diff options
author | Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> | 2019-10-28 16:06:50 +0000 |
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committer | Aron Virginas-Tar <Aron.Virginas-Tar@arm.com> | 2019-10-29 09:52:55 +0000 |
commit | b3670b106bfef93aa2ab800785ff1e982e5bbdb3 (patch) | |
tree | 18438d14e5885d419bf48f62abfd17d6f13ff366 /src/backends/backendsCommon/test/layerTests | |
parent | 102cdbd76edcd033a2f1c35f4d6853f881bcc1b4 (diff) | |
download | armnn-b3670b106bfef93aa2ab800785ff1e982e5bbdb3.tar.gz |
Refactor Resize layer tests
* Move implementations to newly created source file
ResizeTestImpl.cpp, leave only the declarations in
the header
* Reduce code duplication by extracting common code
into a generic implementation template
Signed-off-by: Aron Virginas-Tar <Aron.Virginas-Tar@arm.com>
Change-Id: Ib8aa395e5fb4470b26cbe943c177f6b64c860a93
Diffstat (limited to 'src/backends/backendsCommon/test/layerTests')
-rw-r--r-- | src/backends/backendsCommon/test/layerTests/ResizeTestImpl.cpp | 792 | ||||
-rw-r--r-- | src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp | 1008 |
2 files changed, 825 insertions, 975 deletions
diff --git a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.cpp b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.cpp new file mode 100644 index 0000000000..895afe06af --- /dev/null +++ b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.cpp @@ -0,0 +1,792 @@ +// +// Copyright © 2019 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// + +#include "ResizeTestImpl.hpp" + +#include <DataLayoutIndexed.hpp> +#include <Permute.hpp> +#include <QuantizeHelper.hpp> +#include <TensorUtils.hpp> + +#include <armnn/ArmNN.hpp> + +#include <backendsCommon/test/DataLayoutUtils.hpp> +#include <backendsCommon/test/TensorCopyUtils.hpp> +#include <backendsCommon/test/WorkloadTestUtils.hpp> + +#include <test/TensorHelpers.hpp> + +namespace +{ + +struct ResizeTestParams +{ + ResizeTestParams() + : m_ResizeMethod(armnn::ResizeMethod::Bilinear) + , m_DataLayout(armnn::DataLayout::NCHW) + , m_InQuantScale(1.0f) + , m_InQuantOffset(0) + , m_OutQuantScale(1.0f) + , m_OutQuantOffset(0) {} + + armnn::ResizeMethod m_ResizeMethod; + armnn::DataLayout m_DataLayout; + + armnn::TensorShape m_InputShape; + armnn::TensorShape m_OutputShape; + + std::vector<float> m_InputData; + std::vector<float> m_ExpectedOutputData; + + float m_InQuantScale; + int32_t m_InQuantOffset; + + float m_OutQuantScale; + int32_t m_OutQuantOffset; + + void SetInQuantParams(float quantScale, int32_t quantOffset) + { + m_InQuantScale = quantScale; + m_InQuantOffset = quantOffset; + } + + void SetOutQuantParams(float quantScale, int32_t quantOffset) + { + m_OutQuantScale = quantScale; + m_OutQuantOffset = quantOffset; + } + + void SetInOutQuantParams(float quantScale, int32_t quantOffset) + { + SetInQuantParams(quantScale, quantOffset); + SetOutQuantParams(quantScale, quantOffset); + } +}; + +template<size_t NumDims, + armnn::DataType ArmnnType, + typename T = armnn::ResolveType<ArmnnType>> +LayerTestResult<T, NumDims> ResizeTestImpl( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const ResizeTestParams& params) +{ + armnn::TensorInfo inputInfo(params.m_InputShape, ArmnnType); + armnn::TensorInfo outputInfo(params.m_OutputShape, ArmnnType); + + if (armnn::IsQuantizedType<T>()) + { + inputInfo.SetQuantizationScale(params.m_InQuantScale); + inputInfo.SetQuantizationOffset(params.m_InQuantOffset); + + outputInfo.SetQuantizationScale(params.m_OutQuantScale); + outputInfo.SetQuantizationOffset(params.m_OutQuantOffset); + } + + std::vector<T> inputData = + armnnUtils::QuantizedVector<T>(params.m_InputData, params.m_InQuantScale, params.m_InQuantOffset); + + std::vector<T> expectedOutputData = + armnnUtils::QuantizedVector<T>(params.m_ExpectedOutputData, + params.m_OutQuantScale, + params.m_OutQuantOffset); + + if (params.m_DataLayout == armnn::DataLayout::NHWC) + { + PermuteTensorNchwToNhwc(inputInfo, inputData); + PermuteTensorNchwToNhwc(outputInfo, expectedOutputData); + } + + auto input = MakeTensor<T, NumDims>(inputInfo, inputData); + + LayerTestResult<T, NumDims> result(outputInfo); + result.outputExpected = MakeTensor<T, NumDims>(outputInfo, expectedOutputData); + + std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputInfo); + std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputInfo); + + armnn::ResizeQueueDescriptor