| // |
| // Copyright © 2020 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include "EndToEndTestImpl.hpp" |
| |
| #include <armnn/INetwork.hpp> |
| #include <armnn/TypesUtils.hpp> |
| #include <backendsCommon/test/CommonTestUtils.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <boost/test/unit_test_log.hpp> |
| |
| namespace |
| { |
| |
| /** Defines the acceptable tolerance of ActivationFunction-DataType combinations. |
| * |
| * @param activationFunction The activation function used |
| * @param dataType Data type used |
| * |
| * @return Tolerance depending on the activation function and data type |
| */ |
| float GetActivationTolerance(const armnn::ActivationFunction& activationFunction, DataType dataType) |
| { |
| constexpr float defaultTolerance = 1e-6f; |
| |
| switch (activationFunction) |
| { |
| // The following values are taken from ArmComputeLibrary/tests/validation/CL/ActivationLayer.cpp |
| case ActivationFunction::Elu: |
| return (dataType == DataType::Float16 ? 0.01f : 0.00001f); |
| case ActivationFunction::HardSwish: |
| return (dataType == DataType::Float16 ? 0.01f : defaultTolerance); |
| default: |
| return defaultTolerance; |
| } |
| } |
| |
| /** Creates a network with one layer of the activation function specified in the activation descriptor. |
| * |
| * @param inputInfo Tensor info of inputs |
| * @param outputInfo Tensor info of outputs |
| * @param descriptor Activation descriptor |
| * |
| * @return INetworkPtr A pointer to the created network |
| */ |
| armnn::INetworkPtr CreateActivationNetwork(const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& outputInfo, |
| const armnn::ActivationDescriptor& descriptor) |
| { |
| using namespace armnn; |
| |
| char const* ActivationName = GetActivationFunctionAsCString(descriptor.m_Function); |
| |
| INetworkPtr net(INetwork::Create()); |
| |
| IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| IConnectableLayer* prelu = net->AddActivationLayer(descriptor, ActivationName); |
| IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| |
| Connect(input, prelu, inputInfo, 0, 0); |
| Connect(prelu, output, outputInfo, 0, 0); |
| |
| return net; |
| } |
| |
| /** Specifies the implementation of end to end tests for activation functions. |
| * |
| * - Converts input data and expected-output data to the data type that is desired for the test (ArmnnType) |
| * - Creates a network with one layer of the activation function specified in the activation descriptor. |
| * - Executes the network on specified backends and compares results to expected output values |
| * |
| * @tparam ArmnnType The armnn data type for the input and expected-output data |
| * @param backends Backends to run test on |
| * @param floatInputData Input data given as vector of float |
| * @param floatExpectedOutputData Expected output data given as vector of float |
| * @param inputInfo Tensor info of inputs |
| * @param outputInfo Tensor info of outputs |
| * @param descriptor Activation descriptor |
| */ |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void ActivationEndToEndImpl(const std::vector<armnn::BackendId>& backends, |
| const std::vector<float>& floatInputData, |
| const std::vector<float>& floatExpectedOutputData, |
| const armnn::TensorInfo& inputInfo, |
| const armnn::TensorInfo& outputInfo, |
| const armnn::ActivationDescriptor& descriptor) |
| { |
| using namespace armnn; |
| |
| // Selectively quantizes/transforms float values to the needed data type |
| std::vector<T> inputData = armnnUtils::QuantizedVector<T>( floatInputData, |
| inputInfo.GetQuantizationScale(), |
| inputInfo.GetQuantizationOffset()); |
| std::vector<T> expectedOutputData = armnnUtils::QuantizedVector<T>( floatExpectedOutputData, |
| outputInfo.GetQuantizationScale(), |
| outputInfo.GetQuantizationOffset()); |
| |
| INetworkPtr net = CreateActivationNetwork(inputInfo, outputInfo, descriptor); |
| |
| std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| std::map<int, std::vector<T>> expectedOutputTensorData = { { 0, expectedOutputData } }; |
| |
| float tolerance = GetActivationTolerance(descriptor.m_Function, ArmnnType); |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(move(net), |
| inputTensorData, |
| expectedOutputTensorData, |
| backends, |
| tolerance); |
| } |
| |
| /** Executes an end to end test for Elu activation with specific input and expected-output data |
| * |
| * @tparam ArmnnType The armnn data type for the input and expected-output data |
| * @param backends The backends on which to run the test |
| */ |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void EluEndToEndTest(const std::vector<BackendId>& backends) |
| { |
| std::vector<float> floatInputData{ -2.0f, -1.0f, -0.0f, 0.0f, |
| 1.0f, 2.0f, 3.0f, 4.0f }; |
| |
| std::vector<float> floatExpectedOutputData{ -0.86466471676f, -0.63212055882f, -0.0f, 0.0f, |
| 1.0f , 2.0f , 3.0f, 4.0f }; |
| |
| float qScale = 1.0f; |
| int32_t qOffset = 0; |
| armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); |
| armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); |
| |
| armnn::ActivationDescriptor descriptor(ActivationFunction::Elu, 1.0); |
| |
| ActivationEndToEndImpl<ArmnnType>(backends, |
| floatInputData, |
| floatExpectedOutputData, |
| inputInfo, |
| outputInfo, |
| descriptor); |
| } |
| |
| /** Executes an end to end test for HardSwish activation with specific input and expected-output data |
| * |
| * @tparam ArmnnType The armnn data type for the input and expected-output data |
| * @param backends The backends on which to run the test |
| */ |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void HardSwishEndToEndTest(const std::vector<BackendId>& backends) |
| { |
| std::vector<float> floatInputData{ -2.0f, -1.0f, -0.5f, 0.0f, |
| 1.0f, 2.0f, 3.0f, 4.0f }; |
| |
| std::vector<float> floatExpectedOutputData{ -0.33333333333f, -0.33333333333f, -0.208333f, 0.0f, |
| 0.66666666667f, 1.66666666667f, 3.0f , 4.0f }; |
| |
| float qScale = 1.0f; |
| int32_t qOffset = 0; |
| armnn::TensorInfo inputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); |
| armnn::TensorInfo outputInfo({ 2, 2, 2, 1 }, ArmnnType, qScale, qOffset); |
| |
| armnn::ActivationDescriptor descriptor(ActivationFunction::HardSwish, 1.0); |
| |
| ActivationEndToEndImpl<ArmnnType>(backends, |
| floatInputData, |
| floatExpectedOutputData, |
| inputInfo, |
| outputInfo, |
| descriptor); |
| } |
| |
| } // anonymous namespace |