Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #pragma once |
| 7 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 8 | #include <CommonTestUtils.hpp> |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 9 | |
| 10 | #include <armnn/INetwork.hpp> |
| 11 | #include <ResolveType.hpp> |
| 12 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 13 | #include <doctest/doctest.h> |
| 14 | |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 15 | namespace |
| 16 | { |
| 17 | |
| 18 | template<typename T> |
| 19 | armnn::INetworkPtr CreateDequantizeNetwork(const armnn::TensorInfo& inputInfo, |
| 20 | const armnn::TensorInfo& outputInfo) |
| 21 | { |
| 22 | armnn::INetworkPtr net(armnn::INetwork::Create()); |
| 23 | |
| 24 | armnn::IConnectableLayer* inputLayer = net->AddInputLayer(0); |
| 25 | armnn::IConnectableLayer* dequantizeLayer = net->AddDequantizeLayer("Dequantize"); |
| 26 | armnn::IConnectableLayer* outputLayer = net->AddOutputLayer(0, "output"); |
| 27 | Connect(inputLayer, dequantizeLayer, inputInfo, 0, 0); |
| 28 | Connect(dequantizeLayer, outputLayer, outputInfo, 0, 0); |
| 29 | |
| 30 | return net; |
| 31 | } |
| 32 | |
| 33 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 34 | void DequantizeEndToEndLayerTestImpl(const std::vector<BackendId>& backends, |
| 35 | const armnn::TensorShape& tensorShape, |
| 36 | const std::vector<T>& input, |
| 37 | const std::vector<float>& expectedOutput, |
| 38 | float scale, |
| 39 | int32_t offset) |
| 40 | { |
| 41 | armnn::TensorInfo inputInfo(tensorShape, ArmnnType); |
| 42 | armnn::TensorInfo outputInfo(tensorShape, armnn::DataType::Float32); |
| 43 | |
| 44 | inputInfo.SetQuantizationScale(scale); |
| 45 | inputInfo.SetQuantizationOffset(offset); |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 46 | inputInfo.SetConstant(true); |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 47 | |
| 48 | // Builds up the structure of the network |
| 49 | armnn::INetworkPtr net = CreateDequantizeNetwork<T>(inputInfo, outputInfo); |
| 50 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 51 | CHECK(net); |
Narumol Prangnawarat | 8c7324d | 2019-05-31 16:42:11 +0100 | [diff] [blame] | 52 | |
| 53 | std::map<int, std::vector<T>> inputTensorData = { { 0, input } }; |
| 54 | std::map<int, std::vector<float>> expectedOutputData = { { 0, expectedOutput } }; |
| 55 | |
| 56 | EndToEndLayerTestImpl<ArmnnType, armnn::DataType::Float32>( |
| 57 | move(net), inputTensorData, expectedOutputData, backends); |
| 58 | } |
| 59 | |
| 60 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 61 | void DequantizeEndToEndSimple(const std::vector<BackendId>& backends) |
| 62 | { |
| 63 | const armnn::TensorShape tensorShape({ 1, 2, 2, 4 }); |
| 64 | std::vector<T> inputData = std::vector<T>( |
| 65 | { |
| 66 | 2, 4, 6, 8, |
| 67 | 10, 12, 14, 16, |
| 68 | 18, 20, 22, 24, |
| 69 | 26, 28, 30, 32 |
| 70 | }); |
| 71 | |
| 72 | std::vector<float> expectedOutputData = std::vector<float>( |
| 73 | { |
| 74 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 75 | 5.0f, 6.0f, 7.0f, 8.0f, |
| 76 | 9.0f, 10.0f, 11.0f, 12.0f, |
| 77 | 13.0f, 14.0f, 15.0f, 16.0f |
| 78 | }); |
| 79 | DequantizeEndToEndLayerTestImpl<ArmnnType>(backends, tensorShape, inputData, expectedOutputData, 0.5f, 0); |
| 80 | }; |
| 81 | |
| 82 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 83 | void DequantizeEndToEndOffset(const std::vector<BackendId>& backends) |
| 84 | { |
| 85 | const armnn::TensorShape tensorShape({ 1, 2, 2, 4 }); |
| 86 | std::vector<T> inputData = std::vector<T>( |
| 87 | { |
| 88 | 3, 5, 7, 9, |
| 89 | 11, 13, 15, 17, |
| 90 | 19, 21, 23, 25, |
| 91 | 27, 29, 31, 33 |
| 92 | }); |
| 93 | |
| 94 | std::vector<float> expectedOutputData = std::vector<float>( |
| 95 | { |
| 96 | 1.0f, 2.0f, 3.0f, 4.0f, |
| 97 | 5.0f, 6.0f, 7.0f, 8.0f, |
| 98 | 9.0f, 10.0f, 11.0f, 12.0f, |
| 99 | 13.0f, 14.0f, 15.0f, 16.0f |
| 100 | }); |
| 101 | DequantizeEndToEndLayerTestImpl<ArmnnType>(backends, tensorShape, inputData, expectedOutputData, 0.5f, 1); |
| 102 | }; |
| 103 | |
| 104 | } // anonymous namespace |