descriptor; + descriptor.m_Parameters.m_Method = params.m_ResizeMethod; + descriptor.m_Parameters.m_DataLayout = params.m_DataLayout; + + armnnUtils::DataLayoutIndexed dataLayoutIndexed(params.m_DataLayout); + descriptor.m_Parameters.m_TargetWidth = params.m_OutputShape[dataLayoutIndexed.GetWidthIndex()]; + descriptor.m_Parameters.m_TargetHeight = params.m_OutputShape[dataLayoutIndexed.GetHeightIndex()]; + + armnn::WorkloadInfo info; + AddInputToWorkload(descriptor, info, inputInfo, inputHandle.get()); + AddOutputToWorkload(descriptor, info, outputInfo, outputHandle.get()); + + std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); + + inputHandle->Allocate(); + outputHandle->Allocate(); + CopyDataToITensorHandle(inputHandle.get(), input.origin()); + + workload->PostAllocationConfigure(); + workload->Execute(); + + CopyDataFromITensorHandle(result.output.origin(), outputHandle.get()); + return result; +} + +} // anonymous namespace + +// +// Bilinear +// + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeBilinearNopTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::Bilinear; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 4, 4 }; + testParams.m_OutputShape = testParams.m_InputShape; + + testParams.m_InputData = + { + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f, + + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f + }; + + testParams.m_ExpectedOutputData = testParams.m_InputData; + + testParams.SetInOutQuantParams(1.5f, 3); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> SimpleResizeBilinearTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::Bilinear; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 2, 2 }; + testParams.m_OutputShape = { 1, 2, 1, 1 }; + + testParams.m_InputData = + { + 1.0f, 255.0f, + 200.0f, 250.0f, + + 250.0f, 200.0f, + 250.0f, 1.0f + }; + + // The 'resize' operation projects the top-left corner of output texels into the input image, + // then figures out the interpolants and weights. Note this is different to projecting the centre of the + // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as + // its single element, the value that was at position (0,0) of the input matrix (rather than an average, + // which we would expect if projecting the centre). + testParams.m_ExpectedOutputData = + { + 1.0f, + + 250.0f + }; + + testParams.SetInOutQuantParams(0.1567f, 1); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeBilinearSqMinTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::Bilinear; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 4, 4 }; + testParams.m_OutputShape = { 1, 2, 2, 2 }; + + testParams.m_InputData = + { + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f, + + 7.0f, 6.0f, 5.0f, 4.0f, + 6.0f, 5.0f, 4.0f, 3.0f, + 5.0f, 4.0f, 3.0f, 2.0f, + 4.0f, 3.0f, 2.0f, 1.0f + }; + + testParams.m_ExpectedOutputData = + { + 1.0f, 3.0f, + 3.0f, 5.0f, + + 7.0f, 5.0f, + 5.0f, 3.0f + }; + + testParams.SetInOutQuantParams(3.141592f, 3); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeBilinearMinTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::Bilinear; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 3, 5 }; + testParams.m_OutputShape = { 1, 2, 2, 3 }; + + testParams.m_InputData = + { + 1.5f, 3.0f, 4.5f, 6.0f, 7.5f, + 9.0f, 10.5f, 12.0f, 13.5f, 15.0f, + 16.5f, 18.0f, 19.5f, 21.0f, 22.5f, + + 16.5f, 18.0f, 19.5f, 21.0f, 22.5f, + 9.0f, 10.5f, 12.0f, 13.5f, 15.0f, + 1.5f, 3.0f, 4.5f, 6.0f, 7.5f + }; + + testParams.m_ExpectedOutputData = + { + 1.50f, 4.00f, 6.50f, + 12.75f, 15.25f, 17.75f, + + 16.50f, 19.00f, 21.50f, + 5.25f, 7.75f, 10.25f + }; + + testParams.SetInOutQuantParams(1.5f, -1); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeBilinearMagTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::Bilinear; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 3, 2 }; + testParams.m_OutputShape = { 1, 2, 3, 5 }; + + testParams.m_InputData = + { + 1.0f, 2.0f, + 13.0f, 21.0f, + 144.0f, 233.0f, + + 233.0f, 144.0f, + 21.0f, 13.0f, + 2.0f, 1.0f + }; + + testParams.m_ExpectedOutputData = + { + 1.0f, 1.4f, 1.8f, 2.0f, 2.0f, + 13.0f, 16.2f, 19.4f, 21.0f, 21.0f, + 144.0f, 179.6f, 215.2f, 233.0f, 233.0f, + + 233.0f, 197.4f, 161.8f, 144.0f, 144.0f, + 21.0f, 17.8f, 14.6f, 13.0f, 13.0f, + 2.0f, 1.6f, 1.2f, 1.0f, 1.0f + }; + + testParams.SetInQuantParams(1.0f, 0); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +// +// NearestNeighbor +// + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeNearestNeighborNopTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::NearestNeighbor; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 4, 4 }; + testParams.m_OutputShape = testParams.m_InputShape; + + testParams.m_InputData = + { + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f, + + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f + }; + + testParams.m_ExpectedOutputData = testParams.m_InputData; + + testParams.SetInOutQuantParams(1.5f, 3); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> SimpleResizeNearestNeighborTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::NearestNeighbor; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 2, 2 }; + testParams.m_OutputShape = { 1, 2, 1, 1 }; + + testParams.m_InputData = + { + 1.0f, 255.0f, + 200.0f, 250.0f, + + 250.0f, 200.0f, + 250.0f, 1.0f + }; + + // The 'resize' operation projects the top-left corner of output texels into the input image, + // then figures out the interpolants and weights. Note this is different to projecting the centre of the + // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as + // its single element, the value that was at position (0,0) of the input matrix (rather than an average, + // which we would expect if projecting the centre). + testParams.m_ExpectedOutputData = + { + 1.0f, + + 250.0f + }; + + testParams.SetInOutQuantParams(0.1567f, 1); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeNearestNeighborSqMinTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::NearestNeighbor; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 4, 4 }; + testParams.m_OutputShape = { 1, 2, 2, 2 }; + + testParams.m_InputData = + { + 1.0f, 2.0f, 3.0f, 4.0f, + 2.0f, 3.0f, 4.0f, 5.0f, + 3.0f, 4.0f, 5.0f, 6.0f, + 4.0f, 5.0f, 6.0f, 7.0f, + + 7.0f, 6.0f, 5.0f, 4.0f, + 6.0f, 5.0f, 4.0f, 3.0f, + 5.0f, 4.0f, 3.0f, 2.0f, + 4.0f, 3.0f, 2.0f, 1.0f + }; + + testParams.m_ExpectedOutputData = + { + 1.0f, 3.0f, + 3.0f, 5.0f, + + 7.0f, 5.0f, + 5.0f, 3.0f + }; + + testParams.SetInOutQuantParams(3.141592f, 3); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeNearestNeighborMinTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::NearestNeighbor; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 3, 5 }; + testParams.m_OutputShape = { 1, 2, 2, 3 }; + + testParams.m_InputData = + { + 1.5f, 3.0f, 4.5f, 6.0f, 7.5f, + 9.0f, 10.5f, 12.0f, 13.5f, 15.0f, + 16.5f, 18.0f, 19.5f, 21.0f, 22.5f, + + 16.5f, 18.0f, 19.5f, 21.0f, 22.5f, + 9.0f, 10.5f, 12.0f, 13.5f, 15.0f, + 1.5f, 3.0f, 4.5f, 6.0f, 7.5f + }; + + testParams.m_ExpectedOutputData = + { + 1.5f, 3.0f, 6.0f, + 9.0f, 10.5f, 13.5f, + + 16.5f, 18.0f, 21.0f, + 9.0f, 10.5f, 13.5f + }; + + testParams.SetInOutQuantParams(1.5f, -1); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +template<armnn::DataType ArmnnType, typename T> +LayerTestResult<T, 4> ResizeNearestNeighborMagTest( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout, + float inQuantScale, + int32_t inQuantOffset, + float outQuantScale, + int32_t outQuantOffset) +{ + ResizeTestParams testParams; + testParams.m_ResizeMethod = armnn::ResizeMethod::NearestNeighbor; + testParams.m_DataLayout = dataLayout; + + testParams.m_InputShape = { 1, 2, 3, 2 }; + testParams.m_OutputShape = { 1, 2, 3, 5 }; + + testParams.m_InputData = + { + 0.183005f, 2.379065f, + 1.054970f, 1.302565f, + 2.400595f, 0.688960f, + + 2.400595f, 0.688960f, + 1.054970f, 1.302565f, + 0.183005f, 2.379065f, + }; + + testParams.m_ExpectedOutputData = + { + 0.183005f, 0.183005f, 0.183005f, 2.379065f, 2.379065f, + 1.054970f, 1.054970f, 1.054970f, 1.302565f, 1.302565f, + 2.400595f, 2.400595f, 2.400595f, 0.688960f, 0.688960f, + + 2.400595f, 2.400595f, 2.400595f, 0.688960f, 0.688960f, + 1.054970f, 1.054970f, 1.054970f, 1.302565f, 1.302565f, + 0.183005f, 0.183005f, 0.183005f, 2.379065f, 2.379065f + }; + + testParams.SetInQuantParams(inQuantScale, inQuantOffset); + testParams.SetOutQuantParams(outQuantScale, outQuantOffset); + + return ResizeTestImpl<4, ArmnnType>(workloadFactory, memoryManager, testParams); +} + +// +// Explicit template instantiations +// + +// Float32 +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeBilinearNopTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +SimpleResizeBilinearTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeBilinearSqMinTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeBilinearMinTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeBilinearMagTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeNearestNeighborNopTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +SimpleResizeNearestNeighborTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeNearestNeighborSqMinTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeNearestNeighborMinTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float32>, 4> +ResizeNearestNeighborMagTest<armnn::DataType::Float32>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout, + float inQuantScale, + int32_t inQuantOffset, + float outQuantScale, + int32_t outQuantOffset); + +// Float16 +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeBilinearNopTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +SimpleResizeBilinearTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeBilinearSqMinTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeBilinearMinTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeBilinearMagTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeNearestNeighborNopTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +SimpleResizeNearestNeighborTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeNearestNeighborSqMinTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeNearestNeighborMinTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::Float16>, 4> +ResizeNearestNeighborMagTest<armnn::DataType::Float16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout, + float inQuantScale, + int32_t inQuantOffset, + float outQuantScale, + int32_t outQuantOffset); + +// QAsymm8 +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeBilinearNopTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +SimpleResizeBilinearTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeBilinearSqMinTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeBilinearMinTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeBilinearMagTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeNearestNeighborNopTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +SimpleResizeNearestNeighborTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeNearestNeighborSqMinTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeNearestNeighborMinTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedAsymm8>, 4> +ResizeNearestNeighborMagTest<armnn::DataType::QuantisedAsymm8>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout, + float inQuantScale, + int32_t inQuantOffset, + float outQuantScale, + int32_t outQuantOffset); + +// QSymm16 +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeBilinearNopTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +SimpleResizeBilinearTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeBilinearSqMinTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeBilinearMinTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeBilinearMagTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeNearestNeighborNopTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +SimpleResizeNearestNeighborTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeNearestNeighborSqMinTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeNearestNeighborMinTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); + +template LayerTestResult<armnn::ResolveType<armnn::DataType::QuantisedSymm16>, 4> +ResizeNearestNeighborMagTest<armnn::DataType::QuantisedSymm16>( + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout, + float inQuantScale, + int32_t inQuantOffset, + float outQuantScale, + int32_t outQuantOffset); diff --git a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp index 56ce51a844..4fe4b73ada 100644 --- a/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp +++ b/src/backends/backendsCommon/test/layerTests/ResizeTestImpl.hpp @@ -7,1017 +7,75 @@ #include "LayerTestResult.hpp" -#include <Permute.hpp> -#include <QuantizeHelper.hpp> #include <ResolveType.hpp> -#include <TensorUtils.hpp> -#include <armnn/ArmNN.hpp> +#include <armnn/Types.hpp> #include <backendsCommon/IBackendInternal.hpp> #include <backendsCommon/WorkloadFactory.hpp> -#include <backendsCommon/test/TensorCopyUtils.hpp> -#include <backendsCommon/test/WorkloadTestUtils.hpp> - -#include <test/TensorHelpers.hpp> - -// -// ResizeBilinear -// - +// Bilinear