Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 1 | // |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 2 | // Copyright © 2019-2021,2023 Arm Ltd and Contributors. All rights reserved. |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
| 4 | // |
| 5 | #pragma once |
| 6 | |
| 7 | #include <ResolveType.hpp> |
| 8 | |
| 9 | #include <armnn/INetwork.hpp> |
| 10 | |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 11 | #include <armnn/utility/NumericCast.hpp> |
| 12 | |
Sadik Armagan | a097d2a | 2021-11-24 15:47:28 +0000 | [diff] [blame] | 13 | #include <CommonTestUtils.hpp> |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 14 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 15 | #include <doctest/doctest.h> |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 16 | |
| 17 | #include <vector> |
| 18 | |
| 19 | namespace |
| 20 | { |
| 21 | |
| 22 | template<typename armnn::DataType DataType> |
| 23 | INetworkPtr CreateSplitterNetwork(const TensorShape& inputShape, |
| 24 | const std::vector<TensorShape>& outputShapes, |
| 25 | unsigned int splitAxis, |
| 26 | unsigned int numSplit, |
| 27 | const float qScale = 1.0f, |
| 28 | const int32_t qOffset = 0) |
| 29 | { |
| 30 | using namespace armnn; |
| 31 | // Builds up the structure of the network. |
| 32 | INetworkPtr net(INetwork::Create()); |
| 33 | |
Cathal Corbett | 5b8093c | 2021-10-22 11:12:07 +0100 | [diff] [blame] | 34 | TensorInfo inputTensorInfo(inputShape, DataType, qScale, qOffset, true); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 35 | |
| 36 | std::vector<unsigned int> splitterDimSizes(inputShape.GetNumDimensions()); |
| 37 | |
| 38 | // Add current input shape to splitterDimSizes |
| 39 | for (unsigned int i = 0; i < inputShape.GetNumDimensions(); ++i) |
| 40 | { |
| 41 | splitterDimSizes[i] = inputTensorInfo.GetShape()[i]; |
| 42 | } |
| 43 | |
| 44 | if (splitterDimSizes[splitAxis] % numSplit != 0) |
| 45 | { |
| 46 | throw ParseException("Number of splits must evenly divide the dimension"); |
| 47 | } |
| 48 | splitterDimSizes[splitAxis] /= numSplit; |
| 49 | |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 50 | SplitterDescriptor splitDesc(numSplit, inputShape.GetNumDimensions()); |
Kevin May | 1bea6be | 2023-12-12 11:18:46 +0000 | [diff] [blame] | 51 | splitDesc.SetAxis(static_cast<int32_t>(splitAxis)); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 52 | for (unsigned int g = 0; g < numSplit; ++g) |
| 53 | { |
| 54 | // Set the size of the views. |
| 55 | for (unsigned int dimIdx = 0; dimIdx < splitterDimSizes.size(); ++dimIdx) |
| 56 | { |
| 57 | splitDesc.SetViewSize(g, dimIdx, splitterDimSizes[dimIdx]); |
| 58 | } |
| 59 | splitDesc.SetViewOriginCoord(g, splitAxis, splitterDimSizes[splitAxis] * g); |
| 60 | } |
| 61 | |
| 62 | IConnectableLayer* splitter = net->AddSplitterLayer(splitDesc, "splitter"); |
| 63 | IConnectableLayer* input = net->AddInputLayer(0, "input"); |
| 64 | Connect(input, splitter, inputTensorInfo, 0, 0); |
| 65 | |
| 66 | for (unsigned int i = 0; i < outputShapes.size(); ++i) |
| 67 | { |
| 68 | TensorInfo outputTensorInfo(outputShapes[i], DataType, qScale, qOffset); |
