1 // clang-format off 2 // Generated file (from: logistic_v1_2.mod.py). Do not edit 3 void CreateModel(Model *model) { 4 OperandType type0(Type::TENSOR_FLOAT16, {1, 2, 2, 1}); 5 // Phase 1, operands 6 auto op1 = model->addOperand(&type0); 7 auto op3 = model->addOperand(&type0); 8 // Phase 2, operations 9 model->addOperation(ANEURALNETWORKS_LOGISTIC, {op1}, {op3}); 10 // Phase 3, inputs and outputs 11 model->identifyInputsAndOutputs( 12 {op1}, 13 {op3}); 14 assert(model->isValid()); 15 } 16 17 inline bool is_ignored(int i) { 18 static std::set<int> ignore = {}; 19 return ignore.find(i) != ignore.end(); 20 } 21 22 void CreateModel_dynamic_output_shape(Model *model) { 23 OperandType type0(Type::TENSOR_FLOAT16, {1, 2, 2, 1}); 24 OperandType type13(Type::TENSOR_FLOAT16, {0, 0, 0, 0}); 25 // Phase 1, operands 26 auto op1 = model->addOperand(&type0); 27 auto op3 = model->addOperand(&type13); 28 // Phase 2, operations 29 model->addOperation(ANEURALNETWORKS_LOGISTIC, {op1}, {op3}); 30 // Phase 3, inputs and outputs 31 model->identifyInputsAndOutputs( 32 {op1}, 33 {op3}); 34 assert(model->isValid()); 35 } 36 37 inline bool is_ignored_dynamic_output_shape(int i) { 38 static std::set<int> ignore = {}; 39 return ignore.find(i) != ignore.end(); 40 } 41 42 void CreateModel_2(Model *model) { 43 OperandType type1(Type::TENSOR_FLOAT16, {2, 32, 40, 2}); 44 // Phase 1, operands 45 auto input = model->addOperand(&type1); 46 auto output = model->addOperand(&type1); 47 // Phase 2, operations 48 model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output}); 49 // Phase 3, inputs and outputs 50 model->identifyInputsAndOutputs( 51 {input}, 52 {output}); 53 assert(model->isValid()); 54 } 55 56 inline bool is_ignored_2(int i) { 57 static std::set<int> ignore = {}; 58 return ignore.find(i) != ignore.end(); 59 } 60 61 void CreateModel_dynamic_output_shape_2(Model *model) { 62 OperandType type1(Type::TENSOR_FLOAT16, {2, 32, 40, 2}); 63 OperandType type13(Type::TENSOR_FLOAT16, {0, 0, 0, 0}); 64 // Phase 1, operands 65 auto input = model->addOperand(&type1); 66 auto output = model->addOperand(&type13); 67 // Phase 2, operations 68 model->addOperation(ANEURALNETWORKS_LOGISTIC, {input}, {output}); 69 // Phase 3, inputs and outputs 70 model->identifyInputsAndOutputs( 71 {input}, 72 {output}); 73 assert(model->isValid()); 74 } 75 76 inline bool is_ignored_dynamic_output_shape_2(int i) { 77 static std::set<int> ignore = {}; 78 return ignore.find(i) != ignore.end(); 79 } 80 81 void CreateModel_zero_sized(Model *model) { 82 OperandType type10(Type::BOOL, {}); 83 OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1}); 84 OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1}); 85 OperandType type2(Type::TENSOR_FLOAT32, {1, 2}); 86 OperandType type3(Type::TENSOR_FLOAT32, {1, 8}); 87 OperandType type4(Type::TENSOR_FLOAT32, {0}); 88 OperandType type5(Type::TENSOR_INT32, {0}); 89 OperandType type6(Type::TENSOR_FLOAT32, {0, 4}); 90 OperandType type7(Type::TENSOR_INT32, {1}); 91 OperandType type8(Type::FLOAT32, {}); 92 OperandType type9(Type::INT32, {}); 93 // Phase 1, operands 94 auto scores = model->addOperand(&type2); 95 auto roi = model->addOperand(&type3); 96 auto param = model->addOperand(&type7); 97 auto param1 = model->addOperand(&type8); 98 auto param2 = model->addOperand(&type9); 99 auto param3 = model->addOperand(&type9); 100 auto param4 = model->addOperand(&type8); 101 auto param5 = model->addOperand(&type8); 102 auto param6 = model->addOperand(&type8); 103 auto scoresOut = model->addOperand(&type4); 104 auto roiOut = model->addOperand(&type6); 105 auto classesOut = model->addOperand(&type5); 106 auto batchSplitOut = model->addOperand(&type5); 107 auto in = model->addOperand(&type11); 108 auto param7 = model->addOperand(&type9); 109 auto param8 = model->addOperand(&type9); 110 auto param9 = model->addOperand(&type8); 111 auto param10 = model->addOperand(&type8); 112 auto param11 = model->addOperand(&type9); 113 auto param12 = model->addOperand(&type9); 114 auto layout = model->addOperand(&type10); 115 auto featureMap = model->addOperand(&type12); 116 auto out = model->addOperand(&type12); 117 // Phase 2, operations 118 static float scores_init[] = {0.9f, 0.1f}; 119 model->setOperandValue(scores, scores_init, sizeof(float) * 