1 // clang-format off 2 // Generated file (from: relu6_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 op2 = model->addOperand(&type0); 8 // Phase 2, operations 9 model->addOperation(ANEURALNETWORKS_RELU6, {op1}, {op2}); 10 // Phase 3, inputs and outputs 11 model->identifyInputsAndOutputs( 12 {op1}, 13 {op2}); 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 op2 = model->addOperand(&type13); 28 // Phase 2, operations 29 model->addOperation(ANEURALNETWORKS_RELU6, {op1}, {op2}); 30 // Phase 3, inputs and outputs 31 model->identifyInputsAndOutputs( 32 {op1}, 33 {op2}); 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, 26, 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_RELU6, {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, 26, 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_RELU6, {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_RELU6, {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_RELU6, {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_QUANT16_ASYMM, {1, 8}, 0.125f, 0); 256 OperandType type17(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); 257 OperandType type18(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); 258 OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); 259 OperandType type5(Type::TENSOR_INT32, {0}); 260 OperandType type7(Type::TENSOR_INT32, {1}); 261 OperandType type8(Type::FLOAT32, {}); 262 OperandType type9(Type::INT32, {}); 263 // Phase 1, operands 264 auto scores = model->addOperand(&type18); 265 auto roi = model->addOperand(&type16); 266 auto param = model->addOperand(&type7); 267 auto param1 = model->addOperand(&type8); 268 auto param2 = model->addOperand(&type9); 269 auto param3 = model->addOperand(&type9); 270 auto param4 = model->addOperand(&type8); 271 auto param5 = model->addOperand(&type8); 272 auto param6 = model->addOperand(&type8); 273 auto scoresOut = model->addOperand(&type19); 274 auto roiOut = model->addOperand(&type17); 275 auto classesOut = model->addOperand(&type5); 276 auto batchSplitOut = model->addOperand(&type5); 277 auto in = model->addOperand(&type15); 278 auto param7 = model->addOperand(&type9); 279 auto param8 = model->addOperand(&type9); 280 auto param9 = model->addOperand(&type8); 281 auto param10 = model->addOperand(&type8); 282 auto param11 = model->addOperand(&type9); 283 auto param12 = model->addOperand(&type9); 284 auto layout = model->addOperand(&type10); 285 auto featureMap = model->addOperand(&type14); 286 auto out = model->addOperand(&type14); 287 // Phase 2, operations 288 static uint8_t scores_init[] = {137, 129}; 289 model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); 290 static uint16_t roi_init[] = {8, 8, 80, 80, 0, 0, 80, 80}; 291 model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); 292 static int32_t param_init[] = {0}; 293 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 294 static float param1_init[] = {0.3f}; 295 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 296 static int32_t param2_init[] = {-1}; 297 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 298 static int32_t param3_init[] = {0}; 299 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 300 static float param4_init[] = {0.4f}; 301 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 302 static float param5_init[] = {1.0f}; 303 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 304 static float param6_init[] = {0.3f}; 305 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 306 static int32_t param7_init[] = {2}; 307 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 308 static int32_t param8_init[] = {2}; 309 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 310 static float param9_init[] = {2.0f}; 311 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 312 static float param10_init[] = {2.0f}; 313 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 314 static int32_t param11_init[] = {4}; 315 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 316 static int32_t param12_init[] = {4}; 317 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 318 static bool8 layout_init[] = {false}; 319 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 320 