Matthew Sloyan | 171214c | 2020-09-09 09:07:37 +0100 | [diff] [blame] | 69 | IConnectableLayer* output = net->AddOutputLayer(armnn::numeric_cast<LayerBindingId>(i)); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 70 | Connect(splitter, output, outputTensorInfo, i, 0); |
| 71 | } |
| 72 | |
| 73 | return net; |
| 74 | } |
| 75 | |
| 76 | template<armnn::DataType ArmnnType> |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 77 | void Splitter1dEndToEnd(const std::vector<BackendId>& backends) |
| 78 | { |
| 79 | using namespace armnn; |
| 80 | using T = ResolveType<ArmnnType>; |
| 81 | |
| 82 | unsigned int splitAxis = 0; |
| 83 | unsigned int numSplit = 2; |
| 84 | const TensorShape& inputShape = { 4 }; |
| 85 | const std::vector<TensorShape> outputShapes{{ 2 }, { 2 }}; |
| 86 | |
| 87 | // Builds up the structure of the network |
| 88 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 89 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 90 | CHECK(net); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 91 | |
| 92 | // Creates structures for input & output. |
| 93 | std::vector<T> inputData{ 1, 2, 3, 4 }; |
| 94 | |
| 95 | std::vector<T> expectedOutput0{ 1, 2 }; |
| 96 | std::vector<T> expectedOutput1{ 3, 4 }; |
| 97 | |
| 98 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 99 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, {1, expectedOutput1} }; |
| 100 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 101 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 102 | } |
| 103 | |
| 104 | template<armnn::DataType ArmnnType> |
Kevin May | 1bea6be | 2023-12-12 11:18:46 +0000 | [diff] [blame] | 105 | void Splitter1dEndToEndFloat16(const std::vector<BackendId>& backends) |
| 106 | { |
| 107 | using namespace armnn; |
| 108 | using namespace half_float::literal; |
| 109 | using Half = half_float::half; |
| 110 | |
| 111 | unsigned int splitAxis = 0; |
| 112 | unsigned int numSplit = 2; |
| 113 | const TensorShape& inputShape = { 4 }; |
| 114 | const std::vector<TensorShape> outputShapes{{ 2 }, { 2 }}; |
| 115 | |
| 116 | // Builds up the structure of the network |
| 117 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 118 | |
| 119 | CHECK(net); |
| 120 | |
| 121 | // Creates structures for input & output. |
| 122 | std::vector<Half> inputData{ 1._h, 2._h, 3._h, 4._h }; |
| 123 | |
| 124 | std::vector<Half> expectedOutput0{ 1._h, 2._h }; |
| 125 | std::vector<Half> expectedOutput1{ 3._h, 4._h }; |
| 126 | |
| 127 | std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } }; |
| 128 | std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput0 }, {1, expectedOutput1} }; |
| 129 | |
| 130 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
| 131 | } |
| 132 | |
| 133 | template<armnn::DataType ArmnnType> |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 134 | void Splitter2dDim0EndToEnd(const std::vector<BackendId>& backends) |
| 135 | { |
| 136 | using namespace armnn; |
| 137 | using T = ResolveType<ArmnnType>; |
| 138 | |
| 139 | unsigned int splitAxis = 0; |
| 140 | unsigned int numSplit = 2; |
| 141 | const TensorShape& inputShape = { 4, 3 }; |
| 142 | const std::vector<TensorShape> outputShapes{{ 2, 3 }, { 2, 3 }}; |
| 143 | |
| 144 | // Builds up the structure of the network |
| 145 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 146 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 147 | CHECK(net); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 148 | |
