Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
Matteo Martincigh | e011d20 | 2019-11-28 11:35:47 +0000 | [diff] [blame] | 7 | #include <armnnUtils/Permute.hpp> |
| 8 | |
Colm Donelan | c42a987 | 2022-02-02 16:35:09 +0000 | [diff] [blame] | 9 | #include <armnnUtils/QuantizeHelper.hpp> |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame] | 10 | #include <ResolveType.hpp> |
| 11 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 12 | #include <CommonTestUtils.hpp> |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame] | 13 | |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame] | 14 | #include <map> |
| 15 | #include <vector> |
| 16 | |
| 17 | namespace |
| 18 | { |
| 19 | |
| 20 | armnn::INetworkPtr CreateResizeNetwork(const armnn::ResizeDescriptor& descriptor, |
| 21 | const armnn::TensorInfo& inputInfo, |
| 22 | const armnn::TensorInfo& outputInfo) |
| 23 | { |
| 24 | using namespace armnn; |
| 25 | |
| 26 | INetworkPtr network(INetwork::Create()); |
| 27 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 28 | IConnectableLayer* resize = network->AddResizeLayer(descriptor, "resize"); |
| 29 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 30 | |
| 31 | Connect(input, resize, inputInfo, 0, 0); |
| 32 | Connect(resize, output, outputInfo, 0, 0); |
| 33 | |
| 34 | return network; |
| 35 | } |
| 36 | |
| 37 | template<armnn::DataType ArmnnType> |
| 38 | void ResizeEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 39 | armnn::DataLayout dataLayout, |
| 40 | armnn::ResizeMethod resizeMethod) |
| 41 | { |
| 42 | using namespace armnn; |
| 43 | using T = ResolveType<ArmnnType>; |
| 44 | |
| 45 | constexpr unsigned int inputWidth = 3u; |
| 46 | constexpr unsigned int inputHeight = inputWidth; |
| 47 | |
| 48 | constexpr unsigned int outputWidth = 5u; |
| 49 | constexpr unsigned int outputHeight = outputWidth; |
| 50 | |
| 51 | TensorShape inputShape = MakeTensorShape(1, 1, inputHeight, inputWidth, dataLayout); |
| 52 | TensorShape outputShape = MakeTensorShape(1, 1, outputHeight, outputWidth, dataLayout); |
| 53 | |
| 54 | const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; |
| 55 | const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; |
| 56 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 57 | TensorInfo inputInfo(inputShape, ArmnnType, qScale, qOffset, true); |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame] | 58 | TensorInfo outputInfo(outputShape, ArmnnType, qScale, qOffset); |
| 59 | |
| 60 | std::vector<float> inputData = |
| 61 | { |
| 62 | 1.f, 2.f, 3.f, |
| 63 | 4.f, 5.f, 6.f, |
| 64 | 7.f, 8.f, 9.f |
| 65 | }; |
| 66 | |
| 67 | std::vector<float> expectedOutputData; |
| 68 | switch(resizeMethod) |
| 69 | { |
| 70 | case ResizeMethod::Bilinear: |
| 71 | { |
| 72 | expectedOutputData = |
| 73 | { |
| 74 | 1.0f, 1.6f, 2.2f, 2.8f, 3.0f, |
| 75 | 2.8f, 3.4f, 4.0f, 4.6f, 4.8f, |
| 76 | 4.6f, 5.2f, 5.8f, 6.4f, 6.6f, |
| 77 | 6.4f, 7.0f, 7.6f, 8.2f, 8.4f, |
| 78 | 7.0f, 7.6f, 8.2f, 8.8f, 9.0f |
| 79 | }; |
| 80 | break; |
| 81 | } |
| 82 | case ResizeMethod::NearestNeighbor: |
| 83 | { |
| 84 | expectedOutputData = |
| 85 | { |
| 86 | 1.f, 1.f, 2.f, 2.f, 3.f, |
| 87 | 1.f, 1.f, 2.f, 2.f, 3.f, |
| 88 | 4.f, 4.f, 5.f, 5.f, 6.f, |
| 89 | 4.f, 4.f, 5.f, 5.f, 6.f, |
| 90 | 7.f, 7.f, 8.f, 8.f, 9.f |
| 91 | }; |
| 92 | break; |
| 93 | } |
| 94 | default: |
| 95 | { |
| 96 | throw InvalidArgumentException("Unrecognized resize method"); |
| 97 | } |
| 98 | } |
| 99 | |
| 100 | ResizeDescriptor descriptor; |
| 101 | descriptor.m_TargetWidth = outputWidth; |
| 102 | descriptor.m_TargetHeight = outputHeight; |
| 103 | descriptor.m_Method = resizeMethod; |
| 104 | descriptor.m_DataLayout = dataLayout; |
| 105 | |
| 106 | // swizzle data if needed |
| 107 | if (dataLayout == armnn::DataLayout::NHWC) |
| 108 | { |
| 109 | constexpr size_t dataTypeSize = sizeof(float); |
| 110 | const armnn::PermutationVector nchwToNhwc = { 0, 3, 1, 2 }; |
| 111 | |
| 112 | std::vector<float> tmp(inputData.size()); |
| 113 | armnnUtils::Permute(inputInfo.GetShape(), nchwToNhwc, inputData.data(), tmp.data(), dataTypeSize); |
| 114 | inputData = tmp; |
| 115 | } |
| 116 | |
| 117 | // quantize data |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 118 | std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); |
| 119 | std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); |
Aron Virginas-Tar | fe15eff | 2019-07-01 16:12:58 +0100 | [diff] [blame] | 120 | |
| 121 | INetworkPtr network = CreateResizeNetwork(descriptor, inputInfo, outputInfo); |
| 122 | |
| 123 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| 124 | { { 0, qInputData } }, |
| 125 | { { 0, qExpectedOutputData } }, |
| 126 | backends); |
| 127 | } |
| 128 | |
| 129 | } // anonymous namespace |
| 130 | |
| 131 | template<armnn::DataType ArmnnType> |
| 132 | void ResizeBilinearEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 133 | armnn::DataLayout dataLayout) |
| 134 | { |
| 135 | ResizeEndToEnd<ArmnnType>(backends, dataLayout, armnn::ResizeMethod::Bilinear); |
| 136 | } |
| 137 | |
| 138 | template<armnn::DataType ArmnnType> |
| 139 | void ResizeNearestNeighborEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 140 | armnn::DataLayout dataLayout) |
| 141 | { |
| 142 | ResizeEndToEnd<ArmnnType>(backends, dataLayout, armnn::ResizeMethod::NearestNeighbor); |
Aron Virginas-Tar | 48623a0 | 2019-10-22 10:00:28 +0100 | [diff] [blame] | 143 | } |