f, 2.0f, |
| }, |
| qScale, qOffset); |
| |
| 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( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| 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::Pooling3dDescriptor descriptor; |
| descriptor.m_PoolType = armnn::PoolingAlgorithm::L2; |
| descriptor.m_PoolWidth = 1; |
| descriptor.m_PoolHeight = 2; |
| descriptor.m_PoolDepth = 3; |
| descriptor.m_StrideX = 0; |
| 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; |
| |
| // 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, 2.2360679775f, 0.0f, 2.2360679775f, |
| }, |
| qScale, qOffset); |
| |
| 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, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::ITensorHandleFactory& refTensorHandleFactory, |
| armnn::PoolingAlgorithm poolingType, |
| float qScale = 1.0f, |
| int32_t qOffset = 0) |
| { |
| IgnoreUnused(memoryManager); |
| const unsigned int inputWidth = 16; |
| const unsigned int inputHeight = 32; |
| const unsigned int inputDepth = 48; |
| 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 strideZ = 6; |
| const unsigned int padX = 0; |
| const unsigned int padY = 0; |
| const unsigned int padZ = 0; |
| |
| const unsigned int outputWidth = (inputWidth + 2 * padX + strideX - poolSize) / strideX; |
| 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); |
| |
| // 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> 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); |
| std::unique_ptr<armnn::ITensorHandle> outputHandle = tensorHandleFactory.CreateTensorHandle(outputTensorInfo); |
| |
| armnn::Pooling3dQueueDescriptor 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_PoolDepth = poolSize; |
| data.m_Parameters.m_StrideX = strideX; |
| data.m_Parameters.m_StrideY = strideY; |
| data.m_Parameters.m_StrideZ = strideZ; |
| 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_PadFront = padZ; |
| data.m_Parameters.m_PadBack = padZ; |
| data.m_Parameters.m_OutputShapeRounding = armnn::OutputShapeRounding::Floor; |
| |
| 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. |
| armnn::BackendId backend = workloadFactory.GetBackendId(); |
| std::string reasonIfUnsupported; |
| armnn::LayerSupportHandle handle = armnn::GetILayerSupportByBackendId(backend); |
| comparisonResult.m_Supported = handle.IsPooling3dSupported(inputTensorInfo, |
| outputTensorInfo, |
| data.m_Parameters, |
| reasonIfUnsupported); |
| if (!comparisonResult.m_Supported) |
| { |
| return comparisonResult; |
| } |
| |
| armnn::Pooling3dQueueDescriptor 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.CreateWorkload(armnn::LayerType::Pooling3d, data, info); |
| std::unique_ptr<armnn::IWorkload> workloadRef |
| = refWorkloadFactory.CreateWorkload(armnn::LayerType::Pooling3d, refData, refInfo); |
| |
| outputHandleRef->Allocate(); |
| inputHandleRef->Allocate(); |
| inputHandle->Allocate(); |
| outputHandle->Allocate(); |
| |
| CopyDataToITensorHandle(inputHandle.get(), input.data()); |
| CopyDataToITensorHandle(inputHandleRef.get(), input.data()); |
| |
| workload->Execute(); |
| workloadRef->Execute(); |
| |
| CopyDataFromITensorHandle(actualOutput.data(), outputHandle.get()); |
| CopyDataFromITensorHandle(expectedOutput.data(), outputHandleRef.get()); |
| |
| comparisonResult.m_ActualData = actualOutput; |
| comparisonResult.m_ExpectedData = expectedOutput; |
| |
| return comparisonResult; |
| } |
| |
| |
| } // anonymous namespace |
| |
| LayerTestResult<float, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1Uint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, 0.1f, 128); |
| } |
| |
| LayerTestResult<int16_t, 5> SimpleMaxPooling3dSize2x2x2Stride1x1x1Int16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return SimpleMaxPooling3dSize2x2x2Stride1x1x1TestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> SimpleMaxPooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleMaxPooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<uint8_t, 5> SimpleMaxPooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<int16_t, 5> SimpleMaxPooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleMaxPooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<float, 5> IgnorePaddingSimpleMaxPooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> IgnorePaddingSimpleMaxPooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5); |
| } |
| |
| LayerTestResult<int16_t, 5> IgnorePaddingSimpleMaxPooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleMaxPooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> SimpleAveragePooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleAveragePooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<uint8_t, 5> SimpleAveragePooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<int16_t, 5> SimpleAveragePooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<float, 5> SimpleL2Pooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleL2Pooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<uint8_t, 5> SimpleL2Pooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<int16_t, 5> SimpleL2Pooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::DataLayout dataLayout) |
| { |
| return SimpleL2Pooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory, dataLayout); |
| } |
| |
| LayerTestResult<float, 5> LargeTensorsAveragePooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return LargeTensorsAveragePooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> LargeTensorsAveragePooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return LargeTensorsAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, 0.5, -1); |
| } |
| |
| LayerTestResult<int16_t, 5> LargeTensorsAveragePooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return LargeTensorsAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> IgnorePaddingSimpleAveragePooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> IgnorePaddingSimpleAveragePooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5); |
| } |
| |
| LayerTestResult<int16_t, 5> IgnorePaddingSimpleAveragePooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> IgnorePaddingSimpleL2Pooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> IgnorePaddingSimpleL2Pooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory, 1.0f, -5); |
| } |
| |
| LayerTestResult<int16_t, 5> IgnorePaddingSimpleL2Pooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return IgnorePaddingSimpleL2Pooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> AsymmetricNonSquareMaxPooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> AsymmetricNonSquareMaxPooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<int16_t, 5> AsymmetricNonSquareMaxPooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareMaxPooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> AsymmetricNonSquareAveragePooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> AsymmetricNonSquareAveragePooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<int16_t, 5> AsymmetricNonSquareAveragePooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareAveragePooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> AsymmetricNonSquareL2Pooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<uint8_t, 5> AsymmetricNonSquareL2Pooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<int16_t, 5> AsymmetricNonSquareL2Pooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| const armnn::ITensorHandleFactory& tensorHandleFactory) |
| { |
| return AsymmetricNonSquareL2Pooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, tensorHandleFactory); |
| } |
| |
| LayerTestResult<float, 5> ComparePooling3dTest( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::ITensorHandleFactory& refTensorHandleFactory, |
| armnn::PoolingAlgorithm poolingType) |
| { |
| return ComparePooling3dTestCommon<armnn::DataType::Float32>( |
| workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, poolingType); |
| } |
| |
| LayerTestResult<uint8_t, 5> ComparePooling3dUint8Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::ITensorHandleFactory& refTensorHandleFactory, |
| armnn::PoolingAlgorithm poolingType) |
| { |
| return ComparePooling3dTestCommon<armnn::DataType::QAsymmU8>( |
| workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, |
| poolingType, 0.1f, 128); |
| } |
| |
| LayerTestResult<int16_t, 5> ComparePooling3dInt16Test( |
| armnn::IWorkloadFactory& workloadFactory, |
| const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, |
| armnn::IWorkloadFactory& refWorkloadFactory, |
| const armnn::ITensorHandleFactory& tensorHandleFactory, |
| const armnn::ITensorHandleFactory& refTensorHandleFactory, |
| armnn::PoolingAlgorithm poolingType) |
| { |
| return ComparePooling3dTestCommon<armnn::DataType::QSymmS16>( |
| workloadFactory, memoryManager, refWorkloadFactory, tensorHandleFactory, refTensorHandleFactory, poolingType); |
| } |