| 149 | // Creates structures for input & output. |
| 150 | std::vector<T> inputData{ |
| 151 | 1, 2, |
| 152 | 3, 4, |
| 153 | 5, 6, |
| 154 | 7, 8, |
| 155 | 9, 10, |
| 156 | 11, 12 |
| 157 | }; |
| 158 | |
| 159 | std::vector<T> expectedOutput0{ 1, 2, 3, 4, 5, 6 }; |
| 160 | std::vector<T> expectedOutput1{ 7, 8, 9, 10, 11, 12 }; |
| 161 | |
| 162 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 163 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, {1, expectedOutput1} }; |
| 164 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 165 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 166 | } |
| 167 | |
| 168 | template<armnn::DataType ArmnnType> |
| 169 | void Splitter2dDim1EndToEnd(const std::vector<BackendId>& backends) |
| 170 | { |
| 171 | using namespace armnn; |
| 172 | using T = ResolveType<ArmnnType>; |
| 173 | |
| 174 | unsigned int splitAxis = 1; |
| 175 | unsigned int numSplit = 3; |
| 176 | const TensorShape& inputShape = { 4, 3 }; |
| 177 | const std::vector<TensorShape> outputShapes{{ 4, 1 }, { 4, 1 }, { 4, 1 }}; |
| 178 | |
| 179 | // Builds up the structure of the network |
| 180 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 181 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 182 | CHECK(net); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 183 | |
| 184 | // Creates structures for input & output. |
| 185 | std::vector<T> inputData{ |
| 186 | 1, 2, |
| 187 | 3, 4, |
| 188 | 5, 6, |
| 189 | 7, 8, |
| 190 | 9, 10, |
| 191 | 11, 12 |
| 192 | }; |
| 193 | |
| 194 | std::vector<T> expectedOutput0{ 1, 4, 7, 10 }; |
| 195 | std::vector<T> expectedOutput1{ 2, 5, 8, 11 }; |
| 196 | std::vector<T> expectedOutput2{ 3, 6, 9, 12 }; |
| 197 | |
| 198 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 199 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, |
| 200 | { 1, expectedOutput1 }, |
| 201 | { 2, expectedOutput2 } }; |
| 202 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 203 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 204 | } |
| 205 | |
| 206 | template<armnn::DataType ArmnnType> |
| 207 | void Splitter3dDim0EndToEnd(const std::vector<BackendId>& backends) |
| 208 | { |
| 209 | using namespace armnn; |
| 210 | using T = ResolveType<ArmnnType>; |
| 211 | |
| 212 | unsigned int splitAxis = 0; |
| 213 | unsigned int numSplit = 2; |
| 214 | const TensorShape& inputShape = { 2, 4, 3 }; |
| 215 | const std::vector<TensorShape> outputShapes{{ 1, 4, 3 }, { 1, 4, 3 }}; |
| 216 | |
| 217 | // Builds up the structure of the network |
| 218 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 219 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 220 | CHECK(net); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 221 | |
| 222 | // Creates structures for input & output. |
| 223 | std::vector<T> inputData{ |
| 224 | 1, 2, 3, |
| 225 | 4, 5, 6, |
| 226 | 7, 8, 9, |
| 227 | 10, 11, 12, |
| 228 | 13, 14, 15, |
| 229 | 16, 17, 18, |
| 230 | 19, 20, 21, |
| 231 | 22, 23, 24 |
| 232 | }; |
| 233 | |
| 234 | std::vector<T> expectedOutput0{ |
| 235 | 1, 2, 3, |
| 236 | 4, 5, 6, |
| 237 | 7, 8, 9, |
| 238 | 10, 11, 12 |
| 239 | }; |
| 240 | std::vector<T> expectedOutput1{ |
| 241 | 13, 14, 15, |
| 242 | 16, 17, 18, |
