telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 1 | // |
| 2 | // Copyright © 2017 Arm Ltd. All rights reserved. |
David Beck | ecb56cd | 2018-09-05 12:52:57 +0100 | [diff] [blame] | 3 | // SPDX-License-Identifier: MIT |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 4 | // |
| 5 | |
| 6 | #include <boost/test/unit_test.hpp> |
| 7 | #include "ParserFlatbuffersFixture.hpp" |
| 8 | #include "../TfLiteParser.hpp" |
| 9 | |
| 10 | #include <string> |
| 11 | #include <iostream> |
| 12 | |
| 13 | BOOST_AUTO_TEST_SUITE(TensorflowLiteParser) |
| 14 | |
| 15 | struct DepthwiseConvolution2dFixture : public ParserFlatbuffersFixture |
| 16 | { |
| 17 | explicit DepthwiseConvolution2dFixture(const std::string& inputShape, |
| 18 | const std::string& outputShape, |
| 19 | const std::string& filterShape, |
| 20 | const std::string& filterData, |
| 21 | const std::string& strides, |
| 22 | const std::string& paddingType, |
| 23 | const std::string biasShape = "", |
| 24 | const std::string biasData = "") |
| 25 | { |
| 26 | std::string inputTensors = "[ 0, 2 ]"; |
| 27 | std::string biasTensor = ""; |
| 28 | std::string biasBuffer = ""; |
| 29 | if (biasShape.size() > 0 && biasData.size() > 0) |
| 30 | { |
| 31 | inputTensors = "[ 0, 2, 3 ]"; |
| 32 | biasTensor = R"( |
| 33 | { |
| 34 | "shape": )" + biasShape + R"( , |
| 35 | "type": "INT32", |
| 36 | "buffer": 3, |
| 37 | "name": "biasTensor", |
| 38 | "quantization": { |
| 39 | "min": [ 0.0 ], |
| 40 | "max": [ 255.0 ], |
| 41 | "scale": [ 1.0 ], |
| 42 | "zero_point": [ 0 ], |
| 43 | } |
| 44 | } )"; |
| 45 | biasBuffer = R"( |
| 46 | { "data": )" + biasData + R"(, }, )"; |
| 47 | } |
| 48 | m_JsonString = R"( |
| 49 | { |
| 50 | "version": 3, |
| 51 | "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ], |
| 52 | "subgraphs": [ { |
| 53 | "tensors": [ |
| 54 | { |
| 55 | "shape": )" + inputShape + R"(, |
| 56 | "type": "UINT8", |
| 57 | "buffer": 0, |
| 58 | "name": "inputTensor", |
| 59 | "quantization": { |
| 60 | "min": [ 0.0 ], |
| 61 | "max": [ 255.0 ], |
| 62 | "scale": [ 1.0 ], |
| 63 | "zero_point": [ 0 ], |
| 64 | } |
| 65 | }, |
| 66 | { |
| 67 | "shape": )" + outputShape + R"(, |
| 68 | "type": "UINT8", |
| 69 | "buffer": 1, |
| 70 | "name": "outputTensor", |
| 71 | "quantization": { |
| 72 | "min": [ 0.0 ], |
| 73 | "max": [ 511.0 ], |
| 74 | "scale": [ 2.0 ], |
| 75 | "zero_point": [ 0 ], |
| 76 | } |
| 77 | }, |
| 78 | { |
| 79 | "shape": )" + filterShape + R"(, |
| 80 | "type": "UINT8", |
| 81 | "buffer": 2, |
| 82 | "name": "filterTensor", |
| 83 | "quantization": { |
| 84 | "min": [ 0.0 ], |
| 85 | "max": [ 255.0 ], |
| 86 | "scale": [ 1.0 ], |
| 87 | "zero_point": [ 0 ], |
| 88 | } |
| 89 | }, )" + biasTensor + R"( |
| 90 | ], |
| 91 | "inputs": [ 0 ], |
| 92 | "outputs": [ 1 ], |
| 93 | "operators": [ |
| 94 | { |
| 95 | "opcode_index": 0, |
| 96 | "inputs": )" + inputTensors + R"(, |
| 97 | "outputs": [ 1 ], |
| 98 | "builtin_options_type": "DepthwiseConv2DOptions", |
| 99 | "builtin_options": { |
| 100 | "padding": ")" + paddingType + R"(", |
| 101 | "stride_w": )" + strides+ R"(, |
| 102 | "stride_h": )" + strides+ R"(, |
| 103 | "depth_multiplier": 1, |
| 104 | "fused_activation_function": "NONE" |
| 105 | }, |
| 106 | "custom_options_format": "FLEXBUFFERS" |
| 107 | } |
| 108 | ], |
| 109 | } ], |
