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Diffstat (limited to 'src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp')
| -rw-r--r-- | src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp | 172 |
1 files changed, 172 insertions, 0 deletions
diff --git a/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp b/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp new file mode 100644 index 0000000000..addd27caca --- /dev/null +++ b/src/backends/backendsCommon/test/Pooling2dEndToEndTestImpl.hpp @@ -0,0 +1,172 @@ +// +// 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 , 3.5 , 4 , + 4.5, 5 , 5.5, + 6 , 6.5, 7 }; + } + else + { + expectedOutput = { 1.33333, 2.33333, 1.77778, + 3 , 5 , 3.66667, + 2.66667, 4.33333, 3.11111 }; + } + + 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 |