Sadik Armagan | f0a6dec | 2021-03-25 07:46:55 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2021 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include "CommonTestUtils.hpp" |
| 8 | |
| 9 | #include <ResolveType.hpp> |
| 10 | |
| 11 | #include <armnn/INetwork.hpp> |
| 12 | |
| 13 | #include <armnn/utility/NumericCast.hpp> |
| 14 | |
| 15 | #include <boost/test/unit_test.hpp> |
| 16 | |
| 17 | #include <vector> |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | |
| 22 | armnn::INetworkPtr CreateFullyConnectedNetworkNonConstWeights(const armnn::TensorInfo& inputTensorInfo, |
| 23 | const armnn::TensorInfo& outputTensorInfo, |
| 24 | const armnn::TensorInfo& weightsTensorInfo, |
| 25 | armnn::FullyConnectedDescriptor descriptor) |
| 26 | { |
| 27 | armnn::INetworkPtr network(armnn::INetwork::Create()); |
| 28 | |
| 29 | armnn::IConnectableLayer* inputLayer = network->AddInputLayer(0, "Input"); |
| 30 | armnn::IConnectableLayer* weightsInputLayer = network->AddInputLayer(1, "Weights_Input"); |
| 31 | armnn::IConnectableLayer* fullyConnectedLayer = network->AddFullyConnectedLayer(descriptor, |
| 32 | armnn::EmptyOptional(), |
| 33 | armnn::EmptyOptional(), |
| 34 | "Fully_Connected"); |
| 35 | armnn::IConnectableLayer* outputLayer = network->AddOutputLayer(0, "Output"); |
| 36 | |
| 37 | Connect(inputLayer, fullyConnectedLayer, inputTensorInfo, 0, 0); |
| 38 | Connect(weightsInputLayer, fullyConnectedLayer, weightsTensorInfo, 0, 1); |
| 39 | Connect(fullyConnectedLayer, outputLayer, outputTensorInfo, 0, 0); |
| 40 | |
| 41 | return network; |
| 42 | } |
| 43 | |
| 44 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 45 | void FullyConnectedWithDynamicWeightsEndToEnd(const std::vector<armnn::BackendId>& backends) |
| 46 | { |
| 47 | using namespace armnn; |
| 48 | |
| 49 | armnn::TensorInfo inputTensorInfo({ 1, 1, 2, 3 }, ArmnnType); |
| 50 | inputTensorInfo.SetQuantizationScale(0.1f); |
| 51 | inputTensorInfo.SetQuantizationOffset(63); |
| 52 | |
| 53 | armnn::TensorInfo outputTensorInfo({ 1, 2 }, ArmnnType); |
| 54 | outputTensorInfo.SetQuantizationScale(5.f); |
| 55 | outputTensorInfo.SetQuantizationOffset(10); |
| 56 | |
| 57 | armnn::TensorInfo weightsTensorInfo({ 2, 6 }, ArmnnType); |
| 58 | weightsTensorInfo.SetQuantizationScale(0.2f); |
| 59 | weightsTensorInfo.SetQuantizationOffset(93); |
| 60 | |
| 61 | FullyConnectedDescriptor descriptor; |
| 62 | descriptor.m_ConstantWeights = false; |
| 63 | descriptor.m_BiasEnabled = false; |
| 64 | descriptor.m_TransposeWeightMatrix = true; |
| 65 | |
| 66 | std::vector<T> inputData { |
| 67 | -1.2f, 6.1f, -3.5f, |
| 68 | 18.8f, -5.5f, 2.9f |
| 69 | }; |
| 70 | |
| 71 | std::vector<T> weightsData { |
| 72 | -8.4f, 20.0f, -10.4f, -8, 16.4f, -11.8f, |
| 73 | 23.4f, 10.4f, -14.0f, -3.8f, -11.8f, 11.4f |
| 74 | }; |
| 75 | |
| 76 | std::vector<T> floatExpectedOutputData { |
| 77 | -107.04f, 110.f |
| 78 | }; |
| 79 | std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>(floatExpectedOutputData); |
| 80 | |
| 81 | armnn::INetworkPtr network = CreateFullyConnectedNetworkNonConstWeights(inputTensorInfo, |
| 82 | outputTensorInfo, |
| 83 | weightsTensorInfo, |
| 84 | descriptor); |
| 85 | |
| 86 | BOOST_TEST_CHECKPOINT("create a network"); |
| 87 | |
| 88 | std::map<int, std::vector<T>> inputTensorData = {{ 0, inputData }, {1, weightsData}}; |
| 89 | std::map<int, std::vector<T>> expectedOutputTensorData = {{ 0, expectedOutputData }}; |
| 90 | |
| 91 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(network), |
| 92 | inputTensorData, |
| 93 | expectedOutputTensorData, |
| 94 | backends, |
| 95 | 1.0f); |
| 96 | } |
| 97 | } // anonymous namespace |