| 110 | "buffers" : [ |
| 111 | { }, |
| 112 | { }, |
| 113 | { "data": )" + filterData + R"(, }, )" |
| 114 | + biasBuffer + R"( |
| 115 | ] |
| 116 | } |
| 117 | )"; |
| 118 | SetupSingleInputSingleOutput("inputTensor", "outputTensor"); |
| 119 | } |
| 120 | }; |
| 121 | |
| 122 | struct DepthwiseConvolution2dSameFixture : DepthwiseConvolution2dFixture |
| 123 | { |
| 124 | DepthwiseConvolution2dSameFixture() |
| 125 | : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape |
| 126 | "[ 1, 3, 3, 1 ]", // outputShape |
| 127 | "[ 1, 3, 3, 1 ]", // filterShape |
| 128 | "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData |
| 129 | "1", // stride w and h |
| 130 | "SAME") // padding type |
| 131 | {} |
| 132 | }; |
| 133 | |
| 134 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DSame, DepthwiseConvolution2dSameFixture) |
| 135 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 136 | RunTest<4, armnn::DataType::QAsymmU8>( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 137 | 0, |
| 138 | { 0, 1, 2, |
| 139 | 3, 4, 5, |
| 140 | 6, 7, 8 }, |
| 141 | // the expected values were generated using the example python implementation at |
| 142 | // https://eli.thegreenplace.net/2018/depthwise-separable-convolutions-for-machine-learning/ |
| 143 | // divide the expected values by the output scale, as it is not 1.0 |
| 144 | { 14/2, 35/2, 38/2, |
| 145 | 57/2, 120/2, 111/2, |
| 146 | 110/2, 197/2, 158/2 }); |
| 147 | } |
| 148 | |
| 149 | struct DepthwiseConvolution2dValidFixture : DepthwiseConvolution2dFixture |
| 150 | { |
| 151 | DepthwiseConvolution2dValidFixture () |
| 152 | : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape |
| 153 | "[ 1, 1, 1, 1 ]", // outputShape |
| 154 | "[ 1, 3, 3, 1 ]", // filterShape |
| 155 | "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData |
| 156 | "1", // stride w and h |
| 157 | "VALID") // padding type |
| 158 | {} |
| 159 | }; |
| 160 | |
| 161 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DValid, DepthwiseConvolution2dValidFixture) |
| 162 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 163 | RunTest<4, armnn::DataType::QAsymmU8>( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 164 | 0, |
| 165 | { 0, 1, 2, |
| 166 | 3, 4, 5, |
| 167 | 6, 7, 8 }, |
| 168 | // divide the expected values by the output scale, as it is not 1.0 |
| 169 | { 120/2 }); |
| 170 | } |
| 171 | |
| 172 | struct DepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture |
| 173 | { |
| 174 | DepthwiseConvolution2dSameBiasFixture() |
| 175 | : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape |
| 176 | "[ 1, 3, 3, 1 ]", // outputShape |
| 177 | "[ 1, 3, 3, 1 ]", // filterShape |
| 178 | "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData |
| 179 | "1", // stride w and h |
| 180 | "SAME", // padding type |
| 181 | "[ 1 ]", // biasShape |
| 182 | "[ 10, 0, 0, 0 ]") // biasData |
| 183 | {} |
| 184 | }; |
| 185 | |
| 186 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DSameBias, DepthwiseConvolution2dSameBiasFixture) |
| 187 | { |
Derek Lamberti | f90c56d | 2020-01-10 17:14:08 +0000 | [diff] [blame] | 188 | RunTest<4, armnn::DataType::QAsymmU8>( |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 189 | 0, |
| 190 | { 0, 1, 2, |
| 191 | 3, 4, 5, |
| 192 | 6, 7, 8 }, |