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 321 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 322 model->addOperation(ANEURALNETWORKS_RELU6, {featureMap}, {out}); 323 // Phase 3, inputs and outputs 324 model->identifyInputsAndOutputs( 325 {in}, 326 {scoresOut, classesOut, out}); 327 assert(model->isValid()); 328 } 329 330 inline bool is_ignored_zero_sized_quant8(int i) { 331 static std::set<int> ignore = {}; 332 return ignore.find(i) != ignore.end(); 333 } 334 335 void CreateModel_zero_sized_float16(Model *model) { 336 OperandType type10(Type::BOOL, {}); 337 OperandType type20(Type::TENSOR_FLOAT16, {0, 2, 2, 1}); 338 OperandType type21(Type::TENSOR_FLOAT16, {1, 1, 1, 1}); 339 OperandType type22(Type::FLOAT16, {}); 340 OperandType type23(Type::TENSOR_FLOAT16, {1, 8}); 341 OperandType type24(Type::TENSOR_FLOAT16, {0, 4}); 342 OperandType type25(Type::TENSOR_FLOAT16, {1, 2}); 343 OperandType type26(Type::TENSOR_FLOAT16, {0}); 344 OperandType type5(Type::TENSOR_INT32, {0}); 345 OperandType type7(Type::TENSOR_INT32, {1}); 346 OperandType type9(Type::INT32, {}); 347 // Phase 1, operands 348 auto scores = model->addOperand(&type25); 349 auto roi = model->addOperand(&type23); 350 auto param = model->addOperand(&type7); 351 auto param1 = model->addOperand(&type22); 352 auto param2 = model->addOperand(&type9); 353 auto param3 = model->addOperand(&type9); 354 auto param4 = model->addOperand(&type22); 355 auto param5 = model->addOperand(&type22); 356 auto param6 = model->addOperand(&type22); 357 auto scoresOut = model->addOperand(&type26); 358 auto roiOut = model->addOperand(&type24); 359 auto classesOut = model->addOperand(&type5); 360 auto batchSplitOut = model->addOperand(&type5); 361 auto in = model->addOperand(&type21); 362 auto param7 = model->addOperand(&type9); 363 auto param8 = model->addOperand(&type9); 364 auto param9 = model->addOperand(&type22); 365 auto param10 = model->addOperand(&type22); 366 auto param11 = model->addOperand(&type9); 367 auto param12 = model->addOperand(&type9); 368 auto layout = model->addOperand(&type10); 369 auto featureMap = model->addOperand(&type20); 370 auto out = model->addOperand(&type20); 371 // Phase 2, operations 372 static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f}; 373 model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2); 374 static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 375 model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8); 376 static int32_t param_init[] = {0}; 377 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 378 static _Float16 param1_init[] = {0.30000001192092896f}; 379 model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1); 380 static int32_t param2_init[] = {-1}; 381 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 382 static int32_t param3_init[] = {0}; 383 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 384 static _Float16 param4_init[] = {0.4000000059604645f}; 385 model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1); 386 static _Float16 param5_init[] = {1.0f}; 387 model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1); 388 static _Float16 param6_init[] = {0.30000001192092896f}; 389 model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1); 390 static int32_t param7_init[] = {2}; 391 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 392 static int32_t param8_init[] = {2}; 393 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 394 static _Float16 param9_init[] = {2.0f}; 395 model->setOperandValue(param9, param9_init, sizeof(_Float16) * 1); 396 static _Float16 param10_init[] = {2.0f}; 397 model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1); 398 static int32_t param11_init[] = {4}; 399 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 400 static int32_t param12_init[] = {4}; 401 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 402 static bool8 layout_init[] = {false}; 403 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 404 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 405 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 406 model->addOperation(ANEURALNETWORKS_RELU6, {featureMap}, {out}); 407 // Phase 3, inputs and outputs 408 