| 243 | 19, 20, 21, |
| 244 | 22, 23, 24 |
| 245 | }; |
| 246 | |
| 247 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 248 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, |
| 249 | { 1, expectedOutput1 } }; |
| 250 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 251 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 252 | } |
| 253 | |
| 254 | template<armnn::DataType ArmnnType> |
| 255 | void Splitter3dDim1EndToEnd(const std::vector<BackendId>& backends) |
| 256 | { |
| 257 | using namespace armnn; |
| 258 | using T = ResolveType<ArmnnType>; |
| 259 | |
| 260 | unsigned int splitAxis = 1; |
| 261 | unsigned int numSplit = 2; |
| 262 | const TensorShape& inputShape = { 2, 4, 3 }; |
| 263 | const std::vector<TensorShape> outputShapes{{ 2, 2, 3 }, { 2, 2, 3 }}; |
| 264 | |
| 265 | // Builds up the structure of the network |
| 266 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 267 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 268 | CHECK(net); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 269 | |
| 270 | // Creates structures for input & output. |
| 271 | std::vector<T> inputData{ |
| 272 | 1, 2, 3, |
| 273 | 4, 5, 6, |
| 274 | 7, 8, 9, |
| 275 | 10, 11, 12, |
| 276 | 13, 14, 15, |
| 277 | 16, 17, 18, |
| 278 | 19, 20, 21, |
| 279 | 22, 23, 24 |
| 280 | }; |
| 281 | |
| 282 | std::vector<T> expectedOutput0{ |
| 283 | 1, 2, 3, |
| 284 | 4, 5, 6, |
| 285 | 13, 14, 15, |
| 286 | 16, 17, 18 |
| 287 | }; |
| 288 | std::vector<T> expectedOutput1{ |
| 289 | 7, 8, 9, |
| 290 | 10, 11, 12, |
| 291 | 19, 20, 21, |
| 292 | 22, 23, 24 |
| 293 | }; |
| 294 | |
| 295 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 296 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, |
| 297 | { 1, expectedOutput1 } }; |
| 298 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 299 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 300 | } |
| 301 | |
| 302 | template<armnn::DataType ArmnnType> |
Kevin May | 1bea6be | 2023-12-12 11:18:46 +0000 | [diff] [blame] | 303 | void Splitter3dDim1EndToEndFloat16(const std::vector<BackendId>& backends) |
| 304 | { |
| 305 | using namespace armnn; |
| 306 | using namespace half_float::literal; |
| 307 | using Half = half_float::half; |
| 308 | |
| 309 | unsigned int splitAxis = 1; |
| 310 | unsigned int numSplit = 2; |
| 311 | const TensorShape& inputShape = { 2, 4, 3 }; |
| 312 | const std::vector<TensorShape> outputShapes{{ 2, 2, 3 }, { 2, 2, 3 }}; |
| 313 | |
| 314 | // Builds up the structure of the network |
| 315 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 316 | |
| 317 | CHECK(net); |
| 318 | |
| 319 | // Creates structures for input & output. |
| 320 | std::vector<Half> inputData{ |
| 321 | 1._h, 2._h, 3._h, |
| 322 | 4._h, 5._h, 6._h, |
| 323 | 7._h, 8._h, 9._h, |
| 324 | 10._h, 11._h, 12._h, |
| 325 | 13._h, 14._h, 15._h, |
| 326 | 16._h, 17._h, 18._h, |
| 327 | 19._h, 20._h, 21._h, |
| 328 | 22._h, 23._h, 24._h |
| 329 | }; |
| 330 | |
| 331 | std::vector<Half> expectedOutput0{ |
| 332 | 1._h, 2._h, 3._h, |
| 333 | 4._h, 5._h, 6._h, |
| 334 | 13._h, 14._h, 15._h, |
| 335 | 16._h, 17._h, 18._h |
| 336 | }; |
| 337 | std::vector<Half> expectedOutput1{ |
| 338 | 7._h, 8._h, 9._h, |
| 339 | 10._h, 11._h, 12._h, |
| 340 | 19._h, 20._h, 21._h, |
| 341 | 22._h, 23._h, 24._h |