| 193 | // divide the expected values by the output scale, as it is not 1.0 |
| 194 | { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2, |
| 195 | ( 57+10)/2, (120+10)/2, (111+10)/2, |
| 196 | (110+10)/2, (197+10)/2, (158+10)/2 }); |
| 197 | } |
| 198 | |
Sadik Armagan | d109a4d | 2020-07-28 10:42:13 +0100 | [diff] [blame] | 199 | struct DynamicDepthwiseConvolution2dSameBiasFixture : DepthwiseConvolution2dFixture |
| 200 | { |
| 201 | DynamicDepthwiseConvolution2dSameBiasFixture() |
| 202 | : DepthwiseConvolution2dFixture("[ 1, 3, 3, 1 ]", // inputShape |
| 203 | "[ ]", // outputShape |
| 204 | "[ 1, 3, 3, 1 ]", // filterShape |
| 205 | "[ 9,8,7, 6,5,4, 3,2,1 ]", // filterData |
| 206 | "1", // stride w and h |
| 207 | "SAME", // padding type |
| 208 | "[ 1 ]", // biasShape |
| 209 | "[ 10, 0, 0, 0 ]") // biasData |
| 210 | {} |
| 211 | }; |
| 212 | |
| 213 | BOOST_FIXTURE_TEST_CASE(ParseDynamicDepthwiseConv2DSameBias, DynamicDepthwiseConvolution2dSameBiasFixture) |
| 214 | { |
| 215 | RunTest<4, armnn::DataType::QAsymmU8, armnn::DataType::QAsymmU8>(0, |
| 216 | { { "inputTensor", { 0, 1, 2, |
| 217 | 3, 4, 5, |
| 218 | 6, 7, 8 } } }, |
| 219 | { { "outputTensor", { ( 14+10)/2, ( 35+10)/2, ( 38+10)/2, |
| 220 | ( 57+10)/2, (120+10)/2, (111+10)/2, |
| 221 | (110+10)/2, (197+10)/2, (158+10)/2 } } }, |
| 222 | true); |
| 223 | } |
| 224 | |
Jan Eilers | f649149 | 2021-04-02 13:06:15 +0100 | [diff] [blame] | 225 | struct DepthwiseConvolution2dFixture2 : public ParserFlatbuffersFixture |
| 226 | { |
| 227 | explicit DepthwiseConvolution2dFixture2(const std::string& inputShape, |
| 228 | const std::string& outputShape, |
| 229 | const std::string& filterShape, |
| 230 | const std::string& filterData, |
| 231 | const std::string& strides, |
| 232 | const std::string& paddingType, |
| 233 | const std::string biasShape = "", |
| 234 | const std::string biasData = "", |
| 235 | const std::string filter_quant_min = "[ 0.0 ]", |
| 236 | const std::string filter_quant_max = "[ 255.0 ]", |
| 237 | const std::string filter_quant_scale = "[ 1.0 ]", |
| 238 | const std::string filter_quant_zero_point = "[ 0 ]", |
| 239 | const std::string filter_quant_axis = "" |
| 240 | ) |
| 241 | { |
| 242 | std::string inputTensors = "[ 0, 2 ]"; |
| 243 | std::string biasTensor = ""; |
| 244 | std::string biasBuffer = ""; |
| 245 | if (biasShape.size() > 0 && biasData.size() > 0) |
| 246 | { |
| 247 | inputTensors = "[ 0, 2, 3 ]"; |
| 248 | biasTensor = R"( |
| 249 | { |
| 250 | "shape": )" + biasShape + R"( , |
| 251 | "type": "INT32", |
| 252 | "buffer": 3, |
| 253 | "name": "biasTensor", |
| 254 | "quantization": { |
| 255 | "min": [ 0.0 ], |
| 256 | "max": [ 255.0 ], |
| 257 | "scale": [ 1.0 ], |
| 258 | "zero_point": [ 0 ], |
| 259 | } |
| 260 | } )"; |
| 261 | biasBuffer = R"( |
| 262 | { "data": )" + biasData + R"(, }, )"; |
| 263 | } |
| 264 | |
| 265 | std::string filter_qantization = |
| 266 | R"( |
| 267 | "min": )" + filter_quant_min + R"(, |
| 268 | "max": )" + filter_quant_max + R"(, |
| 269 | "scale": )" + filter_quant_scale + R"(, |
| 270 | "zero_point": )" + filter_quant_zero_point; |
| 271 | // A given quantization axis indicates if per channel quantization is used for filters |
| 272 | if (filter_quant_axis.size() > 0) |
| 273 | { |
| 274 | filter_qantization += |
| 275 | R"(, |
| 276 | "quantized_dimension": )" + filter_quant_axis; |
