| Declan-ARM | 1bf56cd | 2023-07-20 17:32:57 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
| 8 | namespace |
| 9 | { |
| 10 | |
| 11 | armnn::INetworkPtr CreateReverseV2Network(const armnn::TensorInfo& inputInfo, |
| 12 | const armnn::TensorInfo& axisInfo, |
| 13 | const armnn::TensorInfo& outputInfo, |
| 14 | const std::vector<int32_t>& axisData) |
| 15 | { |
| 16 | using namespace armnn; |
| 17 | |
| 18 | INetworkPtr network(INetwork::Create()); |
| 19 | |
| 20 | IConnectableLayer* inputLayer = network->AddInputLayer(0, "input"); |
| 21 | IConnectableLayer* axisLayer = network->AddConstantLayer(ConstTensor(axisInfo, axisData)); |
| 22 | IConnectableLayer* reverseLayer = network->AddReverseV2Layer("reverseV2"); |
| 23 | IConnectableLayer* outputLayer = network->AddOutputLayer(0, "output"); |
| 24 | |
| 25 | Connect(inputLayer, reverseLayer, inputInfo, 0, 0); |
| 26 | Connect(axisLayer, reverseLayer, axisInfo, 0, 1); |
| 27 | Connect(reverseLayer, outputLayer, outputInfo, 0, 0); |
| 28 | |
| 29 | return network; |
| 30 | } |
| 31 | |
| 32 | template <armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 33 | void ReverseV2EndToEnd(const std::vector<BackendId>& backends) |
| 34 | { |
| 35 | float qScale = 1.0f; |
| 36 | int32_t qOffset = 0; |
| 37 | bool qConst = true; |
| 38 | |
| 39 | TensorInfo inputInfo ( { 2, 3, 4 }, ArmnnType, qScale, qOffset, qConst); |
| 40 | TensorInfo axisInfo ( { 1 } , DataType::Signed32, qScale, qOffset, qConst); |
| 41 | TensorInfo outputInfo ( { 2, 3, 4 }, ArmnnType, qScale, qOffset); |
| 42 | |
| 43 | std::vector<T> inputData = armnnUtils::QuantizedVector<T>({ |
| 44 | 1, 2, 3, 4, |
| 45 | 5, 6 , 7, 8, |
| 46 | 9, 10, 11, 12, |
| 47 | 13, 14, 15, 16, |
| 48 | 17, 18, 19, 20, |
| 49 | 21, 22, 23, 24 |
| 50 | }, qScale, qOffset); |
| 51 | |
| 52 | std::vector<int32_t> axisData = { 1 }; |
| 53 | |
| 54 | std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>({ |
| 55 | 9, 10, 11, 12, |
| 56 | 5, 6, 7 , 8, |
| 57 | 1, 2, 3, 4, |
| 58 | 21, 22, 23, 24, |
| 59 | 17, 18, 19, 20, |
| 60 | 13, 14, 15, 16 |
| 61 | }, qScale, qOffset); |
| 62 | |
| 63 | INetworkPtr network = CreateReverseV2Network(inputInfo, axisInfo, outputInfo, axisData); |
| 64 | |
| 65 | std::map<int, std::vector<T>> inputTensor = { { 0, inputData } }; |
| 66 | std::map<int, std::vector<T>> expectedOutputTensor = { { 0, expectedOutputData } }; |
| 67 | |
| 68 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensor, expectedOutputTensor, backends); |
| 69 | } |
| 70 | |
| 71 | } // anonymous namespace |
| 72 | |