| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "LayerTestResult.hpp" |
| |
| #include <Permute.hpp> |
| #include <QuantizeHelper.hpp> |
| #include <ResolveType.hpp> |
| #include <TensorUtils.hpp> |
| |
| #include <armnn/ArmNN.hpp> |
| |
| #include <backendsCommon/IBackendInternal.hpp> |
| #include <backendsCommon/WorkloadFactory.hpp> |
| |
| #include <backendsCommon/test/TensorCopyUtils.hpp> |
| #include <backendsCommon/test/WorkloadTestUtils.hpp> |
| |
| #include <test/TensorHelpers.hpp> |
| |
| // |
| // ResizeBilinear |
| // |
| |
| 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; |
| } |
| |
| 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; |
| } |
| |
| 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; |
| } |
| |
| 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; |
| } |
| |
| 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 |
| // |
| |
| 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; |
| } |
| |
| 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; |
| } |
| |
| 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; |
| } |
| |
| 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; |
| } |
| |
| 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; |
| } |