Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 1 | // |
Tianle Cheng | fa62fdc | 2023-12-15 12:38:40 +0000 | [diff] [blame] | 2 | // Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved. |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <armnn/INetwork.hpp> |
| 8 | #include <armnn/Types.hpp> |
| 9 | |
| 10 | #include <CommonTestUtils.hpp> |
| 11 | #include <ResolveType.hpp> |
| 12 | |
| 13 | #include <doctest/doctest.h> |
| 14 | |
| 15 | namespace |
| 16 | { |
| 17 | |
| 18 | using namespace armnn; |
| 19 | |
| 20 | template<typename armnn::DataType DataType> |
| 21 | armnn::INetworkPtr CreatePooling2dNetwork(const armnn::TensorShape& inputShape, |
| 22 | const armnn::TensorShape& outputShape, |
| 23 | PaddingMethod padMethod = PaddingMethod::Exclude, |
| 24 | PoolingAlgorithm poolAlg = PoolingAlgorithm::Max, |
| 25 | const float qScale = 1.0f, |
| 26 | const int32_t qOffset = 0) |
| 27 | { |
| 28 | INetworkPtr network(INetwork::Create()); |
| 29 | |
| 30 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| 31 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset, true); |
| 32 | |
| 33 | Pooling2dDescriptor descriptor; |
| 34 | descriptor.m_PoolType = poolAlg; |
| 35 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 36 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 37 | descriptor.m_PadLeft = 1; |
| 38 | descriptor.m_PadRight = 1; |
| 39 | descriptor.m_PadTop = 1; |
| 40 | descriptor.m_PadBottom = 1; |
| 41 | descriptor.m_PaddingMethod = padMethod; |
| 42 | descriptor.m_DataLayout = DataLayout::NHWC; |
| 43 | |
| 44 | IConnectableLayer* pool = network->AddPooling2dLayer(descriptor, "pool"); |
| 45 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 46 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 47 | |
| 48 | Connect(input, pool, inputTensorInfo, 0, 0); |
| 49 | Connect(pool, output, outputTensorInfo, 0, 0); |
| 50 | |
| 51 | return network; |
| 52 | } |
| 53 | |
| 54 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 55 | void MaxPool2dEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 56 | PaddingMethod padMethod = PaddingMethod::Exclude) |
| 57 | { |
| 58 | const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| 59 | const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| 60 | |
| 61 | INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod); |
| 62 | |
| 63 | CHECK(network); |
| 64 | |
| 65 | std::vector<T> inputData{ 1, 2, 3, |
| 66 | 4, 5, 6, |
| 67 | 7, 8, 9 }; |
| 68 | std::vector<T> expectedOutput{ 5, 6, 6, |
| 69 | 8, 9, 9, |
| 70 | 8, 9, 9 }; |
| 71 | |
| 72 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 73 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| 74 | |
| 75 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| 76 | } |
| 77 | |
| 78 | template<armnn::DataType ArmnnType> |
Teresa Charlin | a52bca2 | 2024-02-01 17:36:48 +0000 | [diff] [blame^] | 79 | void MaxPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends, |
| 80 | PaddingMethod padMethod = PaddingMethod::Exclude) |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 81 | { |
| 82 | using namespace half_float::literal; |
| 83 | using Half = half_float::half; |
| 84 | |
| 85 | const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| 86 | const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| 87 | |
Teresa Charlin | a52bca2 | 2024-02-01 17:36:48 +0000 | [diff] [blame^] | 88 | INetworkPtr network = CreatePooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod); |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 89 | CHECK(network); |
| 90 | |
| 91 | std::vector<Half> inputData{ 1._h, 2._h, 3._h, |
| 92 | 4._h, 5._h, 6._h, |
| 93 | 7._h, 8._h, 9._h }; |
| 94 | std::vector<Half> expectedOutput{ 5._h, 6._h, 6._h, |
| 95 | 8._h, 9._h, 9._h, |
| 96 | 8._h, 9._h, 9._h }; |
| 97 | |
| 98 | std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } }; |
| 99 | std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } }; |
| 100 | |
| 101 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| 102 | } |
| 103 | |
| 104 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 105 | void AvgPool2dEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 106 | PaddingMethod padMethod = PaddingMethod::Exclude) |
| 107 | { |