| 277 | } |
| 278 | m_JsonString = R"( |
| 279 | { |
| 280 | "version": 3, |
| 281 | "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ], |
| 282 | "subgraphs": [ { |
| 283 | "tensors": [ |
| 284 | { |
| 285 | "shape": )" + inputShape + R"(, |
| 286 | "type": "INT8", |
| 287 | "buffer": 0, |
| 288 | "name": "inputTensor", |
| 289 | "quantization": { |
| 290 | "min": [ 0.0 ], |
| 291 | "max": [ 255.0 ], |
| 292 | "scale": [ 1.0 ], |
| 293 | "zero_point": [ 0 ], |
| 294 | } |
| 295 | }, |
| 296 | { |
| 297 | "shape": )" + outputShape + R"(, |
| 298 | "type": "INT8", |
| 299 | "buffer": 1, |
| 300 | "name": "outputTensor", |
| 301 | "quantization": { |
| 302 | "min": [ 0.0 ], |
| 303 | "max": [ 511.0 ], |
| 304 | "scale": [ 1.0 ], |
| 305 | "zero_point": [ 0 ], |
| 306 | } |
| 307 | }, |
| 308 | { |
| 309 | "shape": )" + filterShape + R"(, |
| 310 | "type": "INT8", |
| 311 | "buffer": 2, |
| 312 | "name": "filterTensor", |
| 313 | "quantization": {)" + filter_qantization + R"( |
| 314 | } |
| 315 | }, )" + biasTensor + R"( |
| 316 | ], |
| 317 | "inputs": [ 0 ], |
| 318 | "outputs": [ 1 ], |
| 319 | "operators": [ |
| 320 | { |
| 321 | "opcode_index": 0, |
| 322 | "inputs": )" + inputTensors + R"(, |
| 323 | "outputs": [ 1 ], |
| 324 | "builtin_options_type": "DepthwiseConv2DOptions", |
| 325 | "builtin_options": { |
| 326 | "padding": ")" + paddingType + R"(", |
| 327 | "stride_w": )" + strides+ R"(, |
| 328 | "stride_h": )" + strides+ R"(, |
| 329 | "depth_multiplier": 1, |
| 330 | "fused_activation_function": "NONE" |
| 331 | }, |
| 332 | "custom_options_format": "FLEXBUFFERS" |
| 333 | } |
| 334 | ], |
| 335 | } ], |
| 336 | "buffers" : [ |
| 337 | { }, |
| 338 | { }, |
| 339 | { "data": )" + filterData + R"(, }, )" |
| 340 | + biasBuffer + R"( |
| 341 | ] |
| 342 | } |
| 343 | )"; |
| 344 | SetupSingleInputSingleOutput("inputTensor", "outputTensor"); |
| 345 | } |
| 346 | }; |
| 347 | |
| 348 | |
| 349 | // No quantization meaning scale=1.0 and offset=0.0 and tensor quantization |
| 350 | struct DepthwiseConvolution2dNoQuantFixture : DepthwiseConvolution2dFixture2 |
| 351 | { |
| 352 | DepthwiseConvolution2dNoQuantFixture() |
| 353 | : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape |
| 354 | "[ 1, 3, 3, 3 ]", // outputShape |
| 355 | "[ 1, 3, 3, 3 ]", // filterShape |
| 356 | "[ 9,8,7, 6,5,4, 3,2,1, " |
| 357 | "9,8,7, 6,5,4, 3,2,1, " |
| 358 | "9,8,7, 6,5,4, 3,2,1 ]", // filterData |
| 359 | "1", // stride w and h |
| 360 | "SAME", // padding type |
| 361 | "", // bias shape |
| 362 | "" // bias data |
| 363 | ) |
| 364 | {} |
| 365 | }; |
| 366 | |
| 367 | // No quantization meaning scale=1.0 and offset=0.0 and tensor quantization |
| 368 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNoQuant, DepthwiseConvolution2dNoQuantFixture) |
| 369 | { |
| 370 | RunTest<4, armnn::DataType::QAsymmS8>( |
| 371 | 0, |
| 372 | { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, |
| 373 | { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, |
| 374 | 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); |
| 375 | } |
| 376 | |
| 377 | // Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0 |
| 378 | struct DepthwiseConvolution2dNoChannelQuantFixture : DepthwiseConvolution2dFixture2 |
| 379 | { |
| 380 | DepthwiseConvolution2dNoChannelQuantFixture() |