2); 120 static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 121 model->setOperandValue(roi, roi_init, sizeof(float) * 8); 122 static int32_t param_init[] = {0}; 123 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 124 static float param1_init[] = {0.3f}; 125 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 126 static int32_t param2_init[] = {-1}; 127 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 128 static int32_t param3_init[] = {0}; 129 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 130 static float param4_init[] = {0.4f}; 131 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 132 static float param5_init[] = {1.0f}; 133 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 134 static float param6_init[] = {0.3f}; 135 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 136 static int32_t param7_init[] = {2}; 137 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 138 static int32_t param8_init[] = {2}; 139 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 140 static float param9_init[] = {2.0f}; 141 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 142 static float param10_init[] = {2.0f}; 143 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 144 static int32_t param11_init[] = {4}; 145 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 146 static int32_t param12_init[] = {4}; 147 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 148 static bool8 layout_init[] = {false}; 149 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 150 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 151 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 152 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 153 // Phase 3, inputs and outputs 154 model->identifyInputsAndOutputs( 155 {in}, 156 {scoresOut, classesOut, out}); 157 assert(model->isValid()); 158 } 159 160 inline bool is_ignored_zero_sized(int i) { 161 static std::set<int> ignore = {}; 162 return ignore.find(i) != ignore.end(); 163 } 164 165 void CreateModel_zero_sized_relaxed(Model *model) { 166 OperandType type10(Type::BOOL, {}); 167 OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1}); 168 OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1}); 169 OperandType type2(Type::TENSOR_FLOAT32, {1, 2}); 170 OperandType type3(Type::TENSOR_FLOAT32, {1, 8}); 171 OperandType type4(Type::TENSOR_FLOAT32, {0}); 172 OperandType type5(Type::TENSOR_INT32, {0}); 173 OperandType type6(Type::TENSOR_FLOAT32, {0, 4}); 174 OperandType type7(Type::TENSOR_INT32, {1}); 175 OperandType type8(Type::FLOAT32, {}); 176 OperandType type9(Type::INT32, {}); 177 // Phase 1, operands 178 auto scores = model->addOperand(&type2); 179 auto roi = model->addOperand(&type3); 180 auto param = model->addOperand(&type7); 181 auto param1 = model->addOperand(&type8); 182 auto param2 = model->addOperand(&type9); 183 auto param3 = model->addOperand(&type9); 184 auto param4 = model->addOperand(&type8); 185 auto param5 = model->addOperand(&type8); 186 auto param6 = model->addOperand(&type8); 187 auto scoresOut = model->addOperand(&type4); 188 auto roiOut = model->addOperand(&type6); 189 auto classesOut = model->addOperand(&type5); 190 auto batchSplitOut = model->addOperand(&type5); 191 auto in = model->addOperand(&type11); 192 auto param7 = model->addOperand(&type9); 193 auto param8 = model->addOperand(&type9); 194 auto param9 = model->addOperand(&type8); 195 auto param10 = model->addOperand(&type8); 196 auto param11 = model->addOperand(&type9); 197 auto param12 = model->addOperand(&type9); 198 auto layout = model->addOperand(&type10); 199 auto featureMap = model->addOperand(&type12); 200 auto out = model->addOperand(&type12); 201 // Phase 2, operations 202 static float scores_init[] = {0.9f, 0.1f}; 203 model->setOperandValue(scores, scores_init, sizeof(float) * 2); 204 static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 205 model->setOperandValue(roi, roi_init, sizeof(float) * 8); 206 static int32_t param_init[] = {0}; 207 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 208 static float param1_init[] = {0.3f}; 209 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 210 static int32_t param2_init[] = {-1}; 211 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 212 static int32_t param3_init[] = {0}; 213 