| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <ResolveType.hpp> |
| |
| #include <armnn/INetwork.hpp> |
| |
| #include <backendsCommon/test/CommonTestUtils.hpp> |
| |
| namespace |
| { |
| template<typename armnn::DataType DataType> |
| INetworkPtr CreatePreluNetwork(const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& alphaInfo, |
| const armnn::TensorInfo& outputInfo) |
| { |
| using namespace armnn; |
| |
| INetworkPtr net(INetwork::Create()); |
| |
| IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| IConnectableLayer* alpha = net->AddInputLayer(1, "alpha"); |
| IConnectableLayer* prelu = net->AddPreluLayer("Prelu"); |
| IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| |
| Connect(input, prelu, inputInfo, 0, 0); |
| Connect(alpha, prelu, alphaInfo, 0, 1); |
| Connect(prelu, output, outputInfo, 0, 0); |
| |
| return net; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void PreluEndToEnd(const std::vector<BackendId>& backends, |
| const std::vector<T>& inputData, |
| const std::vector<T>& alphaData, |
| const std::vector<T>& expectedOutputData, |
| const float qScale , |
| const int32_t qOffset) |
| { |
| using namespace armnn; |
| |
| armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType); |
| armnn::TensorInfo alphaInfo({ 1, 2, 2, 1 }, ArmnnType); |
| armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType); |
| |
| inputInfo.SetQuantizationOffset(qOffset); |
| inputInfo.SetQuantizationScale(qScale); |
| alphaInfo.SetQuantizationOffset(qOffset); |
| alphaInfo.SetQuantizationScale(qScale); |
| outputInfo.SetQuantizationOffset(qOffset); |
| outputInfo.SetQuantizationScale(qScale); |
| |
| INetworkPtr net = CreatePreluNetwork<ArmnnType>(inputInfo, alphaInfo, outputInfo); |
| |
| BOOST_TEST_CHECKPOINT("Create a network"); |
| |
| std::map<int, std::vector<T>> inputTensorData = { { 0, inputData }, { 1, alphaData} }; |
| std::map<int, std::vector<T>> expectedOutputTensorData = { { 0, expectedOutputData } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), |
| inputTensorData, |
| expectedOutputTensorData, |
| backends); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void PreluEndToEndPositiveTest(const std::vector<BackendId>& backends, const float qScale = 1.0f, |
| const int32_t qOffset = 2) |
| { |
| std::vector<T> inputData{ 1, 2, 3, 4, 5, 6, 7, 8 }; |
| std::vector<T> alphaData{ 2, 1, 1, 1 }; |
| |
| std::vector<T> expectedOutputData{ 2, 2, 3, 4, 5, 6, 7, 8 }; |
| |
| PreluEndToEnd<ArmnnType>(backends, inputData, alphaData, expectedOutputData, qScale, qOffset); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void PreluEndToEndNegativeTest(const std::vector<BackendId>& backends, const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| std::vector<T> inputData{ 1, -2, 3, 4, 5, 6, 7, 8 }; |
| std::vector<T> alphaData{ 1, 2, 1, 1 }; |
| |
| std::vector<T> expectedOutputData{ 1, -4, 3, 4, 5, 6, 7, 8 }; |
| |
| PreluEndToEnd<ArmnnType>(backends, inputData, alphaData, expectedOutputData, qScale, qOffset); |
| } |
| |
| } // anonymous namespace |