Teresa Charlin | c17a35f | 2023-01-12 14:13:09 +0000 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2023 Arm Ltd and Contributors. All rights reserved. |
| 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <armnn/INetwork.hpp> |
| 8 | |
| 9 | #include <CommonTestUtils.hpp> |
| 10 | #include <ResolveType.hpp> |
| 11 | |
| 12 | #include <doctest/doctest.h> |
| 13 | |
| 14 | namespace |
| 15 | { |
| 16 | |
| 17 | template<typename armnn::DataType DataType> |
| 18 | armnn::INetworkPtr CreateReduceNetwork(const armnn::TensorShape& inputShape, |
| 19 | const armnn::TensorShape& outputShape, |
| 20 | const armnn::ReduceDescriptor& descriptor, |
| 21 | const float qScale = 1.0f, |
| 22 | const int32_t qOffset = 0) |
| 23 | { |
| 24 | using namespace armnn; |
| 25 | |
| 26 | INetworkPtr network(INetwork::Create()); |
| 27 | |
| 28 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| 29 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset); |
| 30 | |
| 31 | |
| 32 | IConnectableLayer* reduce = network->AddReduceLayer(descriptor, "reduce"); |
| 33 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 34 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 35 | |
| 36 | Connect(input, reduce, inputTensorInfo, 0, 0); |
| 37 | Connect(reduce, output, outputTensorInfo, 0, 0); |
| 38 | |
| 39 | return network; |
| 40 | } |
| 41 | |
| 42 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 43 | void ReduceEndToEnd(const std::vector<armnn::BackendId>& backends) |
| 44 | { |
| 45 | using namespace armnn; |
| 46 | |
| 47 | const TensorShape& inputShape = { 1, 1, 1, 5 }; |
| 48 | const TensorShape& outputShape = { 1, 1, 1 }; |
| 49 | |
| 50 | ReduceDescriptor descriptor; |
| 51 | descriptor.m_KeepDims = false; |
| 52 | descriptor.m_vAxis = { 3 }; |
| 53 | descriptor.m_ReduceOperation = ReduceOperation::Sum; |
| 54 | |
| 55 | INetworkPtr network = CreateReduceNetwork<ArmnnType>(inputShape, outputShape, descriptor); |
| 56 | |
| 57 | CHECK(network); |
| 58 | |
| 59 | std::vector<float> floatInputData({ 5.0f, 2.0f, 8.0f, 10.0f, 9.0f }); |
| 60 | std::vector<float> floatOutputData({ 34.0f }); |
| 61 | |
| 62 | std::vector<T> inputData = armnnUtils::QuantizedVector<T>(floatInputData); |
| 63 | std::vector<T> outputData = armnnUtils::QuantizedVector<T>(floatOutputData); |
| 64 | |
| 65 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 66 | std::map<int, std::vector<T>> expectedOutputData = { { 0, outputData } }; |
| 67 | |
| 68 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| 69 | } |
| 70 | } // anonymous namespace |