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 214 static float param4_init[] = {0.4f}; 215 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 216 static float param5_init[] = {1.0f}; 217 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 218 static float param6_init[] = {0.3f}; 219 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 220 static int32_t param7_init[] = {2}; 221 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 222 static int32_t param8_init[] = {2}; 223 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 224 static float param9_init[] = {2.0f}; 225 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 226 static float param10_init[] = {2.0f}; 227 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 228 static int32_t param11_init[] = {4}; 229 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 230 static int32_t param12_init[] = {4}; 231 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 232 static bool8 layout_init[] = {false}; 233 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 234 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 235 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 236 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 237 // Phase 3, inputs and outputs 238 model->identifyInputsAndOutputs( 239 {in}, 240 {scoresOut, classesOut, out}); 241 // Phase 4: set relaxed execution 242 model->relaxComputationFloat32toFloat16(true); 243 assert(model->isValid()); 244 } 245 246 inline bool is_ignored_zero_sized_relaxed(int i) { 247 static std::set<int> ignore = {}; 248 return ignore.find(i) != ignore.end(); 249 } 250 251 void CreateModel_zero_sized_quant8(Model *model) { 252 OperandType type10(Type::BOOL, {}); 253 OperandType type14(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128); 254 OperandType type15(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128); 255 OperandType type16(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.00390625f, 0); 256 OperandType type17(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); 257 OperandType type18(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); 258 OperandType type19(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); 259 OperandType type20(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); 260 OperandType type5(Type::TENSOR_INT32, {0}); 261 OperandType type7(Type::TENSOR_INT32, {1}); 262 OperandType type8(Type::FLOAT32, {}); 263 OperandType type9(Type::INT32, {}); 264 // Phase 1, operands 265 auto scores = model->addOperand(&type19); 266 auto roi = model->addOperand(&type17); 267 auto param = model->addOperand(&type7); 268 auto param1 = model->addOperand(&type8); 269 auto param2 = model->addOperand(&type9); 270 auto param3 = model->addOperand(&type9); 271 auto param4 = model->addOperand(&type8); 272 auto param5 = model->addOperand(&type8); 273 auto param6 = model->addOperand(&type8); 274 auto scoresOut = model->addOperand(&type20); 275 auto roiOut = model->addOperand(&type18); 276 auto classesOut = model->addOperand(&type5); 277 auto batchSplitOut = model->addOperand(&type5); 278 auto in = model->addOperand(&type15); 279 auto param7 = model->addOperand(&type9); 280 auto param8 = model->addOperand(&type9); 281 auto param9 = model->addOperand(&type8); 282 auto param10 = model->addOperand(&type8); 283 auto param11 = model->addOperand(&type9); 284 auto param12 = model->addOperand(&type9); 285 auto layout = model->addOperand(&type10); 286 auto featureMap = model->addOperand(&type14); 287 auto out = model->addOperand(&type16); 288 // Phase 2, operations 289 static uint8_t scores_init[] = {137, 129}; 290 model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); 291 static uint16_t roi_init[] = {8, 8, 80, 80, 0, 0, 80, 80}; 292 model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); 293 static int32_t param_init[] = {0}; 294 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 295 static float param1_init[] = {0.3f}; 296 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 297 static int32_t param2_init[] = {-1}; 298 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 299 static int32_t param3_init[] = {0}; 300 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 301 static float param4_init[] = {0.4f}; 302 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 