template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeBilinearNopTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-3); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-3); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f, - - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::Bilinear; - descriptor.m_Parameters.m_DataLayout = dataLayout; - - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleResizeBilinearTest( armnn::IWorkloadFactory& workloadFactory, const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 1, 1, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 1, 1, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(0.1567f); - inputTensorInfo.SetQuantizationOffset(1); - outputTensorInfo.SetQuantizationScale(0.1567f); - outputTensorInfo.SetQuantizationOffset(1); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 255, - 200, 250 - } - : std::initializer_list<float> - { - 1.0f, 255.0f, - 200.0f, 250.0f, - - 250.0f, 200.0f, - 250.0f, 1.0f - }; - - // The 'resize bilinear' operation projects the top-left corner of output texels into the input image, - // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as - // its single element, the value that was at position (0,0) of the input matrix (rather than an average, - // which we would expect if projecting the centre). - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1 - } - : std::initializer_list<float> - { - 1.0f, - - 250.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::Bilinear; - descriptor.m_Parameters.m_DataLayout = dataLayout; - - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeBilinearSqMinTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(3.141592f); - inputTensorInfo.SetQuantizationOffset(3); - outputTensorInfo.SetQuantizationScale(3.141592f); - outputTensorInfo.SetQuantizationOffset(3); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f, - - 7.0f, 6.0f, 5.0f, 4.0f, - 6.0f, 5.0f, 4.0f, 3.0f, - 5.0f, 4.0f, 3.0f, 2.0f, - 4.0f, 3.0f, 2.0f, 1.0f - }; - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 3, - 3, 5 - } - : std::initializer_list<float> - { - 1.0f, 3.0f, - 3.0f, 5.0f, - - 7.0f, 5.0f, - 5.0f, 3.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::Bilinear; - descriptor.m_Parameters.m_DataLayout = dataLayout; - - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeBilinearMinTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 2, 3, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 1, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 2, 3, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-1); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-1); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 3.0f, 4.5f, 6.0f, // 1, 2, 3, : Expected quantised values - 9.0f, 13.5f, 21.0f // 5, 8, 13 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, - 13.0f, 21.0f, 34.0f, 55.0f, 89.0f, - 144.0f, 233.0f, 377.0f, 610.0f, 987.0f, - - 987.0f, 610.0f, 377.0f, 233.0f, 144.0f, - 89.0f, 55.0f, 34.0f, 21.0f, 13.0f, - 8.0f, 5.0f, 3.0f, 2.0f, 1.0f - }; - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 3.0f, 5.25f // 1, 3 - } - : std::initializer_list<float> - { - 1.0f, 2.6666f, 6.00f, - 78.5f, 179.3333f, 401.00f, - - 987.0f, 454.6670f, 203.33f, - 48.5f, 22.3333f, 10.00f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::Bilinear; - descriptor.m_Parameters.m_DataLayout = dataLayout; - - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeBilinearMagTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 3, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 3, 2, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 3, 5, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(0.010765f); - inputTensorInfo.SetQuantizationOffset(7); - outputTensorInfo.SetQuantizationScale(0.010132f); - outputTensorInfo.SetQuantizationOffset(-18); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 0.183005f, 2.379065f, // 24, 228, : Expected quantised values - 1.054970f, 1.302565f, // 105, 128, - 2.400595f, 0.688960f // 230, 71 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, - 13.0f, 21.0f, - 144.0f, 233.0f, - - 233.0f, 144.0f, - 21.0f, 13.0f, - 2.0f, 1.0f - }; - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 0.18300501f, 1.06142902f, 1.93985295f, 2.37906504f, 2.37906504f, - 1.05497003f, 1.15400803f, 1.25304604f, 1.30256498f, 1.30256498f, - 2.40059495f, 1.71594095f, 1.03128707f, 0.68896002f, 0.68896002f - // 0, 87, 173, 217, 217, : Expected quantised values - // 86, 96, 106, 111, 111, - // 219, 151, 84, 50, 50 - } - : std::initializer_list<float> - { - 1.0f, 1.4f, 1.8f, 2.0f, 2.0f, - 13.0f, 16.2f, 19.4f, 21.0f, 21.0f, - 144.0f, 179.6f, 215.2f, 233.0f, 233.0f, - - 233.0f, 197.4f, 161.8f, 144.0f, 144.0f, - 21.0f, 17.8f, 14.6f, 13.0f, 13.0f, - 2.0f, 1.6f, 1.2f, 1.0f, 1.