Sadik Armagan | 20bea00 | 2019-10-16 09:29:38 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2019 Arm Ltd. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | |
| 6 | #include "EndToEndTestImpl.hpp" |
| 7 | #include "LogSoftmaxEndToEndTestImpl.hpp" |
| 8 | |
| 9 | #include <armnn/INetwork.hpp> |
| 10 | |
| 11 | #include <test/TestUtils.hpp> |
| 12 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 13 | #include <doctest/doctest.h> |
Sadik Armagan | 20bea00 | 2019-10-16 09:29:38 +0100 | [diff] [blame] | 14 | |
| 15 | namespace { |
| 16 | |
| 17 | template <typename armnn::DataType DataType> |
| 18 | armnn::INetworkPtr CreateLogSoftmaxNetwork(const armnn::TensorShape& inputShape, |
| 19 | const armnn::TensorShape& outputShape, |
| 20 | const float beta, |
| 21 | const int axis, |
| 22 | const float qScale = 1.0f, |
| 23 | const int32_t qOffset = 0) |
| 24 | { |
| 25 | using namespace armnn; |
| 26 | |
| 27 | // Builds up the structure of the network. |
| 28 | INetworkPtr net(INetwork::Create()); |
| 29 | |
| 30 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset); |
| 31 | |
| 32 | LogSoftmaxDescriptor logSoftmaxDesc; |
| 33 | logSoftmaxDesc.m_Beta = beta; |
| 34 | logSoftmaxDesc.m_Axis = axis; |
| 35 | |
| 36 | IConnectableLayer* logSoftmax = net->AddLogSoftmaxLayer(logSoftmaxDesc, "Log_Softmax"); |
| 37 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 38 | Connect(input, logSoftmax, inputTensorInfo, 0, 0); |
| 39 | |
| 40 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| 41 | IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| 42 | Connect(logSoftmax, output, outputTensorInfo, 0, 0); |
| 43 | |
| 44 | return net; |
| 45 | } |
| 46 | |
| 47 | void LogSoftmaxEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 48 | armnn::TensorInfo& inputTensorInfo, |
| 49 | armnn::TensorInfo& outputTensorInfo, |
| 50 | std::vector<float>& inputData, |
| 51 | std::vector<float>& expectedOutputData, |
| 52 | const float beta, |
| 53 | const int axis) |
| 54 | { |
| 55 | using namespace armnn; |
| 56 | |
| 57 | // Builds up the structure of the network |
| 58 | INetworkPtr net = CreateLogSoftmaxNetwork<DataType::Float32>(inputTensorInfo.GetShape(), |
| 59 | outputTensorInfo.GetShape(), |
| 60 | beta, |
| 61 | axis); |
| 62 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 63 | CHECK(net); |
Sadik Armagan | 20bea00 | 2019-10-16 09:29:38 +0100 | [diff] [blame] | 64 | |
| 65 | std::map<int, std::vector<float>> inputTensorData = { {0, inputData} }; |
| 66 | std::map<int, std::vector<float>> expectedOutputTensorData = { {0, expectedOutputData} }; |
| 67 | |
| 68 | EndToEndLayerTestImpl<DataType::Float32, DataType::Float32>(move(net), |
| 69 | inputTensorData, |
| 70 | expectedOutputTensorData, |
| 71 | backends); |
| 72 | } |
| 73 | |
| 74 | } // anonymous namespace |
| 75 | |
| 76 | void LogSoftmaxEndToEndTest(const std::vector<armnn::BackendId>& defaultBackends) |
| 77 | { |
| 78 | using namespace armnn; |
| 79 | |
| 80 | const float beta = 10.0f; // non-default beta |
| 81 | const int axis = 3; // positive axis |
| 82 | |
| 83 | const TensorShape inputShape{1, 1, 2, 4}; |
| 84 | TensorInfo inputTensorInfo(inputShape, DataType::Float32); |
| 85 | |
| 86 | const TensorShape outputShape{1, 1, 2, 4}; |
| 87 | TensorInfo outputTensorInfo(outputShape, DataType::Float32); |
| 88 | |
| 89 | std::vector<float> inputData = std::vector<float>({ |
| 90 | 0.0f, -0.6f, 0.2f, 0.4f, |
| 91 | 0.3f, -0.2f, 1.0f, 0.1f |
| 92 | }); |
| 93 | |
| 94 | std::vector<float> expectedOutputData = std::vector<float>({ |
| 95 | -4.14297f, -10.14297f, -2.14297f, -0.14297f, |
| 96 | -7.00104f, -12.00104f, -0.00104087f, -9.00104f |
| 97 | }); |
| 98 | |
| 99 | LogSoftmaxEndToEnd(defaultBackends, |
| 100 | inputTensorInfo, |
| 101 | outputTensorInfo, |
| 102 | inputData, |
| 103 | expectedOutputData, |
| 104 | beta, |
| 105 | axis); |
| 106 | } |