303 static float param5_init[] = {1.0f}; 304 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 305 static float param6_init[] = {0.3f}; 306 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 307 static int32_t param7_init[] = {2}; 308 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 309 static int32_t param8_init[] = {2}; 310 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 311 static float param9_init[] = {2.0f}; 312 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 313 static float param10_init[] = {2.0f}; 314 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 315 static int32_t param11_init[] = {4}; 316 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 317 static int32_t param12_init[] = {4}; 318 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 319 static bool8 layout_init[] = {false}; 320 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 321 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 322 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 323 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 324 // Phase 3, inputs and outputs 325 model->identifyInputsAndOutputs( 326 {in}, 327 {scoresOut, classesOut, out}); 328 assert(model->isValid()); 329 } 330 331 inline bool is_ignored_zero_sized_quant8(int i) { 332 static std::set<int> ignore = {}; 333 return ignore.find(i) != ignore.end(); 334 } 335 336 void CreateModel_zero_sized_float16(Model *model) { 337 OperandType type10(Type::BOOL, {}); 338 OperandType type21(Type::TENSOR_FLOAT16, {0, 2, 2, 1}); 339 OperandType type22(Type::TENSOR_FLOAT16, {1, 1, 1, 1}); 340 OperandType type23(Type::FLOAT16, {}); 341 OperandType type24(Type::TENSOR_FLOAT16, {1, 8}); 342 OperandType type25(Type::TENSOR_FLOAT16, {0, 4}); 343 OperandType type26(Type::TENSOR_FLOAT16, {1, 2}); 344 OperandType type27(Type::TENSOR_FLOAT16, {0}); 345 OperandType type5(Type::TENSOR_INT32, {0}); 346 OperandType type7(Type::TENSOR_INT32, {1}); 347 OperandType type9(Type::INT32, {}); 348 // Phase 1, operands 349 auto scores = model->addOperand(&type26); 350 auto roi = model->addOperand(&type24); 351 auto param = model->addOperand(&type7); 352 auto param1 = model->addOperand(&type23); 353 auto param2 = model->addOperand(&type9); 354 auto param3 = model->addOperand(&type9); 355 auto param4 = model->addOperand(&type23); 356 auto param5 = model->addOperand(&type23); 357 auto param6 = model->addOperand(&type23); 358 auto scoresOut = model->addOperand(&type27); 359 auto roiOut = model->addOperand(&type25); 360 auto classesOut = model->addOperand(&type5); 361 auto batchSplitOut = model->addOperand(&type5); 362 auto in = model->addOperand(&type22); 363 auto param7 = model->addOperand(&type9); 364 auto param8 = model->addOperand(&type9); 365 auto param9 = model->addOperand(&type23); 366 auto param10 = model->addOperand(&type23); 367 auto param11 = model->addOperand(&type9); 368 auto param12 = model->addOperand(&type9); 369 auto layout = model->addOperand(&type10); 370 auto featureMap = model->addOperand(&type21); 371 auto out = model->addOperand(&type21); 372 // Phase 2, operations 373 static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f}; 374 model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2); 375 static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 376 model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8); 377 static int32_t param_init[] = {0}; 378 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 379 static _Float16 param1_init[] = {0.30000001192092896f}; 380 model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1); 381 static int32_t param2_init[] = {-1}; 382 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 383 static int32_t param3_init[] = {0}; 384 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 385 static _Float16 param4_init[] = {0.4000000059604645f}; 386 model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1); 387 static _Float16 param5_init[] = {1.0f}; 388 model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1); 389 static _Float16 param6_init[] = {0.30000001192092896f}; 390 model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1); 391 static int32_t param7_init[] = {2}; 392 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 393 static int32_t param8_init[] = {2}; 394 