model->identifyInputsAndOutputs( 409 {in}, 410 {scoresOut, classesOut, out}); 411 assert(model->isValid()); 412 } 413 414 inline bool is_ignored_zero_sized_float16(int i) { 415 static std::set<int> ignore = {}; 416 return ignore.find(i) != ignore.end(); 417 } 418 419 void CreateModel_zero_sized_dynamic_output_shape(Model *model) { 420 OperandType type10(Type::BOOL, {}); 421 OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1}); 422 OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1}); 423 OperandType type2(Type::TENSOR_FLOAT32, {1, 2}); 424 OperandType type27(Type::TENSOR_FLOAT32, {0, 0, 0, 0}); 425 OperandType type3(Type::TENSOR_FLOAT32, {1, 8}); 426 OperandType type4(Type::TENSOR_FLOAT32, {0}); 427 OperandType type5(Type::TENSOR_INT32, {0}); 428 OperandType type6(Type::TENSOR_FLOAT32, {0, 4}); 429 OperandType type7(Type::TENSOR_INT32, {1}); 430 OperandType type8(Type::FLOAT32, {}); 431 OperandType type9(Type::INT32, {}); 432 // Phase 1, operands 433 auto scores = model->addOperand(&type2); 434 auto roi = model->addOperand(&type3); 435 auto param = model->addOperand(&type7); 436 auto param1 = model->addOperand(&type8); 437 auto param2 = model->addOperand(&type9); 438 auto param3 = model->addOperand(&type9); 439 auto param4 = model->addOperand(&type8); 440 auto param5 = model->addOperand(&type8); 441 auto param6 = model->addOperand(&type8); 442 auto scoresOut = model->addOperand(&type4); 443 auto roiOut = model->addOperand(&type6); 444 auto classesOut = model->addOperand(&type5); 445 auto batchSplitOut = model->addOperand(&type5); 446 auto in = model->addOperand(&type11); 447 auto param7 = model->addOperand(&type9); 448 auto param8 = model->addOperand(&type9); 449 auto param9 = model->addOperand(&type8); 450 auto param10 = model->addOperand(&type8); 451 auto param11 = model->addOperand(&type9); 452 auto param12 = model->addOperand(&type9); 453 auto layout = model->addOperand(&type10); 454 auto featureMap = model->addOperand(&type12); 455 auto out = model->addOperand(&type27); 456 // Phase 2, operations 457 static float scores_init[] = {0.9f, 0.1f}; 458 model->setOperandValue(scores, scores_init, sizeof(float) * 2); 459 static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 460 model->setOperandValue(roi, roi_init, sizeof(float) * 8); 461 static int32_t param_init[] = {0}; 462 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 463 static float param1_init[] = {0.3f}; 464 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 465 static int32_t param2_init[] = {-1}; 466 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 467 static int32_t param3_init[] = {0}; 468 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 469 static float param4_init[] = {0.4f}; 470 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 471 static float param5_init[] = {1.0f}; 472 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 473 static float param6_init[] = {0.3f}; 474 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 475 static int32_t param7_init[] = {2}; 476 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 477 static int32_t param8_init[] = {2}; 478 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 479 static float param9_init[] = {2.0f}; 480 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 481 static float param10_init[] = {2.0f}; 482 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 483 static int32_t param11_init[] = {4}; 484 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 485 static int32_t param12_init[] = {4}; 486 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 487 static bool8 layout_init[] = {false}; 488 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 489 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 490 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 491 model->addOperation(ANEURALNETWORKS_RELU6, {featureMap}, {out}); 492 // Phase 3, inputs and outputs 493 model->identifyInputsAndOutputs( 494 {in}, 495 {scoresOut, classesOut, out}); 496 assert(model->isValid()); 497 } 498 499 inline bool is_ignored_zero_sized_dynamic_output_shape(int i) { 500 static std::set<int> ignore = {}; 501 return ignore.find(i) != ignore.end(); 502 } 503 504 void