| 108 | const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| 109 | const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| 110 | |
| 111 | INetworkPtr network = CreatePooling2dNetwork<ArmnnType>( |
| 112 | inputShape, outputShape, padMethod, PoolingAlgorithm::Average); |
| 113 | CHECK(network); |
| 114 | |
| 115 | std::vector<T> inputData{ 1, 2, 3, |
| 116 | 4, 5, 6, |
| 117 | 7, 8, 9 }; |
| 118 | std::vector<T> expectedOutput; |
| 119 | if (padMethod == PaddingMethod::Exclude) |
| 120 | { |
Teresa Charlin | 3fbad94 | 2022-12-15 10:35:37 +0000 | [diff] [blame] | 121 | expectedOutput = { 3.f , 3.5f, 4.f , |
| 122 | 4.5f, 5.f , 5.5f, |
| 123 | 6.f , 6.5f, 7.f }; |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 124 | } |
| 125 | else |
| 126 | { |
Teresa Charlin | 3fbad94 | 2022-12-15 10:35:37 +0000 | [diff] [blame] | 127 | expectedOutput = { 1.33333f, 2.33333f, 1.77778f, |
| 128 | 3.f , 5.f , 3.66667f, |
| 129 | 2.66667f, 4.33333f, 3.11111f }; |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 130 | } |
| 131 | |
| 132 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 133 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| 134 | |
| 135 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| 136 | inputTensorData, |
| 137 | expectedOutputData, |
| 138 | backends, |
| 139 | 0.00001f); |
| 140 | } |
| 141 | |
| 142 | template<armnn::DataType ArmnnType> |
| 143 | void AvgPool2dEndToEndFloat16(const std::vector<armnn::BackendId>& backends, |
Teresa Charlin | a52bca2 | 2024-02-01 17:36:48 +0000 | [diff] [blame^] | 144 | PaddingMethod padMethod = PaddingMethod::Exclude) |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 145 | { |
| 146 | using namespace half_float::literal; |
| 147 | using Half = half_float::half; |
| 148 | |
| 149 | const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| 150 | const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| 151 | |
| 152 | INetworkPtr network = CreatePooling2dNetwork<ArmnnType>( |
| 153 | inputShape, outputShape, padMethod, PoolingAlgorithm::Average); |
| 154 | CHECK(network); |
| 155 | |
| 156 | std::vector<Half> inputData{ 1._h, 2._h, 3._h, |
| 157 | 4._h, 5._h, 6._h, |
| 158 | 7._h, 8._h, 9._h }; |
Teresa Charlin | a52bca2 | 2024-02-01 17:36:48 +0000 | [diff] [blame^] | 159 | std::vector<Half> expectedOutput; |
| 160 | if (padMethod == PaddingMethod::Exclude) |
| 161 | { |
| 162 | expectedOutput = { 3._h , 3.5_h, 4._h , |
| 163 | 4.5_h, 5._h , 5.5_h, |
| 164 | 6._h , 6.5_h, 7._h }; |
| 165 | } |
| 166 | else |
| 167 | { |
| 168 | expectedOutput = { 1.33333_h, 2.33333_h, 1.77778_h, |
| 169 | 3._h , 5._h , 3.66667_h, |
| 170 | 2.66667_h, 4.33333_h, 3.11111_h }; |
| 171 | } |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 172 | |
| 173 | std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } }; |
| 174 | std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput } }; |
| 175 | |
| 176 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), |
| 177 | inputTensorData, |
| 178 | expectedOutputData, |
| 179 | backends, |
| 180 | 0.00001f); |
| 181 | } |
| 182 | |
Tianle Cheng | fa62fdc | 2023-12-15 12:38:40 +0000 | [diff] [blame] | 183 | template<typename armnn::DataType DataType> |
| 184 | armnn::INetworkPtr CreateTwoLayerPooling2dNetwork(const armnn::TensorShape& inputShape, |
| 185 | const armnn::TensorShape& outputShape, |
| 186 | PaddingMethod padMethod = PaddingMethod::Exclude, |
| 187 | PoolingAlgorithm poolAlg = PoolingAlgorithm::Max, |
| 188 | const float qScale = 1.0f, |
| 189 | const int32_t qOffset = 0) |
| 190 | { |
| 191 | INetworkPtr network(INetwork::Create()); |
| 192 | |
| 193 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| 194 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset, true); |
| 195 | |
| 196 | Pooling2dDescriptor descriptor; |
| 197 | descriptor.m_PoolType = poolAlg; |
| 198 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 199 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 200 | descriptor.m_PadLeft = 1; |
| 201 | descriptor.m_PadRight = 1; |
| 202 | descriptor.m_PadTop = 1; |
| 203 | descriptor.m_PadBottom = 1; |
| 204 | descriptor.m_PaddingMethod = padMethod; |
| 205 | descriptor.m_DataLayout = DataLayout::NHWC; |
| 206 | |
