| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #pragma once |
| |
| #include "CommonTestUtils.hpp" |
| |
| #include <QuantizeHelper.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <armnn/ArmNN.hpp> |
| |
| namespace |
| { |
| |
| armnn::INetworkPtr CreateAbsNetwork(const armnn::TensorInfo& tensorInfo) |
| { |
| armnn::INetworkPtr network(armnn::INetwork::Create()); |
| |
| armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "input"); |
| armnn::IConnectableLayer* absLayer = network->AddAbsLayer("abs"); |
| armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "output"); |
| |
| Connect(inputLayer, absLayer, tensorInfo, 0, 0); |
| Connect(absLayer, outputLayer, tensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| } // anonymous namespace |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void AbsEndToEnd(const std::vector<armnn::BackendId>& backends) |
| { |
| using namespace armnn; |
| |
| const float qScale = IsQuantizedType<T>() ? 0.25f : 1.0f; |
| const int32_t qOffset = IsQuantizedType<T>() ? 50 : 0; |
| |
| TensorInfo tensorInfo({ 1, 1, 2, 3 }, ArmnnType, qScale, qOffset); |
| |
| std::vector<float> inputData = |
| { |
| -1.f, 2.f, -3.f, |
| 4.f, -5.f, 6.f |
| }; |
| |
| std::vector<float> expectedOutputData = |
| { |
| 1.f, 2.f, 3.f, |
| 4.f, 5.f, 6.f |
| }; |
| |
| // quantize data |
| std::vector<T> qInputData = armnnUtils::QuantizedVector<T>(inputData, qScale, qOffset); |
| std::vector<T> qExpectedOutputData = armnnUtils::QuantizedVector<T>(expectedOutputData, qScale, qOffset); |
| |
| INetworkPtr network = CreateAbsNetwork(tensorInfo); |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| { { 0, qInputData } }, |
| { { 0, qExpectedOutputData } }, |
| backends); |
| } |