CreateModel_zero_sized_dynamic_output_shape_relaxed(Model *model) { 505 OperandType type10(Type::BOOL, {}); 506 OperandType type11(Type::TENSOR_FLOAT32, {1, 1, 1, 1}); 507 OperandType type12(Type::TENSOR_FLOAT32, {0, 2, 2, 1}); 508 OperandType type2(Type::TENSOR_FLOAT32, {1, 2}); 509 OperandType type27(Type::TENSOR_FLOAT32, {0, 0, 0, 0}); 510 OperandType type3(Type::TENSOR_FLOAT32, {1, 8}); 511 OperandType type4(Type::TENSOR_FLOAT32, {0}); 512 OperandType type5(Type::TENSOR_INT32, {0}); 513 OperandType type6(Type::TENSOR_FLOAT32, {0, 4}); 514 OperandType type7(Type::TENSOR_INT32, {1}); 515 OperandType type8(Type::FLOAT32, {}); 516 OperandType type9(Type::INT32, {}); 517 // Phase 1, operands 518 auto scores = model->addOperand(&type2); 519 auto roi = model->addOperand(&type3); 520 auto param = model->addOperand(&type7); 521 auto param1 = model->addOperand(&type8); 522 auto param2 = model->addOperand(&type9); 523 auto param3 = model->addOperand(&type9); 524 auto param4 = model->addOperand(&type8); 525 auto param5 = model->addOperand(&type8); 526 auto param6 = model->addOperand(&type8); 527 auto scoresOut = model->addOperand(&type4); 528 auto roiOut = model->addOperand(&type6); 529 auto classesOut = model->addOperand(&type5); 530 auto batchSplitOut = model->addOperand(&type5); 531 auto in = model->addOperand(&type11); 532 auto param7 = model->addOperand(&type9); 533 auto param8 = model->addOperand(&type9); 534 auto param9 = model->addOperand(&type8); 535 auto param10 = model->addOperand(&type8); 536 auto param11 = model->addOperand(&type9); 537 auto param12 = model->addOperand(&type9); 538 auto layout = model->addOperand(&type10); 539 auto featureMap = model->addOperand(&type12); 540 auto out = model->addOperand(&type27); 541 // Phase 2, operations 542 static float scores_init[] = {0.9f, 0.1f}; 543 model->setOperandValue(scores, scores_init, sizeof(float) * 2); 544 static float roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 545 model->setOperandValue(roi, roi_init, sizeof(float) * 8); 546 static int32_t param_init[] = {0}; 547 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 548 static float param1_init[] = {0.3f}; 549 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 550 static int32_t param2_init[] = {-1}; 551 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 552 static int32_t param3_init[] = {0}; 553 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 554 static float param4_init[] = {0.4f}; 555 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 556 static float param5_init[] = {1.0f}; 557 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 558 static float param6_init[] = {0.3f}; 559 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 560 static int32_t param7_init[] = {2}; 561 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 562 static int32_t param8_init[] = {2}; 563 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 564 static float param9_init[] = {2.0f}; 565 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 566 static float param10_init[] = {2.0f}; 567 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 568 static int32_t param11_init[] = {4}; 569 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 570 static int32_t param12_init[] = {4}; 571 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 572 static bool8 layout_init[] = {false}; 573 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 574 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 575 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 576 model->addOperation(ANEURALNETWORKS_RELU6, {featureMap}, {out}); 577 // Phase 3, inputs and outputs 578 model->identifyInputsAndOutputs( 579 {in}, 580 {scoresOut, classesOut, out}); 581 // Phase 4: set relaxed execution 582 model->relaxComputationFloat32toFloat16(true); 583 assert(model->isValid()); 584 } 585 586 inline bool is_ignored_zero_sized_dynamic_output_shape_relaxed(int i) { 587 static std::set<int> ignore = {}; 588 return ignore.find(i) != ignore.end(); 589 } 590 591 void CreateModel_zero_sized_dynamic_output_shape_quant8(Model *model) { 592 OperandType type10(Type::BOOL, {}); 593 OperandType type14(Type::TENSOR_QUANT8_ASYMM, {0, 2, 2, 1}, 0.1f, 128); 