| 342 | }; |
| 343 | |
| 344 | std::map<int, std::vector<Half>> inputTensorData = { { 0, inputData } }; |
| 345 | std::map<int, std::vector<Half>> expectedOutputData = { { 0, expectedOutput0 }, |
| 346 | { 1, expectedOutput1 } }; |
| 347 | |
| 348 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
| 349 | } |
| 350 | |
| 351 | template<armnn::DataType ArmnnType> |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 352 | void Splitter3dDim2EndToEnd(const std::vector<BackendId>& backends) |
| 353 | { |
| 354 | using namespace armnn; |
| 355 | using T = ResolveType<ArmnnType>; |
| 356 | |
| 357 | unsigned int splitAxis = 2; |
| 358 | unsigned int numSplit = 3; |
| 359 | const TensorShape& inputShape = { 2, 4, 3 }; |
| 360 | const std::vector<TensorShape> outputShapes{{ 2, 4, 1 }, { 2, 4, 1 }, { 2, 4, 1 }}; |
| 361 | |
| 362 | // Builds up the structure of the network |
| 363 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 364 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 365 | CHECK(net); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 366 | |
| 367 | // Creates structures for input & output. |
| 368 | std::vector<T> inputData{ |
| 369 | 1, 2, 3, |
| 370 | 4, 5, 6, |
| 371 | 7, 8, 9, |
| 372 | 10, 11, 12, |
| 373 | 13, 14, 15, |
| 374 | 16, 17, 18, |
| 375 | 19, 20, 21, |
| 376 | 22, 23, 24 |
| 377 | }; |
| 378 | |
| 379 | std::vector<T> expectedOutput0{ 1, 4, 7, 10, 13, 16, 19, 22 }; |
| 380 | std::vector<T> expectedOutput1{ 2, 5, 8, 11, 14, 17, 20, 23 }; |
| 381 | std::vector<T> expectedOutput2{ 3, 6, 9, 12, 15, 18, 21, 24 }; |
| 382 | |
| 383 | std::map<int, std::vector<T>> inputTensorData = { { 0, inputData } }; |
| 384 | std::map<int, std::vector<T>> expectedOutputData = { { 0, expectedOutput0 }, |
| 385 | { 1, expectedOutput1 }, |
| 386 | { 2, expectedOutput2 } }; |
| 387 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 388 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 389 | } |
| 390 | |
| 391 | template<armnn::DataType ArmnnType> |
| 392 | void Splitter4dDim0EndToEnd(const std::vector<BackendId>& backends) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 393 | { |
| 394 | using namespace armnn; |
| 395 | using T = ResolveType<ArmnnType>; |
| 396 | |
| 397 | unsigned int splitAxis = 0; |
| 398 | unsigned int numSplit = 2; |
| 399 | const TensorShape& inputShape = { 4, 3, 2, 2 }; |
| 400 | const std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }}; |
| 401 | |
| 402 | // Builds up the structure of the network |
| 403 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 404 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 405 | CHECK(net); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 406 | |
| 407 | // Creates structures for input & output. |
| 408 | std::vector<T> inputData{ |
| 409 | 1, 2, |
| 410 | 3, 4, |
| 411 | 5, 6, |
| 412 | 7, 8, |
| 413 | 9, 10, |
| 414 | 11, 12, |
| 415 | 13, 14, |
| 416 | 15, 16, |
| 417 | 17, 18, |
| 418 | 19, 20, |
| 419 | 21, 22, |
| 420 | 23, 24, |
| 421 | 25, 26, |
| 422 | 27, 28, |
| 423 | 29, 30, |
| 424 | 31, 32, |
| 425 | 33, 34, |
| 426 | 35, 36, |
| 427 | 37, 38, |
| 428 | 39, 40, |
| 429 | 41, 42, |
| 430 | 43, 44, |
| 431 | 45, 46, |
| 432 | 47, 48 |
| 433 | }; |
| 434 | |
| 435 | std::vector<T> expectedOutput0{ |