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 395 static _Float16 param9_init[] = {2.0f}; 396 model->setOperandValue(param9, param9_init, sizeof(_Float16) * 1); 397 static _Float16 param10_init[] = {2.0f}; 398 model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1); 399 static int32_t param11_init[] = {4}; 400 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 401 static int32_t param12_init[] = {4}; 402 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 403 static bool8 layout_init[] = {false}; 404 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 405 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 406 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 407 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 408 // Phase 3, inputs and outputs 409 model->identifyInputsAndOutputs( 410 {in}, 411 {scoresOut, classesOut, out}); 412 assert(model->isValid()); 413 } 414 415 inline bool is_ignored_zero_sized_float16(int i) { 416 static std::set<int> ignore = {}; 417 return ignore.find(i) != ignore.end(); 418 } 419 420 void CreateModel_zero_sized_dynamic_output_shape(Model *model) { 421 OperandType type10(Type::BOOL, {}); 422 OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1}); 423 OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1}); 424 OperandType type2(Type::TENSOR_FLOAT32, {1, 2}); 425 OperandType type28(Type::TENSOR_FLOAT32, {0, 0, 0, 0}); 426 OperandType type3(Type::TENSOR_FLOAT32, {1, 8}); 427 OperandType type4(Type::TENSOR_FLOAT32, {0}); 428 OperandType type5(Type::TENSOR_INT32, {0}); 429 OperandType type6(Type::TENSOR_FLOAT32, {0, 4}); 430 OperandType type7(Type::TENSOR_INT32, {1}); 431 OperandType type8(Type::FLOAT32, {}); 432 OperandType type9(Type::INT32, {}); 433 // Phase 1, operands 434 auto scores = model->addOperand(&type2); 435 auto roi = model->addOperand(&type3); 436 auto param = model->addOperand(&type7); 437 auto param1 = model->addOperand(&type8); 438 auto param2 = model->addOperand(&type9); 439 auto param3 = model->addOperand(&type9); 440 auto param4 = model->addOperand(&type8); 441 auto param5 = model->addOperand(&type8); 442 auto param6 = model->addOperand(&type8); 443 auto scoresOut = model->addOperand(&type4); 444 auto roiOut = model->addOperand(&type6); 445 auto classesOut = model->addOperand(&type5); 446 auto batchSplitOut = model->addOperand(&type5); 447 auto in = model->addOperand(&type11); 448 auto param7 = model->addOperand(&type9); 449 auto param8 = model->addOperand(&type9); 450 auto param9 = model->addOperand(&type8); 451 auto param10 = model->addOperand(&type8); 452 auto param11 = model->addOperand(&type9); 453 auto param12 = model->addOperand(&type9); 454 auto layout = model->addOperand(&type10); 455 auto featureMap = model->addOperand(&type12); 456 auto out = model->addOperand(&type28); 457 // Phase 2, operations 458 static float scores_init[] = {0.9f, 0.1f}; 459 model->setOperandValue(scores, scores_init, sizeof(float) * 2); 460 static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 461 model->setOperandValue(roi, roi_init, sizeof(float) * 8); 462 static int32_t param_init[] = {0}; 463 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 464 static float param1_init[] = {0.3f}; 465 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 466 static int32_t param2_init[] = {-1}; 467 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 468 static int32_t param3_init[] = {0}; 469 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 470 static float param4_init[] = {0.4f}; 471 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 472 static float param5_init[] = {1.0f}; 473 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 474 static float param6_init[] = {0.3f}; 475 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 476 static int32_t param7_init[] = {2}; 477 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 478 static int32_t param8_init[] = {2}; 479 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 480 static float param9_init[] = {2.0f}; 481 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 482 static float param10_init[] = {2.0f}; 483 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 484 static int32_t param11_init[] = {4}; 485 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 486 static int32_t