594 OperandType type15(Type::TENSOR_QUANT8_ASYMM, {1, 1, 1, 1}, 0.1f, 128); 595 OperandType type16(Type::TENSOR_QUANT16_ASYMM, {1, 8}, 0.125f, 0); 596 OperandType type17(Type::TENSOR_QUANT16_ASYMM, {0, 4}, 0.125f, 0); 597 OperandType type18(Type::TENSOR_QUANT8_ASYMM, {1, 2}, 0.1f, 128); 598 OperandType type19(Type::TENSOR_QUANT8_ASYMM, {0}, 0.1f, 128); 599 OperandType type28(Type::TENSOR_QUANT8_ASYMM, {0, 0, 0, 0}, 0.1f, 128); 600 OperandType type5(Type::TENSOR_INT32, {0}); 601 OperandType type7(Type::TENSOR_INT32, {1}); 602 OperandType type8(Type::FLOAT32, {}); 603 OperandType type9(Type::INT32, {}); 604 // Phase 1, operands 605 auto scores = model->addOperand(&type18); 606 auto roi = model->addOperand(&type16); 607 auto param = model->addOperand(&type7); 608 auto param1 = model->addOperand(&type8); 609 auto param2 = model->addOperand(&type9); 610 auto param3 = model->addOperand(&type9); 611 auto param4 = model->addOperand(&type8); 612 auto param5 = model->addOperand(&type8); 613 auto param6 = model->addOperand(&type8); 614 auto scoresOut = model->addOperand(&type19); 615 auto roiOut = model->addOperand(&type17); 616 auto classesOut = model->addOperand(&type5); 617 auto batchSplitOut = model->addOperand(&type5); 618 auto in = model->addOperand(&type15); 619 auto param7 = model->addOperand(&type9); 620 auto param8 = model->addOperand(&type9); 621 auto param9 = model->addOperand(&type8); 622 auto param10 = model->addOperand(&type8); 623 auto param11 = model->addOperand(&type9); 624 auto param12 = model->addOperand(&type9); 625 auto layout = model->addOperand(&type10); 626 auto featureMap = model->addOperand(&type14); 627 auto out = model->addOperand(&type28); 628 // Phase 2, operations 629 static uint8_t scores_init[] = {137, 129}; 630 model->setOperandValue(scores, scores_init, sizeof(uint8_t) * 2); 631 static uint16_t roi_init[] = {8, 8, 80, 80, 0, 0, 80, 80}; 632 model->setOperandValue(roi, roi_init, sizeof(uint16_t) * 8); 633 static int32_t param_init[] = {0}; 634 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 635 static float param1_init[] = {0.3f}; 636 model->setOperandValue(param1, param1_init, sizeof(float) * 1); 637 static int32_t param2_init[] = {-1}; 638 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 639 static int32_t param3_init[] = {0}; 640 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 641 static float param4_init[] = {0.4f}; 642 model->setOperandValue(param4, param4_init, sizeof(float) * 1); 643 static float param5_init[] = {1.0f}; 644 model->setOperandValue(param5, param5_init, sizeof(float) * 1); 645 static float param6_init[] = {0.3f}; 646 model->setOperandValue(param6, param6_init, sizeof(float) * 1); 647 static int32_t param7_init[] = {2}; 648 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 649 static int32_t param8_init[] = {2}; 650 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 651 static float param9_init[] = {2.0f}; 652 model->setOperandValue(param9, param9_init, sizeof(float) * 1); 653 static float param10_init[] = {2.0f}; 654 model->setOperandValue(param10, param10_init, sizeof(float) * 1); 655 static int32_t param11_init[] = {4}; 656 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 657 static int32_t param12_init[] = {4}; 658 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 659 static bool8 layout_init[] = {false}; 660 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 661 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 662 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 663 model->addOperation(ANEURALNETWORKS_RELU6, {featureMap}, {out}); 664 // Phase 3, inputs and outputs 665 model->identifyInputsAndOutputs( 666 {in}, 667 {scoresOut, classesOut, out}); 668 assert(model->isValid()); 669 } 670 671 inline bool is_ignored_zero_sized_dynamic_output_shape_quant8(int i) { 672 static std::set<int> ignore = {}; 673 return ignore.find(i) != ignore.end(); 674 } 675 676 void CreateModel_zero_sized_dynamic_output_shape_float16(Model *model) { 677 OperandType type10(Type::BOOL, {}); 678 OperandType type13(Type::TENSOR_FLOAT16, {0, 0, 0, 0}); 679 OperandType type20(Type::TENSOR_FLOAT16, {0, 2, 2, 1}); 680 OperandType type21(Type::TENSOR_FLOAT16, {1, 