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::Bilinear; - descriptor.m_Parameters.m_DataLayout = dataLayout; - - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} - -// -// ResizeNearestNeighbor -// + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); +// NearestNeighbor template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeNearestNeighborNopTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-3); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-3); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f, - - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = input; - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor; - descriptor.m_Parameters.m_DataLayout = dataLayout; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> SimpleResizeNearestNeighborTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 1, 1, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 1, 1, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(0.1567f); - inputTensorInfo.SetQuantizationOffset(1); - outputTensorInfo.SetQuantizationScale(0.1567f); - outputTensorInfo.SetQuantizationOffset(1); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 255, - 200, 250 - } - : std::initializer_list<float> - { - 1.0f, 255.0f, - 200.0f, 250.0f, - - 250.0f, 200.0f, - 250.0f, 1.0f - }; - - // The 'resize' operation projects the top-left corner of output texels into the input image, - // then figures out the interpolants and weights. Note this is different to projecting the centre of the - // output texel. Thus, for a input matrix of 2x2, we'll expect the output 1x1 matrix to contain, as - // its single element, the value that was at position (0,0) of the input matrix (rather than an average, - // which we would expect if projecting the centre). - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1 - } - : std::initializer_list<float> - { - 1.0f, - - 250.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_DataLayout = dataLayout; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeNearestNeighborSqMinTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 4, 4, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 4, 4, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 2, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 2, 2, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(3.141592f); - inputTensorInfo.SetQuantizationOffset(3); - outputTensorInfo.SetQuantizationScale(3.141592f); - outputTensorInfo.SetQuantizationOffset(3); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 2, 3, 4, - 2, 3, 4, 5, - 3, 4, 5, 6, - 4, 5, 6, 7 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, 3.0f, 4.0f, - 2.0f, 3.0f, 4.0f, 5.0f, - 3.0f, 4.0f, 5.0f, 6.0f, - 4.0f, 5.0f, 6.0f, 7.0f, - - 7.0f, 6.0f, 5.0f, 4.0f, - 6.0f, 5.0f, 4.0f, 3.0f, - 5.0f, 4.0f, 3.0f, 2.0f, - 4.0f, 3.0f, 2.0f, 1.0f - }; - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 1, 3, - 3, 5 - } - : std::initializer_list<float> - { - 1.0f, 3.0f, - 3.0f, 5.0f, - - 7.0f, 5.0f, - 5.0f, 3.0f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_DataLayout = dataLayout; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeNearestNeighborMinTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 2, 3, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 1, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 2, 3, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(1.5f); - inputTensorInfo.SetQuantizationOffset(-1); - outputTensorInfo.SetQuantizationScale(1.5f); - outputTensorInfo.SetQuantizationOffset(-1); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 3.0f, 4.5f, 6.0f, // 1, 2, 3, : Expected quantised values - 9.0f, 13.5f, 21.0f // 5, 8, 13 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, 3.0f, 5.0f, 8.0f, - 13.0f, 21.0f, 34.0f, 55.0f, 89.0f, - 144.0f, 233.0f, 377.0f, 610.0f, 987.0f, - - 987.0f, 610.0f, 377.0f, 233.0f, 144.0f, - 89.0f, 55.0f, 34.0f, 21.0f, 13.0f, - 8.0f, 5.0f, 3.0f, 2.0f, 1.0f - }; - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 