param12_init[] = {4}; 487 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 488 static bool8 layout_init[] = {false}; 489 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 490 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 491 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 492 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 493 // Phase 3, inputs and outputs 494 model->identifyInputsAndOutputs( 495 {in}, 496 {scoresOut, classesOut, out}); 497 assert(model->isValid()); 498 } 499 500 inline bool is_ignored_zero_sized_dynamic_output_shape(int i) { 501 static std::set<int> ignore = {}; 502 return ignore.find(i) != ignore.end(); 503 } 504 505 void CreateModel_zero_sized_dynamic_output_shape_relaxed(Model *model) { 506 OperandType type10(Type::BOOL, {}); 507 OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1}); 508 OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1}); 509 OperandType type2(Type::TENSOR_FLOAT32, {1, 2}); 510 OperandType type28(Type::TENSOR_FLOAT32, {0, 0, 0, 0}); 511 OperandType type3(Type::TENSOR_FLOAT32, {1, 8}); 512 OperandType type4(Type::TENSOR_FLOAT32, {0}); 513 OperandType type5(Type::TENSOR_INT32, {0}); 514 OperandType type6(Type::TENSOR_FLOAT32, {0, 4}); 515 OperandType type7(Type::TENSOR_INT32, {1}); 516 OperandType type8(Type::FLOAT32, {}); 517 OperandType type9(Type::INT32, {}); 518 // Phase 1, operands 519 auto scores = model->addOperand(&type2); 520 auto roi = model->addOperand(&type3); 521 auto param = model->addOperand(&type7); 522 auto param1 = model->addOperand(&type8); 523 auto param2 = model->addOperand(&type9); 524 auto param3 = model->addOperand(&type9); 525 auto param4 = model->addOperand(&type8); 526 auto param5 = model->addOperand(&type8); 527 auto param6 = model->addOperand(&type8); 528 auto scoresOut = model->addOperand(&type4); 529 auto roiOut = model->addOperand(&type6); 530 auto classesOut = model->addOperand(&type5); 531 auto batchSplitOut = model->addOperand(&type5); 532 auto in = model->addOperand(&type11); 533 auto param7 = model->addOperand(&type9); 534 auto param8 = model->addOperand(&type9); 535 auto param9 = model->addOperand(&type8); 536 auto param10 = model->addOperand(&type8); 537 auto param11 = model->addOperand(&type9); 538 auto param12 = model->addOperand(&type9); 539 auto layout = model->addOperand(&type10); 540 auto featureMap = model->addOperand(&type12); 541 auto out = model->addOperand(&type28); 542 // Phase 2, operations 543 static float scores_init[] = {0.9f, 0.1f}; 544 model->setOperandValue(scores, scores_init, sizeof(float) * 2); 545 static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 546 model->setOperandValue(roi, roi_init, sizeof(float) * 8); 547 static int32_t param_init[] = {0}; 548 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 549 static float param1_init[] = {0.3f}; 550 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 551 static int32_t param2_init[] = {-1}; 552 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 553 static int32_t param3_init[] = {0}; 554 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 555 static float param4_init[] = {0.4f}; 556 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 557 static float param5_init[] = {1.0f}; 558 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 559 static float param6_init[] = {0.3f}; 560 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 561 static int32_t param7_init[] = {2}; 562 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 563 static int32_t param8_init[] = {2}; 564 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 565 static float param9_init[] = {2.0f}; 566 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 567 static float param10_init[] = {2.0f}; 568 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 569 static int32_t param11_init[] = {4}; 570 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 571 static int32_t param12_init[] = {4}; 572 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 573 static bool8 layout_init[] = {false}; 574 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 575 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 576 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 577 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 578 // Phase 3, inputs