| // |
| // Copyright © 2017 Arm Ltd. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include "CommonTestUtils.hpp" |
| |
| #include <ResolveType.hpp> |
| |
| #include <armnn/INetwork.hpp> |
| |
| #include <boost/test/unit_test.hpp> |
| |
| #include <vector> |
| |
| namespace |
| { |
| |
| template<armnn::DataType ArmnnTypeInput, armnn::DataType ArmnnTypeOutput> |
| INetworkPtr CreateArithmeticNetwork(const std::vector<TensorShape>& inputShapes, |
| const TensorShape& outputShape, |
| const LayerType type, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| using namespace armnn; |
| |
| // Builds up the structure of the network. |
| INetworkPtr net(INetwork::Create()); |
| |
| IConnectableLayer* arithmeticLayer = nullptr; |
| |
| switch(type){ |
| case LayerType::Equal: arithmeticLayer = net->AddEqualLayer("equal"); break; |
| case LayerType::Greater: arithmeticLayer = net->AddGreaterLayer("greater"); break; |
| default: BOOST_TEST_FAIL("Non-Arithmetic layer type called."); |
| } |
| |
| for (unsigned int i = 0; i < inputShapes.size(); ++i) |
| { |
| TensorInfo inputTensorInfo(inputShapes[i], ArmnnTypeInput, qScale, qOffset); |
| IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i)); |
| Connect(input, arithmeticLayer, inputTensorInfo, 0, i); |
| } |
| |
| TensorInfo outputTensorInfo(outputShape, ArmnnTypeOutput, qScale, qOffset); |
| IConnectableLayer* output = net->AddOutputLayer(0, "output"); |
| Connect(arithmeticLayer, output, outputTensorInfo, 0, 0); |
| |
| return net; |
| } |
| |
| template<armnn::DataType ArmnnInputType, |
| armnn::DataType ArmnnOutputType, |
| typename TInput = armnn::ResolveType<ArmnnInputType>, |
| typename TOutput = armnn::ResolveType<ArmnnOutputType>> |
| void ArithmeticSimpleEndToEnd(const std::vector<BackendId>& backends, |
| const LayerType type, |
| const std::vector<TOutput> expectedOutput) |
| { |
| using namespace armnn; |
| |
| const std::vector<TensorShape> inputShapes{{ 2, 2, 2, 2 }, { 2, 2, 2, 2 }}; |
| const TensorShape& outputShape = { 2, 2, 2, 2 }; |
| |
| // Builds up the structure of the network |
| INetworkPtr net = CreateArithmeticNetwork<ArmnnInputType, ArmnnOutputType>(inputShapes, outputShape, type); |
| |
| BOOST_TEST_CHECKPOINT("create a network"); |
| |
| const std::vector<TInput> input0({ 1, 1, 1, 1, 5, 5, 5, 5, |
| 3, 3, 3, 3, 4, 4, 4, 4 }); |
| |
| const std::vector<TInput> input1({ 1, 1, 1, 1, 3, 3, 3, 3, |
| 5, 5, 5, 5, 4, 4, 4, 4 }); |
| |
| std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }}; |
| std::map<int, std::vector<TOutput>> expectedOutputData = {{ 0, expectedOutput }}; |
| |
| EndToEndLayerTestImpl<ArmnnInputType, ArmnnOutputType>(move(net), inputTensorData, expectedOutputData, backends); |
| } |
| |
| template<armnn::DataType ArmnnInputType, |
| armnn::DataType ArmnnOutputType, |
| typename TInput = armnn::ResolveType<ArmnnInputType>, |
| typename TOutput = armnn::ResolveType<ArmnnOutputType>> |
| void ArithmeticBroadcastEndToEnd(const std::vector<BackendId>& backends, |
| const LayerType type, |
| const std::vector<TOutput> expectedOutput) |
| { |
| using namespace armnn; |
| |
| const std::vector<TensorShape> inputShapes{{ 1, 2, 2, 3 }, { 1, 1, 1, 3 }}; |
| const TensorShape& outputShape = { 1, 2, 2, 3 }; |
| |
| // Builds up the structure of the network |
| INetworkPtr net = CreateArithmeticNetwork<ArmnnInputType, ArmnnOutputType>(inputShapes, outputShape, type); |
| |
| BOOST_TEST_CHECKPOINT("create a network"); |
| |
| const std::vector<TInput> input0({ 1, 2, 3, 1, 0, 6, |
| 7, 8, 9, 10, 11, 12 }); |
| |
| const std::vector<TInput> input1({ 1, 1, 3 }); |
| |
| std::map<int, std::vector<TInput>> inputTensorData = {{ 0, input0 }, { 1, input1 }}; |
| std::map<int, std::vector<TOutput>> expectedOutputData = {{ 0, expectedOutput }}; |
| |
| EndToEndLayerTestImpl<ArmnnInputType, ArmnnOutputType>(move(net), inputTensorData, expectedOutputData, backends); |
| } |
| |
| } // anonymous namespace |