| 207 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 208 | IConnectableLayer* pool1 = network->AddPooling2dLayer(descriptor, "pool_1"); |
| 209 | IConnectableLayer* pool2 = network->AddPooling2dLayer(descriptor, "pool_2"); |
| 210 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 211 | |
| 212 | Connect(input, pool1, inputTensorInfo, 0, 0); |
| 213 | Connect(pool1, pool2, inputTensorInfo, 0, 0); |
| 214 | Connect(pool2, output, outputTensorInfo, 0, 0); |
| 215 | |
| 216 | return network; |
| 217 | } |
| 218 | |
| 219 | template<typename armnn::DataType DataType> |
| 220 | armnn::INetworkPtr CreateThreeLayerPooling2dNetwork(const armnn::TensorShape& inputShape, |
| 221 | const armnn::TensorShape& outputShape, |
| 222 | PaddingMethod padMethod = PaddingMethod::Exclude, |
| 223 | PoolingAlgorithm poolAlg = PoolingAlgorithm::Max, |
| 224 | const float qScale = 1.0f, |
| 225 | const int32_t qOffset = 0) |
| 226 | { |
| 227 | INetworkPtr network(INetwork::Create()); |
| 228 | |
| 229 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
| 230 | TensorInfo outputTensorInfo(outputShape, DataType, qScale, qOffset, true); |
| 231 | |
| 232 | Pooling2dDescriptor descriptor; |
| 233 | descriptor.m_PoolType = poolAlg; |
| 234 | descriptor.m_PoolWidth = descriptor.m_PoolHeight = 3; |
| 235 | descriptor.m_StrideX = descriptor.m_StrideY = 1; |
| 236 | descriptor.m_PadLeft = 1; |
| 237 | descriptor.m_PadRight = 1; |
| 238 | descriptor.m_PadTop = 1; |
| 239 | descriptor.m_PadBottom = 1; |
| 240 | descriptor.m_PaddingMethod = padMethod; |
| 241 | descriptor.m_DataLayout = DataLayout::NHWC; |
| 242 | |
| 243 | IConnectableLayer* input = network->AddInputLayer(0, "input"); |
| 244 | IConnectableLayer* pool1 = network->AddPooling2dLayer(descriptor, "pool_1"); |
| 245 | IConnectableLayer* pool2 = network->AddPooling2dLayer(descriptor, "pool_2"); |
| 246 | IConnectableLayer* pool3 = network->AddPooling2dLayer(descriptor, "pool_3"); |
| 247 | IConnectableLayer* output = network->AddOutputLayer(0, "output"); |
| 248 | |
| 249 | Connect(input, pool1, inputTensorInfo, 0, 0); |
| 250 | Connect(pool1, pool2, inputTensorInfo, 0, 0); |
| 251 | Connect(pool2, pool3, inputTensorInfo, 0, 0); |
| 252 | Connect(pool3, output, outputTensorInfo, 0, 0); |
| 253 | |
| 254 | return network; |
| 255 | } |
| 256 | |
| 257 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 258 | void MaxPool2dTwoLayerEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 259 | PaddingMethod padMethod = PaddingMethod::Exclude) |
| 260 | { |
| 261 | const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| 262 | const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| 263 | |
| 264 | INetworkPtr network = CreateTwoLayerPooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod); |
| 265 | |
| 266 | CHECK(network); |
| 267 | |
| 268 | std::vector<T> inputData{ 1, 2, 3, |
| 269 | 4, 5, 6, |
| 270 | 7, 8, 9 }; |
| 271 | std::vector<T> expectedOutput{ 9, 9, 9, |
| 272 | 9, 9, 9, |
| 273 | 9, 9, 9 }; |
| 274 | |
| 275 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 276 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| 277 | |
| 278 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| 279 | } |
| 280 | |
| 281 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
| 282 | void MaxPool2dThreeLayerEndToEnd(const std::vector<armnn::BackendId>& backends, |
| 283 | PaddingMethod padMethod = PaddingMethod::Exclude) |
| 284 | { |
| 285 | const TensorShape& inputShape = { 1, 3, 3, 1 }; |
| 286 | const TensorShape& outputShape = { 1, 3, 3, 1 }; |
| 287 | |
| 288 | INetworkPtr network = CreateThreeLayerPooling2dNetwork<ArmnnType>(inputShape, outputShape, padMethod); |
| 289 | |
| 290 | CHECK(network); |
| 291 | |
| 292 | std::vector<T> inputData{ 1, 2, 3, |
| 293 | 4, 5, 6, |
| 294 | 7, 8, 9 }; |
| 295 | std::vector<T> expectedOutput{ 9, 9, 9, |
| 296 | 9, 9, 9, |
| 297 | 9, 9, 9 }; |
| 298 | |
| 299 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 300 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput } }; |
| 301 | |
| 302 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(network), inputTensorData, expectedOutputData, backends); |
| 303 | } |
| 304 | |
Cathal Corbett | bd18eab | 2022-11-15 12:56:16 +0000 | [diff] [blame] | 305 | } // anonymous namespace |