| 381 | : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape |
| 382 | "[ 1, 3, 3, 3 ]", // outputShape |
| 383 | "[ 1, 3, 3, 3 ]", // filterShape |
| 384 | "[ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]", // filterData |
| 385 | "1", // stride w and h |
| 386 | "SAME", // padding type |
| 387 | "", // bias shape |
| 388 | "", // bias data |
| 389 | "[ 0.0 ]", // filter quantization min values |
| 390 | "[ 255.0 ]", // filter quantization max values |
| 391 | "[ 1.0, 1.0, 1.0]", // filter quantization scales |
| 392 | "[ 0, 0, 0]", // filter quantization zero-points |
| 393 | "3" // filter quantized axis |
| 394 | // (in case of per channel quantization) |
| 395 | ) |
| 396 | {} |
| 397 | }; |
| 398 | |
| 399 | // Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0 |
| 400 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterNoChannelQuant, DepthwiseConvolution2dNoChannelQuantFixture) |
| 401 | { |
| 402 | RunTest<4, armnn::DataType::QAsymmS8>( |
| 403 | 0, |
| 404 | { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, |
| 405 | { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, |
| 406 | 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); |
| 407 | } |
| 408 | |
| 409 | // Uses per channel quantization on weights but all scales are set to the same value |
| 410 | struct DepthwiseConvolution2dWeightsPerChannelQuantFixture : DepthwiseConvolution2dFixture2 |
| 411 | { |
| 412 | DepthwiseConvolution2dWeightsPerChannelQuantFixture() |
| 413 | : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape |
| 414 | "[ 1, 3, 3, 3 ]", // outputShape |
| 415 | "[ 1, 3, 3, 3 ]", // filterShape |
| 416 | // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ] |
| 417 | // quantized per channel with q_dim=3 |
| 418 | "[36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, " |
| 419 | "20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 8, 4]", |
| 420 | "1", // stride w and h |
| 421 | "SAME", // padding type |
| 422 | "", // bias shape |
| 423 | "", // bias data |
| 424 | "[ 0.0 ]", // filter quantization min values |
| 425 | "[ 255.0 ]", // filter quantization max values |
| 426 | "[ 0.25, 0.25, 0.25]", // filter quantization scales |
| 427 | "[ 0, 0, 0]", // filter quantization zero-points |
| 428 | "3" // filter quantized axis |
| 429 | // (in case of per channel quantization) |
| 430 | ) |
| 431 | {} |
| 432 | }; |
| 433 | |
| 434 | // Weights are per channel quantized but all scales are set to the same value |
| 435 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant, |
| 436 | DepthwiseConvolution2dWeightsPerChannelQuantFixture) |
| 437 | { |
| 438 | RunTest<4, armnn::DataType::QAsymmS8>( |
| 439 | 0, |
| 440 | { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, |
| 441 | { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, |
| 442 | 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); |
| 443 | } |
| 444 | |
| 445 | // Uses per channel quantization on weights all scales are different in this test |
| 446 | struct DepthwiseConvolution2dWeightsPerChannelQuant1Fixture : DepthwiseConvolution2dFixture2 |
| 447 | { |
| 448 | DepthwiseConvolution2dWeightsPerChannelQuant1Fixture() |
| 449 | : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape |
| 450 | "[ 1, 3, 3, 3 ]", // outputShape |
| 451 | "[ 1, 3, 3, 3 ]", // filterShape |
