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Diffstat (limited to 'src/backends/backendsCommon/test')
-rw-r--r-- | src/backends/backendsCommon/test/ArithmeticTestImpl.hpp | 105 |
1 files changed, 105 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp b/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp new file mode 100644 index 0000000000..f70bf48ca9 --- /dev/null +++ b/src/backends/backendsCommon/test/ArithmeticTestImpl.hpp @@ -0,0 +1,105 @@ +// +// Copyright © 2017 Arm Ltd. All rights reserved. +// SPDX-License-Identifier: MIT +// +#pragma once + +#include <armnn/INetwork.hpp> + +#include <backendsCommon/test/CommonTestUtils.hpp> + +#include <boost/test/unit_test.hpp> + +#include <vector> + +namespace +{ + +template<typename armnn::DataType DataType> +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], DataType, qScale, qOffset); + IConnectableLayer* input = net->AddInputLayer(boost::numeric_cast<LayerBindingId>(i)); + Connect(input, arithmeticLayer, inputTensorInfo, 0, i); + } + + TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); + IConnectableLayer* output = net->AddOutputLayer(0, "output"); + Connect(arithmeticLayer, output, outputTensorInfo, 0, 0); + + return net; +} + +template<typename T> +void ArithmeticSimpleEndToEnd(const std::vector<BackendId>& backends, + const LayerType type, + const std::vector<T> 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<GetDataType<T>()>(inputShapes, outputShape, type); + + BOOST_TEST_CHECKPOINT("create a network"); + + const std::vector<T> input0({ 1, 1, 1, 1, 5, 5, 5, 5, + 3, 3, 3, 3, 4, 4, 4, 4 }); + + const std::vector<T> input1({ 1, 1, 1, 1, 3, 3, 3, 3, + 5, 5, 5, 5, 4, 4, 4, 4 }); + + std::map<int, std::vector<T>> inputTensorData = {{ 0, input0 }, { 1, input1 }}; + std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }}; + + EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends); +} + +template<typename T> +void ArithmeticBroadcastEndToEnd(const std::vector<BackendId>& backends, + const LayerType type, + const std::vector<T> 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<GetDataType<T>()>(inputShapes, outputShape, type); + + BOOST_TEST_CHECKPOINT("create a network"); + + const std::vector<T> input0({ 1, 2, 3, 1, 0, 6, + 7, 8, 9, 10, 11, 12 }); + + const std::vector<T> input1({ 1, 1, 3 }); + + std::map<int, std::vector<T>> inputTensorData = {{ 0, input0 }, { 1, input1 }}; + std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput }}; + + EndToEndLayerTestImpl<T>(move(net), inputTensorData, expectedOutputData, backends); +} + +} // anonymous namespace |