| 436 | 1, 2, |
| 437 | 3, 4, |
| 438 | 5, 6, |
| 439 | 7, 8, |
| 440 | 9, 10, |
| 441 | 11, 12, |
| 442 | 13, 14, |
| 443 | 15, 16, |
| 444 | 17, 18, |
| 445 | 19, 20, |
| 446 | 21, 22, |
| 447 | 23, 24 |
| 448 | }; |
| 449 | |
| 450 | std::vector<T> expectedOutput1{ |
| 451 | 25, 26, |
| 452 | 27, 28, |
| 453 | 29, 30, |
| 454 | 31, 32, |
| 455 | 33, 34, |
| 456 | 35, 36, |
| 457 | 37, 38, |
| 458 | 39, 40, |
| 459 | 41, 42, |
| 460 | 43, 44, |
| 461 | 45, 46, |
| 462 | 47, 48 |
| 463 | }; |
| 464 | |
| 465 | std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }}; |
| 466 | std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }}; |
| 467 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 468 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 469 | } |
| 470 | |
| 471 | template<armnn::DataType ArmnnType> |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 472 | void Splitter4dDim1EndToEnd(const std::vector<BackendId>& backends) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 473 | { |
| 474 | using namespace armnn; |
| 475 | using T = ResolveType<ArmnnType>; |
| 476 | |
| 477 | unsigned int splitAxis = 1; |
| 478 | unsigned int numSplit = 2; |
| 479 | const TensorShape& inputShape = { 2, 6, 2, 2 }; |
| 480 | const std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }}; |
| 481 | |
| 482 | // Builds up the structure of the network |
| 483 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 484 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 485 | CHECK(net); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 486 | |
| 487 | // Creates structures for input & output. |
| 488 | std::vector<T> inputData{ |
| 489 | 1, 2, |
| 490 | 3, 4, |
| 491 | 5, 6, |
| 492 | 7, 8, |
| 493 | 9, 10, |
| 494 | 11, 12, |
| 495 | 13, 14, |
| 496 | 15, 16, |
| 497 | 17, 18, |
| 498 | 19, 20, |
| 499 | 21, 22, |
| 500 | 23, 24, |
| 501 | 25, 26, |
| 502 | 27, 28, |
| 503 | 29, 30, |
| 504 | 31, 32, |
| 505 | 33, 34, |
| 506 | 35, 36, |
| 507 | 37, 38, |
| 508 | 39, 40, |
| 509 | 41, 42, |
| 510 | 43, 44, |
| 511 | 45, 46, |
| 512 | 47, 48 |
| 513 | }; |
| 514 | |
| 515 | std::vector<T> expectedOutput0{ |
| 516 | 1, 2, |
| 517 | 3, 4, |
| 518 | 5, 6, |
| 519 | 7, 8, |
| 520 | 9, 10, |
| 521 | 11, 12, |
| 522 | 25, 26, |
| 523 | 27, 28, |
| 524 | 29, 30, |
| 525 | 31, 32, |
| 526 | 33, 34, |
| 527 | 35, 36 |
| 528 | }; |
| 529 | |
| 530 | std::vector<T> expectedOutput1{ |
| 531 | 13, 14, |
| 532 | 15, 16, |
| 533 | 17, 18, |
| 534 | 19, 20, |
| 535 | 21, 22, |
| 536 | 23, 24, |
| 537 | 37, 38, |
| 538 | 39, 40, |
| 539 | 41, 42, |
| 540 | 43, 44, |
| 541 | 45, 46, |
| 542 | 47, 48 |
| 543 | }; |
| 544 | |
| 545 | std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }}; |
| 546 | std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }}; |
| 547 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 548 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 549 | } |
| 550 | |
| 551 | template<armnn::DataType ArmnnType> |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 552 | void Splitter4dDim2EndToEnd(const std::vector<BackendId>& backends) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 553 | { |
| 554 | using namespace armnn; |
| 555 | using T = ResolveType<ArmnnType>; |
| 556 | |
| 557 | unsigned int splitAxis = 2; |
