| // |
| // Copyright © 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 CreateReduceNetwork(const armnn::TensorShape& inputShape, |
| const armnn::TensorShape& outputShape, |
| const armnn::ReduceDescriptor& descriptor, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| using namespace armnn; |
| |
| INetworkPtr network(INetwork::Create()); |
| |
| TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| |
| |
| IConnectableLayer* reduce = network->AddReduceLayer(descriptor, "reduce"); |
| IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| |
| Connect(input, reduce, inputTensorInfo, 0, 0); |
| Connect(reduce, output, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void ReduceEndToEnd(const std::vector<armnn::BackendId>& backends) |
| { |
| using namespace armnn; |
| |
| const TensorShape& inputShape = { 1, 1, 1, 5 }; |
| const TensorShape& outputShape = { 1, 1, 1 }; |
| |
| ReduceDescriptor descriptor; |
| descriptor.m_KeepDims = false; |
| descriptor.m_vAxis = { 3 }; |
| descriptor.m_ReduceOperation = ReduceOperation::Sum; |
| |
| INetworkPtr network = CreateReduceNetwork<ArmnnType>(inputShape, outputShape, descriptor); |
| |
| CHECK(network); |
| |
| std::vector<float> floatInputData({ 5.0f, 2.0f, 8.0f, 10.0f, 9.0f }); |
| std::vector<float> floatOutputData({ 34.0f }); |
| |
| std::vector<T> inputData = armnnUtils::QuantizedVector<T>(floatInputData); |
| std::vector<T> outputData = armnnUtils::QuantizedVector<T>(floatOutputData); |
| |
| std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| std::map<int, std::vector<T>> expectedOutputData = { { 0, outputData } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| } |
| } // anonymous namespace |