| // |
| // Copyright © 2022 Arm Ltd and Contributors. All rights reserved. |
| // SPDX-License-Identifier: MIT |
| // |
| #pragma once |
| |
| #include <armnn/INetwork.hpp> |
| #include <armnn/Types.hpp> |
| |
| #include <CommonTestUtils.hpp> |
| #include <ResolveType.hpp> |
| |
| #include <doctest/doctest.h> |
| |
| namespace |
| { |
| |
| using namespace armnn; |
| |
| template<typename armnn::DataType DataType> |
| armnn::INetworkPtr CreatePooling2dNetwork(const armnn::TensorShape& inputShape, |
| const armnn::TensorShape& outputShape, |
| PaddingMethod padMethod = PaddingMethod::Exclude, |
| PoolingAlgorithm poolAlg = PoolingAlgorithm::Max, |
| const float qScale = 1.0f, |
| const int32_t qOffset = 0) |
| { |
| INetworkPtr network(INetwork::Create()); |
| |
| TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset, true); |
| |
| Pooling2dDescriptor descriptor; |
| descriptor.m_PoolType = poolAlg; |
| descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| descriptor.m_PadLeft = 1; |
| descriptor.m_PadRight = 1; |
| descriptor.m_PadTop = 1; |
| descriptor.m_PadBottom = 1; |
| descriptor.m_PaddingMethod = padMethod; |
| descriptor.m_DataLayout = DataLayout::NHWC; |
| |
| IConnectableLayer* pool = network->AddPooling2dLayer(descriptor, "pool"); |
| IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| |
| Connect(input, pool, inputTensorInfo, 0, 0); |
| Connect(pool, output, outputTensorInfo, 0, 0); |
| |
| return network; |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void MaxPool2dEndToEnd(const std::vector<armnn::BackendId>& backends, |
| PaddingMethod padMethod = PaddingMethod::Exclude) |
| { |
| const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| |
| INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod); |
| |
| CHECK(network); |
| |
| std::vector<T> inputData{ 1, 2, 3, |
| 4, 5, 6, |
| 7, 8, 9 }; |
| std::vector<T> expectedOutput{ 5, 6, 6, |
| 8, 9, 9, |
| 8, 9, 9 }; |
| |
| std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| } |
| |
| template<armnn::DataType ArmnnType> |
| void MaxPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends) |
| { |
| using namespace half_float::literal; |
| using Half = half_float::half; |
| |
| const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| |
| INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape); |
| CHECK(network); |
| |
| std::vector<Half> inputData{ 1._h, 2._h, 3._h, |
| 4._h, 5._h, 6._h, |
| 7._h, 8._h, 9._h }; |
| std::vector<Half> expectedOutput{ 5._h, 6._h, 6._h, |
| 8._h, 9._h, 9._h, |
| 8._h, 9._h, 9._h }; |
| |
| std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } }; |
| std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| } |
| |
| template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| void AvgPool2dEndToEnd(const std::vector<armnn::BackendId>& backends, |
| PaddingMethod padMethod = PaddingMethod::Exclude) |
| { |
| const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| |
| INetworkPtr network = CreatePooling2dNetwork<ArmnnType>( |
| inputShape, outputShape, padMethod, PoolingAlgorithm::Average); |
| CHECK(network); |
| |
| std::vector<T> inputData{ 1, 2, 3, |
| 4, 5, 6, |
| 7, 8, 9 }; |
| std::vector<T> expectedOutput; |
| if (padMethod == PaddingMethod::Exclude) |
| { |
| expectedOutput = { 3.f , 3.5f, 4.f , |
| 4.5f, 5.f , 5.5f, |
| 6.f , 6.5f, 7.f }; |
| } |
| else |
| { |
| expectedOutput = { 1.33333f, 2.33333f, 1.77778f, |
| 3.f , 5.f , 3.66667f, |
| 2.66667f, 4.33333f, 3.11111f }; |
| } |
| |
| std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| inputTensorData, |
| expectedOutputData, |
| backends, |
| 0.00001f); |
| } |
| |
| template<armnn::DataType ArmnnType> |
| void AvgPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends, |
| PaddingMethod padMethod = PaddingMethod::IgnoreValue) |
| { |
| using namespace half_float::literal; |
| using Half = half_float::half; |
| |
| const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| |
| INetworkPtr network = CreatePooling2dNetwork<ArmnnType>( |
| inputShape, outputShape, padMethod, PoolingAlgorithm::Average); |
| CHECK(network); |
| |
| std::vector<Half> inputData{ 1._h, 2._h, 3._h, |
| 4._h, 5._h, 6._h, |
| 7._h, 8._h, 9._h }; |
| std::vector<Half> expectedOutput{ 1.33333_h, 2.33333_h, 1.77778_h, |
| 3._h , 5._h , 3.66667_h, |
| 2.66667_h, 4.33333_h, 3.11111_h }; |
| |
| std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } }; |
| std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } }; |
| |
| EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| inputTensorData, |
| expectedOutputData, |
| backends, |
| 0.00001f); |
| } |
| |
| } // anonymous namespace |