1, 1, 1}); 681 OperandType type22(Type::FLOAT16, {}); 682 OperandType type23(Type::TENSOR_FLOAT16, {1, 8}); 683 OperandType type24(Type::TENSOR_FLOAT16, {0, 4}); 684 OperandType type25(Type::TENSOR_FLOAT16, {1, 2}); 685 OperandType type29(Type::TENSOR_FLOAT16, {0}); 686 OperandType type5(Type::TENSOR_INT32, {0}); 687 OperandType type7(Type::TENSOR_INT32, {1}); 688 OperandType type9(Type::INT32, {}); 689 // Phase 1, operands 690 auto scores = model->addOperand(&type25); 691 auto roi = model->addOperand(&type23); 692 auto param = model->addOperand(&type7); 693 auto param1 = model->addOperand(&type22); 694 auto param2 = model->addOperand(&type9); 695 auto param3 = model->addOperand(&type9); 696 auto param4 = model->addOperand(&type22); 697 auto param5 = model->addOperand(&type22); 698 auto param6 = model->addOperand(&type22); 699 auto scoresOut = model->addOperand(&type29); 700 auto roiOut = model->addOperand(&type24); 701 auto classesOut = model->addOperand(&type5); 702 auto batchSplitOut = model->addOperand(&type5); 703 auto in = model->addOperand(&type21); 704 auto param7 = model->addOperand(&type9); 705 auto param8 = model->addOperand(&type9); 706 auto param9 = model->addOperand(&type22); 707 auto param10 = model->addOperand(&type22); 708 auto param11 = model->addOperand(&type9); 709 auto param12 = model->addOperand(&type9); 710 auto layout = model->addOperand(&type10); 711 auto featureMap = model->addOperand(&type20); 712 auto out = model->addOperand(&type13); 713 // Phase 2, operations 714 static _Float16 scores_init[] = {0.8999999761581421f, 0.10000000149011612f}; 715 model->setOperandValue(scores, scores_init, sizeof(_Float16) * 2); 716 static _Float16 roi_init[] = {1.0f, 1.0f, 10.0f, 10.0f, 0.0f, 0.0f, 10.0f, 10.0f}; 717 model->setOperandValue(roi, roi_init, sizeof(_Float16) * 8); 718 static int32_t param_init[] = {0}; 719 model->setOperandValue(param, param_init, sizeof(int32_t) * 1); 720 static _Float16 param1_init[] = {0.30000001192092896f}; 721 model->setOperandValue(param1, param1_init, sizeof(_Float16) * 1); 722 static int32_t param2_init[] = {-1}; 723 model->setOperandValue(param2, param2_init, sizeof(int32_t) * 1); 724 static int32_t param3_init[] = {0}; 725 model->setOperandValue(param3, param3_init, sizeof(int32_t) * 1); 726 static _Float16 param4_init[] = {0.4000000059604645f}; 727 model->setOperandValue(param4, param4_init, sizeof(_Float16) * 1); 728 static _Float16 param5_init[] = {1.0f}; 729 model->setOperandValue(param5, param5_init, sizeof(_Float16) * 1); 730 static _Float16 param6_init[] = {0.30000001192092896f}; 731 model->setOperandValue(param6, param6_init, sizeof(_Float16) * 1); 732 static int32_t param7_init[] = {2}; 733 model->setOperandValue(param7, param7_init, sizeof(int32_t) * 1); 734 static int32_t param8_init[] = {2}; 735 model->setOperandValue(param8, param8_init, sizeof(int32_t) * 1); 736 static _Float16 param9_init[] = {2.0f}; 737 model->setOperandValue(param9, param9_init, sizeof(_Float16) * 1); 738 static _Float16 param10_init[] = {2.0f}; 739 model->setOperandValue(param10, param10_init, sizeof(_Float16) * 1); 740 static int32_t param11_init[] = {4}; 741 model->setOperandValue(param11, param11_init, sizeof(int32_t) * 1); 742 static int32_t param12_init[] = {4}; 743 model->setOperandValue(param12, param12_init, sizeof(int32_t) * 1); 744 static bool8 layout_init[] = {false}; 745 model->setOperandValue(layout, layout_init, sizeof(bool8) * 1); 746 model->addOperation(ANEURALNETWORKS_BOX_WITH_NMS_LIMIT, {scores, roi, param, param1, param2, param3, param4, param5, param6}, {scoresOut, roiOut, classesOut, batchSplitOut}); 747 model->addOperation(ANEURALNETWORKS_ROI_ALIGN, {in, roiOut, batchSplitOut, param7, param8, param9, param10, param11, param12, layout}, {featureMap}); 748 model->addOperation(ANEURALNETWORKS_RELU6, {featureMap}, {out}); 749 // Phase 3, inputs and outputs 750 model->identifyInputsAndOutputs( 751 {in}, 752 {scoresOut, classesOut, out}); 753 assert(model->isValid()); 754 } 755 756 inline bool is_ignored_zero_sized_dynamic_output_shape_float16(int i) { 757 static std::set<int> ignore = {}; 758 return ignore.find(i) != ignore.end(); 759 } 760 761