and outputs 579 model->identifyInputsAndOutputs( 580 {in}, 581 {scoresOut, classesOut, out}); 582 // Phase 4: set relaxed execution 583 model->relaxComputationFloat32toFloat16(true); 584 assert(model->isValid()); 585 } 586 587 inline bool is_ignored_zero_sized_dynamic_output_shape_relaxed(int i) { 588 static std::set<int> ignore = {}; 589 return ignore.find(i) != ignore.end(); 590 } 591 592 void CreateModel_zero_sized_dynamic_output_shape_quant8(Model *model) { 593 OperandType type10(Type::BOOL, {}); 594 OperandType type14(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128); 595 OperandType type15(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128); 596 OperandType type17(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); 597 OperandType type18(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); 598 OperandType type19(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); 599 OperandType type20(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); 600 OperandType type29(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.00390625f, 0); 601 OperandType type5(Type::TENSOR_INT32, {0}); 602 OperandType type7(Type::TENSOR_INT32, {1}); 603 OperandType type8(Type::FLOAT32, {}); 604 OperandType type9(Type::INT32, {}); 605 // Phase 1, operands 606 auto scores = model->addOperand(&type19); 607 auto roi = model->addOperand(&type17); 608 auto param = model->addOperand(&type7); 609 auto param1 = model->addOperand(&type8); 610 auto param2 = model->addOperand(&type9); 611 auto param3 = model->addOperand(&type9); 612 auto param4 = model->addOperand(&type8); 613 auto param5 = model->addOperand(&type8); 614 auto param6 = model->addOperand(&type8); 615 auto scoresOut = model->addOperand(&type20); 616 auto roiOut = model->addOperand(&type18); 617 auto classesOut = model->addOperand(&type5); 618 auto batchSplitOut = model->addOperand(&type5); 619 auto in = model->addOperand(&type15); 620 auto param7 = model->addOperand(&type9); 621 auto param8 = model->addOperand(&type9); 622 auto param9 = model->addOperand(&type8); 623 auto param10 = model->addOperand(&type8); 624 auto param11 = model->addOperand(&type9); 625 auto param12 = model->addOperand(&type9); 626 auto layout = model->addOperand(&type10); 627 auto featureMap = model->addOperand(&type14); 628 auto out = model->addOperand(&type29); 629 // Phase 2, operations 630 static uint8_t scores_init[] = {137, 129}; 631 model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); 632 static uint16_t roi_init[] = {8, 8, 80, 80, 0, 0, 80, 80}; 633 model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); 634 static int32_t param_init[] = {0}; 635 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 636 static float param1_init[] = {0.3f}; 637 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 638 static int32_t param2_init[] = {-1}; 639 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 640 static int32_t param3_init[] = {0}; 641 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 642 static float param4_init[] = {0.4f}; 643 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 644 static float param5_init[] = {1.0f}; 645 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 646 static float param6_init[] = {0.3f}; 647 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 648 static int32_t param7_init[] = {2}; 649 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 650 static int32_t param8_init[] = {2}; 651 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 652 static float param9_init[] = {2.0f}; 653 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 654 static float param10_init[] = {2.0f}; 655 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 656 static int32_t param11_init[] = {4}; 657 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 658 static int32_t param12_init[] = {4}; 659 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 660 static bool8 layout_init[] = {false}; 661 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 662 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 663 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 664 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 665 // Phase 3, inputs and outputs 666 model->identifyInputsAndOutputs( 667 {in}, 668 {scoresOut, classesOut, out}); 669 