3.0f, 4.5f // 1, 3 - } - : std::initializer_list<float> - { - 1.f, 2.f, 5.f, - 13.f, 21.f, 55.f, - - 987.f, 610.f, 233.f, - 89.f, 55.f, 21.f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr<armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr<armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_DataLayout = dataLayout; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout); template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> LayerTestResult<T, 4> ResizeNearestNeighborMagTest( - armnn::IWorkloadFactory& workloadFactory, - const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, - const armnn::DataLayout dataLayout, - float inQuantScale, - int32_t inQuantOffset, - float outQuantScale, - int32_t outQuantOffset) -{ - armnn::TensorInfo inputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 3, 2, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 3, 2, dataLayout, ArmnnType); - - armnn::TensorInfo outputTensorInfo = armnn::IsQuantizedType<T>() - ? armnnUtils::GetTensorInfo(1, 1, 3, 5, dataLayout, ArmnnType) - : armnnUtils::GetTensorInfo(1, 2, 3, 5, dataLayout, ArmnnType); - - if (armnn::IsQuantizedType<T>()) - { - inputTensorInfo.SetQuantizationScale(inQuantScale); - inputTensorInfo.SetQuantizationOffset(inQuantOffset); - outputTensorInfo.SetQuantizationScale(outQuantScale); - outputTensorInfo.SetQuantizationOffset(outQuantOffset); - } - - std::vector<float> inputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 0.183005f, 2.379065f, // 24, 228, : expected quantised values - 1.054970f, 1.302565f, // 105, 128, - 2.400595f, 0.688960f // 230, 71 - } - : std::initializer_list<float> - { - 1.0f, 2.0f, - 13.0f, 21.0f, - 144.0f, 233.0f, - - 233.0f, 144.0f, - 21.0f, 13.0f, - 2.0f, 1.0f - }; - - std::vector<float> outputData = armnn::IsQuantizedType<T>() - ? std::initializer_list<float> - { - 0.183005f, 0.183005f, 0.183005f, 2.379065f, 2.379065f, - 1.054970f, 1.054970f, 1.054970f, 1.302565f, 1.302565f, - 2.400595f, 2.400595f, 2.400595f, 0.688960f, 0.688960f - } - : std::initializer_list<float> - { - 1.f, 1.f, 1.f, 2.f, 2.f, - 13.f, 13.f, 13.f, 21.f, 21.f, - 144.f, 144.f, 144.f, 233.f, 233.f, - - 233.f, 233.f, 233.f, 144.f, 144.f, - 21.f, 21.f, 21.f, 13.f, 13.f, - 2.f, 2.f, 2.f, 1.f, 1.f - }; - - const armnn::PermutationVector NCHWToNHWC = { 0, 3, 1, 2 }; - if (dataLayout == armnn::DataLayout::NHWC) - { - std::vector<float> tmp(inputData.size()); - armnnUtils::Permute(inputTensorInfo.GetShape(), NCHWToNHWC, inputData.data(), tmp.data(), sizeof(float)); - inputData = tmp; - - std::vector<float> tmp1(outputData.size()); - armnnUtils::Permute(outputTensorInfo.GetShape(), NCHWToNHWC, outputData.data(), tmp1.data(), sizeof(float)); - outputData = tmp1; - } - - auto input = MakeTensor<T, 4>(inputTensorInfo, - armnnUtils::QuantizedVector<T>(inputData, - inputTensorInfo.GetQuantizationScale(), - inputTensorInfo.GetQuantizationOffset())); - - LayerTestResult<T, 4> result(outputTensorInfo); - result.outputExpected = MakeTensor<T, 4>(outputTensorInfo, - armnnUtils::QuantizedVector<T>(outputData, - outputTensorInfo.GetQuantizationScale(), - outputTensorInfo.GetQuantizationOffset())); - - std::unique_ptr <armnn::ITensorHandle> inputHandle = workloadFactory.CreateTensorHandle(inputTensorInfo); - std::unique_ptr <armnn::ITensorHandle> outputHandle = workloadFactory.CreateTensorHandle(outputTensorInfo); - - armnn::ResizeQueueDescriptor descriptor; - descriptor.m_Parameters.m_DataLayout = dataLayout; - descriptor.m_Parameters.m_Method = armnn::ResizeMethod::NearestNeighbor; - armnn::WorkloadInfo info; - AddInputToWorkload(descriptor, info, inputTensorInfo, inputHandle.get()); - AddOutputToWorkload(descriptor, info, outputTensorInfo, outputHandle.get()); - - std::unique_ptr<armnn::IWorkload> workload = workloadFactory.CreateResize(descriptor, info); - - inputHandle->Allocate(); - outputHandle->Allocate(); - CopyDataToITensorHandle(inputHandle.get(), &input[0][0][0][0]); - - workload->PostAllocationConfigure(); - workload->Execute(); - - CopyDataFromITensorHandle(&result.output[0][0][0][0], outputHandle.get()); - return result; -} + armnn::IWorkloadFactory& workloadFactory, + const armnn::IBackendInternal::IMemoryManagerSharedPtr& memoryManager, + const armnn::DataLayout dataLayout, + float inQuantScale, + int32_t inQuantOffset, + float outQuantScale, + int32_t outQuantOffset); |