| 558 | unsigned int numSplit = 2; |
| 559 | const TensorShape& inputShape = { 2, 3, 4, 2 }; |
| 560 | const std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }}; |
| 561 | |
| 562 | // Builds up the structure of the network |
| 563 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 564 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 565 | CHECK(net); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 566 | |
| 567 | // Creates structures for input & output. |
| 568 | std::vector<T> inputData{ |
| 569 | 1, 2, |
| 570 | 3, 4, |
| 571 | 5, 6, |
| 572 | 7, 8, |
| 573 | 9, 10, |
| 574 | 11, 12, |
| 575 | 13, 14, |
| 576 | 15, 16, |
| 577 | 17, 18, |
| 578 | 19, 20, |
| 579 | 21, 22, |
| 580 | 23, 24, |
| 581 | 25, 26, |
| 582 | 27, 28, |
| 583 | 29, 30, |
| 584 | 31, 32, |
| 585 | 33, 34, |
| 586 | 35, 36, |
| 587 | 37, 38, |
| 588 | 39, 40, |
| 589 | 41, 42, |
| 590 | 43, 44, |
| 591 | 45, 46, |
| 592 | 47, 48 |
| 593 | }; |
| 594 | |
| 595 | std::vector<T> expectedOutput0{ |
| 596 | 1, 2, |
| 597 | 3, 4, |
| 598 | 9, 10, |
| 599 | 11, 12, |
| 600 | 17, 18, |
| 601 | 19, 20, |
| 602 | 25, 26, |
| 603 | 27, 28, |
| 604 | 33, 34, |
| 605 | 35, 36, |
| 606 | 41, 42, |
| 607 | 43, 44 |
| 608 | }; |
| 609 | |
| 610 | std::vector<T> expectedOutput1{ |
| 611 | 5, 6, |
| 612 | 7, 8, |
| 613 | 13, 14, |
| 614 | 15, 16, |
| 615 | 21, 22, |
| 616 | 23, 24, |
| 617 | 29, 30, |
| 618 | 31, 32, |
| 619 | 37, 38, |
| 620 | 39, 40, |
| 621 | 45, 46, |
| 622 | 47, 48 |
| 623 | }; |
| 624 | |
| 625 | std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }}; |
| 626 | std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }}; |
| 627 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 628 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 629 | } |
| 630 | |
Kevin May | 1bea6be | 2023-12-12 11:18:46 +0000 | [diff] [blame] | 631 | template<armnn::DataType ArmnnType> |
| 632 | void Splitter4dDim2EndToEndFloat16(const std::vector<BackendId>& backends) |
| 633 | { |
| 634 | using namespace armnn; |
| 635 | using namespace half_float::literal; |
| 636 | using Half = half_float::half; |
| 637 | |
| 638 | unsigned int splitAxis = 2; |
| 639 | unsigned int numSplit = 2; |
| 640 | const TensorShape& inputShape = { 2, 3, 4, 2 }; |
| 641 | const std::vector<TensorShape> outputShapes{{ 2, 3, 2, 2 }, { 2, 3, 2, 2 }}; |
| 642 | |
| 643 | // Builds up the structure of the network |
| 644 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 645 | |
| 646 | CHECK(net); |
| 647 | |
| 648 | // Creates structures for input & output. |
| 649 | std::vector<Half> inputData{ |
| 650 | 1._h, 2._h, |
| 651 | 3._h, 4._h, |
| 652 | 5._h, 6._h, |
| 653 | 7._h, 8._h, |
| 654 | 9._h, 10._h, |
| 655 | 11._h, 12._h, |
| 656 | 13._h, 14._h, |
| 657 | 15._h, 16._h, |
| 658 | 17._h, 18._h, |
| 659 | 19._h, 20._h, |
| 660 | 21._h, 22._h, |
| 661 | 23._h, 24._h, |
| 662 | 25._h, 26._h, |
| 663 | 27._h, 28._h, |
| 664 | 29._h, 30._h, |
| 665 | 31._h, 32._h, |
| 666 | 33._h, 34._h, |
| 667 | 35._h, 36._h, |
| 668 | 37._h, 38._h, |
| 669 | 39._h, 40._h, |
| 670 | 41._h, 42._h, |
| 671 | 43._h, 44._h, |
| 672 | 45._h, 46._h, |
| 673 | 47._h, 48._h |
| 674 | }; |
| 675 | |
| 676 | std::vector<Half> expectedOutput0{ |
| 677 | 1._h, 2._h, |
| 678 | 3._h, 4._h, |
| 679 | 9._h, 10._h, |