assert(model->isValid()); 670 } 671 672 inline bool is_ignored_zero_sized_dynamic_output_shape_quant8(int i) { 673 static std::set<int> ignore = {}; 674 return ignore.find(i) != ignore.end(); 675 } 676 677 void CreateModel_zero_sized_dynamic_output_shape_float16(Model *model) { 678 OperandType type10(Type::BOOL, {}); 679 OperandType type13(Type::TENSOR_FLOAT16, {0, 0, 0, 0}); 680 OperandType type21(Type::TENSOR_FLOAT16, {0, 2, 2, 1}); 681 OperandType type22(Type::TENSOR_FLOAT16, {1, 1, 1, 1}); 682 OperandType type23(Type::FLOAT16, {}); 683 OperandType type24(Type::TENSOR_FLOAT16, {1, 8}); 684 OperandType type25(Type::TENSOR_FLOAT16, {0, 4}); 685 OperandType type26(Type::TENSOR_FLOAT16, {1, 2}); 686 OperandType type30(Type::TENSOR_FLOAT16, {0}); 687 OperandType type5(Type::TENSOR_INT32, {0}); 688 OperandType type7(Type::TENSOR_INT32, {1}); 689 OperandType type9(Type::INT32, {}); 690 // Phase 1, operands 691 auto scores = model->addOperand(&type26); 692 auto roi = model->addOperand(&type24); 693 auto param = model->addOperand(&type7); 694 auto param1 = model->addOperand(&type23); 695 auto param2 = model->addOperand(&type9); 696 auto param3 = model->addOperand(&type9); 697 auto param4 = model->addOperand(&type23); 698 auto param5 = model->addOperand(&type23); 699 auto param6 = model->addOperand(&type23); 700 auto scoresOut = model->addOperand(&type30); 701 auto roiOut = model->addOperand(&type25); 702 auto classesOut = model->addOperand(&type5); 703 auto batchSplitOut = model->addOperand(&type5); 704 auto in = model->addOperand(&type22); 705 auto param7 = model->addOperand(&type9); 706 auto param8 = model->addOperand(&type9); 707 auto param9 = model->addOperand(&type23); 708 auto param10 = model->addOperand(&type23); 709 auto param11 = model->addOperand(&type9); 710 auto param12 = model->addOperand(&type9); 711 auto layout = model->addOperand(&type10); 712 auto featureMap = model->addOperand(&type21); 713 auto out = model->addOperand(&type13); 714 // Phase 2, operations 715 static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f}; 716 model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2); 717 static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 718 model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8); 719 static int32_t param_init[] = {0}; 720 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 721 static _Float16 param1_init[] = {0.30000001192092896f}; 722 model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1); 723 static int32_t param2_init[] = {-1}; 724 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 725 static int32_t param3_init[] = {0}; 726 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 727 static _Float16 param4_init[] = {0.4000000059604645f}; 728 model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1); 729 static _Float16 param5_init[] = {1.0f}; 730 model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1); 731 static _Float16 param6_init[] = {0.30000001192092896f}; 732 model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1); 733 static int32_t param7_init[] = {2}; 734 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 735 static int32_t param8_init[] = {2}; 736 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 737 static _Float16 param9_init[] = {2.0f}; 738 model->setOperandValue(param9, param9_init, sizeof(_Float16) * 1); 739 static _Float16 param10_init[] = {2.0f}; 740 model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1); 741 static int32_t param11_init[] = {4}; 742 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 743 static int32_t param12_init[] = {4}; 744 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 745 static bool8 layout_init[] = {false}; 746 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 747 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 748 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 749 model->addOperation(ANEURALNETWORKS_LOGISTIC, {featureMap}, {out}); 750 // Phase 3, inputs and outputs 751 model->identifyInputsAndOutputs( 752 {in}, 753 {scoresOut, classesOut, out}); 754 assert(model->isValid()); 755 } 756 757 inline bool is_ignored_zero_sized_dynamic_output_shape_float16(int i) { 758 static std::set<int> ignore = {}; 759 return ignore.find(i) != ignore.end(); 760 } 761 762