| 452 | // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ] |
| 453 | // quantized per channel with q_dim=3 |
| 454 | "[36, 40, 70, 24, 25, 40, 12, 10, 10, 36, 40, 70, 24, " |
| 455 | "25, 40, 12, 10, 10, 36, 40, 70, 24, 25, 40, 12, 10, 10]", |
| 456 | "1", // stride w and h |
| 457 | "SAME", // padding type |
| 458 | "", // bias shape |
| 459 | "", // bias data |
| 460 | "[ 0.0 ]", // filter quantization min values |
| 461 | "[ 255.0 ]", // filter quantization max values |
| 462 | "[ 0.25, 0.2, 0.1]", // filter quantization scales |
| 463 | "[ 0, 0, 0]", // filter quantization zero-points |
| 464 | "3" // filter quantized axis |
| 465 | // (in case of per channel quantization) |
| 466 | ) |
| 467 | {} |
| 468 | }; |
| 469 | |
| 470 | // Uses per channel quantization on weights all scales are different in this test |
| 471 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant1, |
| 472 | DepthwiseConvolution2dWeightsPerChannelQuant1Fixture) |
| 473 | { |
| 474 | RunTest<4, armnn::DataType::QAsymmS8>( |
| 475 | 0, |
| 476 | { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1}, |
| 477 | { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45, |
| 478 | 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22}); |
| 479 | } |
| 480 | |
| 481 | |
| 482 | // Uses per channel quantization on weights all scales are different in this test |
| 483 | // Uses different shape for weights and input compared to the other tests above |
| 484 | struct DepthwiseConvolution2dWeightsPerChannelQuant2Fixture : DepthwiseConvolution2dFixture2 |
| 485 | { |
| 486 | DepthwiseConvolution2dWeightsPerChannelQuant2Fixture() |
| 487 | : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape |
| 488 | "[ 1, 4, 4, 4 ]", // outputShape |
| 489 | "[ 1, 2, 2, 4 ]", // filterShape |
| 490 | // filterData is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ] |
| 491 | // quantized per channel with q_dim=3 |
| 492 | "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]", |
| 493 | "1", // stride w and h |
| 494 | "SAME", // padding type |
| 495 | "", // bias shape |
| 496 | "", // bias data |
| 497 | "[ 0.0 ]", // filter quantization min values |
| 498 | "[ 255.0 ]", // filter quantization max values |
| 499 | "[ 0.25, 0.2, 0.1, 0.3]", // filter quantization scales |
| 500 | "[ 0, 0, 0, 0]", // filter quantization zero-points |
| 501 | "3" // filter quantized axis |
| 502 | // (in case of per channel quantization) |
| 503 | ) |
| 504 | {} |
| 505 | }; |
| 506 | |
| 507 | // Uses per channel quantization on weights all scales are different in this test |
| 508 | // Uses different shape for weights and input compared to the other tests above |
| 509 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant2, |
| 510 | DepthwiseConvolution2dWeightsPerChannelQuant2Fixture) |
| 511 | { |
| 512 | RunTest<4, armnn::DataType::QAsymmS8>( |
| 513 | 0, |
| 514 | { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, |
| 515 | 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, |
| 516 | 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, |
| 517 | 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1}, |
| 518 | { 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13, |
| 519 | 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13, |
| 520 | 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13, |