| 680 | 11._h, 12._h, |
| 681 | 17._h, 18._h, |
| 682 | 19._h, 20._h, |
| 683 | 25._h, 26._h, |
| 684 | 27._h, 28._h, |
| 685 | 33._h, 34._h, |
| 686 | 35._h, 36._h, |
| 687 | 41._h, 42._h, |
| 688 | 43._h, 44._h |
| 689 | }; |
| 690 | |
| 691 | std::vector<Half> expectedOutput1{ |
| 692 | 5._h, 6._h, |
| 693 | 7._h, 8._h, |
| 694 | 13._h, 14._h, |
| 695 | 15._h, 16._h, |
| 696 | 21._h, 22._h, |
| 697 | 23._h, 24._h, |
| 698 | 29._h, 30._h, |
| 699 | 31._h, 32._h, |
| 700 | 37._h, 38._h, |
| 701 | 39._h, 40._h, |
| 702 | 45._h, 46._h, |
| 703 | 47._h, 48._h |
| 704 | }; |
| 705 | |
| 706 | std::map<int, std::vector<Half>> inputTensorData = {{ 0,inputData }}; |
| 707 | std::map<int, std::vector<Half>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }}; |
| 708 | |
| 709 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
| 710 | } |
| 711 | |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 712 | template<armnn::DataType ArmnnType, typename T = armnn::ResolveType<ArmnnType>> |
Narumol Prangnawarat | 0f072ab | 2019-05-29 14:12:46 +0100 | [diff] [blame] | 713 | void Splitter4dDim3EndToEnd(const std::vector<BackendId>& backends) |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 714 | { |
| 715 | using namespace armnn; |
| 716 | |
| 717 | unsigned int splitAxis = 3; |
| 718 | unsigned int numSplit = 2; |
| 719 | const TensorShape& inputShape = { 2, 3, 4, 2 }; |
| 720 | const std::vector<TensorShape> outputShapes{{ 2, 3, 4, 1 }, { 2, 3, 4, 1 }}; |
| 721 | |
| 722 | // Builds up the structure of the network |
| 723 | INetworkPtr net = CreateSplitterNetwork<ArmnnType>(inputShape, outputShapes, splitAxis, numSplit); |
| 724 | |
Sadik Armagan | 1625efc | 2021-06-10 18:24:34 +0100 | [diff] [blame] | 725 | CHECK(net); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 726 | |
| 727 | // Creates structures for input & output. |
| 728 | std::vector<T> inputData{ |
| 729 | 1, 2, |
| 730 | 3, 4, |
| 731 | 5, 6, |
| 732 | 7, 8, |
| 733 | 9, 10, |
| 734 | 11, 12, |
| 735 | 13, 14, |
| 736 | 15, 16, |
| 737 | 17, 18, |
| 738 | 19, 20, |
| 739 | 21, 22, |
| 740 | 23, 24, |
| 741 | 25, 26, |
| 742 | 27, 28, |
| 743 | 29, 30, |
| 744 | 31, 32, |
| 745 | 33, 34, |
| 746 | 35, 36, |
| 747 | 37, 38, |
| 748 | 39, 40, |
| 749 | 41, 42, |
| 750 | 43, 44, |
| 751 | 45, 46, |
| 752 | 47, 48 |
| 753 | }; |
| 754 | |
| 755 | std::vector<T> expectedOutput0{ |
| 756 | 1, 3, 5, 7, |
| 757 | 9, 11, 13, 15, |
| 758 | 17, 19, 21, 23, |
| 759 | 25, 27, 29, 31, |
| 760 | 33, 35, 37, 39, |
| 761 | 41, 43, 45, 47 |
| 762 | }; |
| 763 | |
| 764 | std::vector<T> expectedOutput1{ |
| 765 | 2, 4, 6, 8, |
| 766 | 10, 12, 14, 16, |
| 767 | 18, 20, 22, 24, |
| 768 | 26, 28, 30, 32, |
| 769 | 34, 36, 38, 40, |
| 770 | 42, 44, 46, 48 |
| 771 | }; |
| 772 | |
| 773 | std::map<int, std::vector<T>> inputTensorData = {{ 0,inputData }}; |
| 774 | std::map<int, std::vector<T>> expectedOutputData = {{ 0, expectedOutput0 }, { 1, expectedOutput1 }}; |
| 775 | |
Mike Kelly | a9c3267 | 2023-12-04 17:23:09 +0000 | [diff] [blame] | 776 | EndToEndLayerTestImpl<ArmnnType, ArmnnType>(std::move(net), inputTensorData, expectedOutputData, backends); |
Narumol Prangnawarat | 0be4338 | 2019-05-27 11:29:59 +0100 | [diff] [blame] | 777 | } |
| 778 | |
| 779 | } // anonymous namespace |