Teresa Charlin | b2d3ec5 | 2022-04-12 22:07:09 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | #include <CommonTestUtils.hpp> |
| 9 | |
| 10 | #include <armnn/INetwork.hpp> |
| 11 | #include <ResolveType.hpp> |
| 12 | |
| 13 | #include <doctest/doctest.h> |
| 14 | |
| 15 | namespace{ |
| 16 | |
| 17 | armnn::INetworkPtr CreateGatherNdNetwork(const armnn::TensorInfo& paramsInfo, |
| 18 | const armnn::TensorInfo& indicesInfo, |
| 19 | const armnn::TensorInfo& outputInfo, |
| 20 | const std::vector<int32_t>& indicesData) |
| 21 | { |
| 22 | armnn::INetworkPtr net(armnn::INetwork::Create()); |
| 23 | |
| 24 | armnn::IConnectableLayer* paramsLayer = net->AddInputLayer(0); |
| 25 | armnn::IConnectableLayer* indicesLayer = net->AddConstantLayer(armnn::ConstTensor(indicesInfo, indicesData)); |
| 26 | armnn::IConnectableLayer* gatherNdLayer = net->AddGatherNdLayer("gatherNd"); |
| 27 | armnn::IConnectableLayer* outputLayer = net->AddOutputLayer(0, "output"); |
| 28 | Connect(paramsLayer, gatherNdLayer, paramsInfo, 0, 0); |
| 29 | Connect(indicesLayer, gatherNdLayer, indicesInfo, 0, 1); |
| 30 | Connect(gatherNdLayer, outputLayer, outputInfo, 0, 0); |
| 31 | |
| 32 | return net; |
| 33 | } |
| 34 | |
| 35 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 36 | void GatherNdEndToEnd(const std::vector<BackendId>& backends) |
| 37 | { |
| 38 | armnn::TensorInfo paramsInfo({ 2, 3, 8, 4 }, ArmnnType); |
| 39 | armnn::TensorInfo indicesInfo({ 2, 2 }, armnn::DataType::Signed32); |
| 40 | armnn::TensorInfo outputInfo({ 2, 8, 4 }, ArmnnType); |
| 41 | |
| 42 | paramsInfo.SetQuantizationScale(1.0f); |
| 43 | paramsInfo.SetQuantizationOffset(0); |
| 44 | paramsInfo.SetConstant(true); |
| 45 | indicesInfo.SetConstant(true); |
| 46 | outputInfo.SetQuantizationScale(1.0f); |
| 47 | outputInfo.SetQuantizationOffset(0); |
| 48 | |
| 49 | // Creates structures for input & output. |
| 50 | std::vector<T> paramsData{ |
| 51 | 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, |
| 52 | 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, |
| 53 | |
| 54 | 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, |
| 55 | 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, |
| 56 | |
| 57 | 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, |
| 58 | 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, |
| 59 | |
| 60 | 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, |
| 61 | 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, |
| 62 | |
| 63 | 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, |
| 64 | 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, |
| 65 | |
| 66 | 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, |
| 67 | 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191 |
| 68 | }; |
| 69 | |
| 70 | std::vector<int32_t> indicesData{ |
| 71 | { 1, 2, 1, 1}, |
| 72 | }; |
| 73 | |
| 74 | std::vector<T> expectedOutput{ |
| 75 | 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, |
| 76 | 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, |
| 77 | |
| 78 | 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, |
| 79 | 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159 |
| 80 | }; |
| 81 | |
| 82 | // Builds up the structure of the network |
| 83 | armnn::INetworkPtr net = CreateGatherNdNetwork(paramsInfo, indicesInfo, outputInfo, indicesData); |
| 84 | |
| 85 | CHECK(net); |
| 86 | |
| 87 | std::map<int, std::vector<T>> inputTensorData = {{ 0, paramsData }}; |
| 88 | std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }}; |
| 89 | |
| 90 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends); |
| 91 | } |
| 92 | |
| 93 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 94 | void GatherNdMultiDimEndToEnd(const std::vector<BackendId>& backends) |
| 95 | { |
| 96 | armnn::TensorInfo paramsInfo({ 5, 5, 2 }, ArmnnType); |
| 97 | armnn::TensorInfo indicesInfo({ 2, 2, 3, 2 }, armnn::DataType::Signed32); |
| 98 | armnn::TensorInfo outputInfo({ 2, 2, 3, 2 }, ArmnnType); |
| 99 | |
| 100 | paramsInfo.SetQuantizationScale(1.0f); |
| 101 | paramsInfo.SetQuantizationOffset(0); |
| 102 | paramsInfo.SetConstant(true); |
| 103 | indicesInfo.SetConstant(true); |
| 104 | outputInfo.SetQuantizationScale(1.0f); |
| 105 | outputInfo.SetQuantizationOffset(0); |
| 106 | |
| 107 | // Creates structures for input & output. |
| 108 | std::vector<T> paramsData{ |
| 109 | 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, |
| 110 | 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, |
| 111 | 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, |
| 112 | 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, |
| 113 | 40, 41, 42, 43, 44, 45, 46, 47, 48, 49 |
| 114 | }; |
| 115 | |
| 116 | std::vector<int32_t> indicesData{ |
| 117 | 0, 0, |
| 118 | 3, 3, |
| 119 | 4, 4, |
| 120 | |
| 121 | 0, 0, |
| 122 | 1, 1, |
| 123 | 2, 2, |
| 124 | |
| 125 | 4, 4, |
| 126 | 3, 3, |
| 127 | 0, 0, |
| 128 | |
| 129 | 2, 2, |
| 130 | 1, 1, |
| 131 | 0, 0 |
| 132 | }; |
| 133 | |
| 134 | std::vector<T> expectedOutput{ |
| 135 | 0, 1, |
| 136 | 36, 37, |
| 137 | 48, 49, |
| 138 | |
| 139 | 0, 1, |
| 140 | 12, 13, |
| 141 | 24, 25, |
| 142 | |
| 143 | 48, 49, |
| 144 | 36, 37, |
| 145 | 0, 1, |
| 146 | |
| 147 | 24, 25, |
| 148 | 12, 13, |
| 149 | 0, 1 |
| 150 | }; |
| 151 | |
| 152 | // Builds up the structure of the network |
| 153 | armnn::INetworkPtr net = CreateGatherNdNetwork(paramsInfo, indicesInfo, outputInfo, indicesData); |
| 154 | |
| 155 | std::map<int, std::vector<T>> inputTensorData = {{ 0, paramsData }}; |
| 156 | std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }}; |
| 157 | |
| 158 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), inputTensorData, expectedOutputData, backends); |
| 159 | } |
| 160 | |
| 161 | } // anonymous namespace |