Nikhil Raj | 747f586 | 2019-07-19 15:15:23 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <ResolveType.hpp> |
| 8 | |
| 9 | #include <armnn/INetwork.hpp> |
| 10 | |
| 11 | #include <backendsCommon/test/CommonTestUtils.hpp> |
| 12 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 13 | #include <doctest/doctest.h> |
| 14 | |
Nikhil Raj | 747f586 | 2019-07-19 15:15:23 +0100 | [diff] [blame] | 15 | namespace |
| 16 | { |
| 17 | template<typename armnn::DataType DataType> |
| 18 | INetworkPtr CreatePreluNetwork(const armnn::TensorInfo& inputInfo, |
| 19 | const armnn::TensorInfo& alphaInfo, |
| 20 | const armnn::TensorInfo& outputInfo) |
| 21 | { |
| 22 | using namespace armnn; |
| 23 | |
| 24 | INetworkPtr net(INetwork::Create()); |
| 25 | |
| 26 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 27 | IConnectableLayer* alpha = net->AddInputLayer(1, "alpha"); |
| 28 | IConnectableLayer* prelu = net->AddPreluLayer("Prelu"); |
| 29 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 30 | |
| 31 | Connect(input, prelu, inputInfo, 0, 0); |
| 32 | Connect(alpha, prelu, alphaInfo, 0, 1); |
| 33 | Connect(prelu, output, outputInfo, 0, 0); |
| 34 | |
| 35 | return net; |
| 36 | } |
| 37 | |
| 38 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 39 | void PreluEndToEnd(const std::vector<BackendId>& backends, |
| 40 | const std::vector<T>& inputData, |
| 41 | const std::vector<T>& alphaData, |
| 42 | const std::vector<T>& expectedOutputData, |
| 43 | const float qScale , |
| 44 | const int32_t qOffset) |
| 45 | { |
| 46 | using namespace armnn; |
| 47 | |
| 48 | armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType); |
| 49 | armnn::TensorInfo alphaInfo({ 1, 2, 2, 1 }, ArmnnType); |
| 50 | armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType); |
| 51 | |
| 52 | inputInfo.SetQuantizationOffset(qOffset); |
| 53 | inputInfo.SetQuantizationScale(qScale); |
| 54 | alphaInfo.SetQuantizationOffset(qOffset); |
| 55 | alphaInfo.SetQuantizationScale(qScale); |
| 56 | outputInfo.SetQuantizationOffset(qOffset); |
| 57 | outputInfo.SetQuantizationScale(qScale); |
| 58 | |
| 59 | INetworkPtr net = CreatePreluNetwork<ArmnnType>(inputInfo, alphaInfo, outputInfo); |
| 60 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 61 | CHECK(net); |
Nikhil Raj | 747f586 | 2019-07-19 15:15:23 +0100 | [diff] [blame] | 62 | |
| 63 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData }, { 1, alphaData} }; |
| 64 | std::map<int, std::vector<T>> expectedOutputTensorData = { { 0, expectedOutputData } }; |
| 65 | |
| 66 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), |
| 67 | inputTensorData, |
| 68 | expectedOutputTensorData, |
| 69 | backends); |
| 70 | } |
| 71 | |
| 72 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 73 | void PreluEndToEndPositiveTest(const std::vector<BackendId>& backends, const float qScale = 1.0f, |
| 74 | const int32_t qOffset = 2) |
| 75 | { |
| 76 | std::vector<T> inputData{ 1, 2, 3, 4, 5, 6, 7, 8 }; |
| 77 | std::vector<T> alphaData{ 2, 1, 1, 1 }; |
| 78 | |
| 79 | std::vector<T> expectedOutputData{ 2, 2, 3, 4, 5, 6, 7, 8 }; |
| 80 | |
| 81 | PreluEndToEnd<ArmnnType>(backends, inputData, alphaData, expectedOutputData, qScale, qOffset); |
| 82 | } |
| 83 | |
| 84 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 85 | void PreluEndToEndNegativeTest(const std::vector<BackendId>& backends, const float qScale = 1.0f, |
| 86 | const int32_t qOffset = 0) |
| 87 | { |
| 88 | std::vector<T> inputData{ 1, -2, 3, 4, 5, 6, 7, 8 }; |
| 89 | std::vector<T> alphaData{ 1, 2, 1, 1 }; |
| 90 | |
| 91 | std::vector<T> expectedOutputData{ 1, -4, 3, 4, 5, 6, 7, 8 }; |
| 92 | |
| 93 | PreluEndToEnd<ArmnnType>(backends, inputData, alphaData, expectedOutputData, qScale, qOffset); |
| 94 | } |
| 95 | |
| 96 | } // anonymous namespace |