| 521 | 14, 12, 10, 8, 14, 12, 10, 8, 14, 12, 10, 8, 9, 8, 7, 6}); |
| 522 | } |
| 523 | |
| 524 | // Test for depthwise_multiplier different to one (M > 1) |
| 525 | struct DepthwiseConvolution2dWeightsPerChannelQuant4Fixture : DepthwiseConvolution2dFixture2 |
| 526 | { |
| 527 | DepthwiseConvolution2dWeightsPerChannelQuant4Fixture() |
| 528 | : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape |
| 529 | "[ 1, 4, 4, 16 ]", // outputShape |
| 530 | "[ 1, 2, 2, 16 ]", // filterShape |
| 531 | // filter data is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3, |
| 532 | // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3, |
| 533 | // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3, |
| 534 | // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ] |
| 535 | // quantized per channel with q_dim=3 |
| 536 | "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, " |
| 537 | "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, " |
| 538 | "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, " |
| 539 | "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]", |
| 540 | "1", // stride w and h |
| 541 | "SAME", // padding type |
| 542 | "", // bias shape |
| 543 | "", // bias data |
| 544 | "[ 0.0 ]", // filter quantization min values |
| 545 | "[ 255.0 ]", // filter quantization max values |
| 546 | "[ 0.25, 0.2, 0.1, 0.3," |
| 547 | "0.25, 0.2, 0.1, 0.3," |
| 548 | "0.25, 0.2, 0.1, 0.3," |
| 549 | "0.25, 0.2, 0.1, 0.3]", // filter quantization scales |
| 550 | "[ 0, 0, 0, 0]", // filter quantization zero-points |
| 551 | "3" // filter quantized axis |
| 552 | // (in case of per channel quantization) |
| 553 | ) |
| 554 | {} |
| 555 | }; |
| 556 | |
| 557 | // Test for depthwise_multiplier different to one (M > 1) |
| 558 | BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4, |
| 559 | DepthwiseConvolution2dWeightsPerChannelQuant4Fixture) |
| 560 | { |
| 561 | RunTest<4, armnn::DataType::QAsymmS8>( |
| 562 | 0, |
| 563 | { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, |
| 564 | 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, |
| 565 | 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1, |
| 566 | 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1}, |
| 567 | { 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 568 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 569 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 570 | 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, |
| 571 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 572 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 573 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 574 | 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, |
| 575 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 576 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 577 | 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, |
| 578 | 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, |
| 579 | 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, |
| 580 | 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, |
| 581 | 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6, |
| 582 | 9, 8, 7, 6, 5, 4, 3, 2, 1, 9, 8, 7, 6, 5, 4, 3}); |
| 583 | } |
| 584 | |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 585 | BOOST_AUTO_TEST_SUITE_END() |