| // |
| // Copyright © 2019,2021,2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| |
| #include "EndToEndTestImpl.hpp" |
| #include "LogSoftmaxEndToEndTestImpl.hpp" |
| |
| #include <armnn/INetwork.hpp> |
| |
| #include <TestUtils.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| namespace { |
| |
| template <typename armnn::DataType DataType> |
| armnn::INetworkPtr CreateLogSoftmaxNetwork(const armnn::TensorShape& inputShape, |
| const armnn::TensorShape& outputShape, |
| const float beta, |
| const int axis, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| using namespace armnn; |
| |
| // Builds up the structure of the network. |
| INetworkPtr net(INetwork::Create()); |
| |
| TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| |
| LogSoftmaxDescriptor logSoftmaxDesc; |
| logSoftmaxDesc.m_Beta = beta; |
| logSoftmaxDesc.m_Axis = axis; |
| |
| IConnectableLayer* logSoftmax = net->AddLogSoftmaxLayer(logSoftmaxDesc, "Log_Softmax"); |
| IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| Connect(input, logSoftmax, inputTensorInfo, 0, 0); |
| |
| TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| Connect(logSoftmax, output, outputTensorInfo, 0, 0); |
| |
| return net; |
| } |
| |
| void LogSoftmaxEndToEnd(const std::vector<armnn::BackendId>& backends, |
| armnn::TensorInfo& inputTensorInfo, |
| armnn::TensorInfo& outputTensorInfo, |
| std::vector<float>& inputData, |
| std::vector<float>& expectedOutputData, |
| const float beta, |
| const int axis) |
| { |
| using namespace armnn; |
| |
| // Builds up the structure of the network |
| INetworkPtr net = CreateLogSoftmaxNetwork<DataType::Float32>(inputTensorInfo.GetShape(), |
| outputTensorInfo.GetShape(), |
| beta, |
| axis); |
| |
| CHECK(net); |
| |
| std::map<int, std::vector<float>> inputTensorData = { {0, inputData} }; |
| std::map<int, std::vector<float>> expectedOutputTensorData = { {0, expectedOutputData} }; |
| |
| EndToEndLayerTestImpl<DataType::Float32, DataType::Float32>(std::move(net), |
| inputTensorData, |
| expectedOutputTensorData, |
| backends); |
| } |
| |
| } // anonymous namespace |
| |
| void LogSoftmaxEndToEndTest(const std::vector<armnn::BackendId>& defaultBackends) |
| { |
| using namespace armnn; |
| |
| const float beta = 10.0f; // non-default beta |
| const int axis = 3; // positive axis |
| |
| const TensorShape inputShape{1, 1, 2, 4}; |
| TensorInfo inputTensorInfo(inputShape, DataType::Float32); |
| |
| const TensorShape outputShape{1, 1, 2, 4}; |
| TensorInfo outputTensorInfo(outputShape, DataType::Float32); |
| |
| std::vector<float> inputData = std::vector<float>({ |
| 0.0f, -0.6f, 0.2f, 0.4f, |
| 0.3f, -0.2f, 1.0f, 0.1f |
| }); |
| |
| std::vector<float> expectedOutputData = std::vector<float>({ |
| -4.14297f, -10.14297f, -2.14297f, -0.14297f, |
| -7.00104f, -12.00104f, -0.00104087f, -9.00104f |
| }); |
| |
| LogSoftmaxEndToEnd(defaultBackends, |
| inputTensorInfo, |
| outputTensorInfo, |
| inputData, |
| expectedOutputData, |
| beta, |
| axis); |
| } |