| // |
| // Copyright © 2022-2023 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <armnn/INetwork.hpp> |
| |
| #include <CommonTestUtils.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| |
| template<typename armnn::DataType DataType> |
| armnn::INetworkPtr CreateSubtractionNetwork(const armnn::TensorShape& inputXShape, |
| const armnn::TensorShape& inputYShape, |
| const armnn::TensorShape& outputShape, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| using namespace armnn; |
| |
| INetworkPtr network(INetwork::Create()); |
| |
| TensorInfo inputXTensorInfo(inputXShape, DataType, qScale, qOffset, true); |
| TensorInfo inputYTensorInfo(inputYShape, DataType, qScale, qOffset, true); |
| |
| TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| |
| ARMNN_NO_DEPRECATE_WARN_BEGIN |
| IConnectableLayer* subtraction = network->AddSubtractionLayer("subtraction"); |
| ARMNN_NO_DEPRECATE_WARN_END |
| IConnectableLayer* inputX = network->AddInputLayer(0, "inputX"); |
| IConnectableLayer* inputY = network->AddInputLayer(1, "inputY"); |
| IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| |
| Connect(inputX, subtraction, inputXTensorInfo, 0, 0); |
| Connect(inputY, subtraction, inputYTensorInfo, 0, 1); |
| Connect(subtraction, output, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void SubtractionEndToEnd(const std::vector<armnn::BackendId>& backends) |
| { |
| using namespace armnn; |
| |
| const TensorShape& inputXShape = { 2, 2 }; |
| const TensorShape& inputYShape = { 2, 2 }; |
| const TensorShape& outputShape = { 2, 2 }; |
| |
| INetworkPtr network = CreateSubtractionNetwork<ArmnnType>(inputXShape, inputYShape, outputShape); |
| |
| CHECK(network); |
| |
| std::vector<T> inputXData{ 10, 11, 12, 13 }; |
| std::vector<T> inputYData{ 5, 7, 6, 8 }; |
| std::vector<T> expectedOutput{ 5, 4, 6, 5 }; |
| |
| std::map<int, std::vector<T>> inputTensorData = {{ 0, inputXData }, {1, inputYData}}; |
| std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| } |
| |
| template<armnn::DataType ArmnnType> |
| void SubtractionEndToEndFloat16(const std::vector<armnn::BackendId>& backends) |
| { |
| using namespace armnn; |
| using namespace half_float::literal; |
| using Half = half_float::half; |
| |
| const TensorShape& inputXShape = { 2, 2 }; |
| const TensorShape& inputYShape = { 2, 2 }; |
| const TensorShape& outputShape = { 2, 2 }; |
| |
| INetworkPtr network = CreateSubtractionNetwork<ArmnnType>(inputXShape, inputYShape, outputShape); |
| CHECK(network); |
| |
| std::vector<Half> inputXData{ 11._h, 12._h, |
| 13._h, 14._h }; |
| std::vector<Half> inputYData{ 5._h, 7._h, |
| 6._h, 8._h }; |
| std::vector<Half> expectedOutput{ 6._h, 5._h, |
| 7._h, 6._h }; |
| |
| std::map<int, std::vector<Half>> inputTensorData = {{ 0, inputXData }, { 1, inputYData }}; |
| std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| } |
| |
| } // anonymous namespace |