surmeh01 | bceff2f | 2018-03-29 16:29:27 +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 |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 4 | // |
| 5 | |
| 6 | #include <boost/test/unit_test.hpp> |
| 7 | #include "armnnTfParser/ITfParser.hpp" |
| 8 | #include "ParserPrototxtFixture.hpp" |
| 9 | #include "Runtime.hpp" |
| 10 | #include "Network.hpp" |
| 11 | #include "Graph.hpp" |
| 12 | |
| 13 | BOOST_AUTO_TEST_SUITE(TensorflowParser) |
| 14 | |
| 15 | // In Tensorflow fully connected layers are expressed as a MatMul followed by an Add. |
| 16 | // The TfParser must detect this case and convert them to a FullyConnected layer. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 17 | struct FullyConnectedFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 18 | { |
| 19 | FullyConnectedFixture() |
| 20 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 21 | // Input = tf.placeholder(tf.float32, [1, 1], "input") |
| 22 | // Weights = tf.constant([2], tf.float32, [1, 1]) |
| 23 | // Matmul = tf.matmul(input, weights) |
| 24 | // Bias = tf.constant([1], tf.float32) |
| 25 | // Output = tf.add(matmul, bias, name="output") |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 26 | m_Prototext = R"( |
| 27 | node { |
| 28 | name: "input" |
| 29 | op: "Placeholder" |
| 30 | attr { |
| 31 | key: "dtype" |
| 32 | value { |
| 33 | type: DT_FLOAT |
| 34 | } |
| 35 | } |
| 36 | attr { |
| 37 | key: "shape" |
| 38 | value { |
| 39 | shape { |
| 40 | dim { |
| 41 | size: 1 |
| 42 | } |
| 43 | dim { |
| 44 | size: 1 |
| 45 | } |
| 46 | } |
| 47 | } |
| 48 | } |
| 49 | } |
| 50 | node { |
| 51 | name: "Const" |
| 52 | op: "Const" |
| 53 | attr { |
| 54 | key: "dtype" |
| 55 | value { |
| 56 | type: DT_FLOAT |
| 57 | } |
| 58 | } |
| 59 | attr { |
| 60 | key: "value" |
| 61 | value { |
| 62 | tensor { |
| 63 | dtype: DT_FLOAT |
| 64 | tensor_shape { |
| 65 | dim { |
| 66 | size: 1 |
| 67 | } |
| 68 | dim { |
| 69 | size: 1 |
| 70 | } |
| 71 | } |
| 72 | float_val: 2.0 |
| 73 | } |
| 74 | } |
| 75 | } |
| 76 | } |
| 77 | node { |
| 78 | name: "MatMul" |
| 79 | op: "MatMul" |
| 80 | input: "input" |
| 81 | input: "Const" |
| 82 | attr { |
| 83 | key: "T" |
| 84 | value { |
| 85 | type: DT_FLOAT |
| 86 | } |
| 87 | } |
| 88 | attr { |
| 89 | key: "transpose_a" |
| 90 | value { |
| 91 | b: false |
| 92 | } |
| 93 | } |
| 94 | attr { |
| 95 | key: "transpose_b" |
| 96 | value { |
| 97 | b: false |
| 98 | } |
| 99 | } |
| 100 | } |
| 101 | node { |
| 102 | name: "Const_1" |
| 103 | op: "Const" |
| 104 | attr { |
| 105 | key: "dtype" |
| 106 | value { |
| 107 | type: DT_FLOAT |
| 108 | } |
| 109 | } |
| 110 | attr { |
| 111 | key: "value" |
| 112 | value { |
| 113 | tensor { |
| 114 | dtype: DT_FLOAT |
| 115 | tensor_shape { |
| 116 | dim { |
| 117 | size: 1 |
| 118 | } |
| 119 | } |
| 120 | float_val: 1.0 |
| 121 | } |
| 122 | } |
| 123 | } |
| 124 | } |
| 125 | node { |
| 126 | name: "output" |
| 127 | op: "Add" |
| 128 | input: "MatMul" |
| 129 | input: "Const_1" |
| 130 | attr { |
| 131 | key: "T" |
| 132 | value { |
| 133 | type: DT_FLOAT |
| 134 | } |
| 135 | } |
| 136 | } |
| 137 | )"; |
| 138 | SetupSingleInputSingleOutput({ 1, 1 }, "input", "output"); |
| 139 | } |
| 140 | }; |
| 141 | |
| 142 | BOOST_FIXTURE_TEST_CASE(FullyConnected, FullyConnectedFixture) |
| 143 | { |
| 144 | RunTest<1>({ 3 }, { 7 }); |
| 145 | } |
| 146 | |
| 147 | // Similar to FullyConnectedFixture, but this time the MatMul's output is used by two Adds. This should result |
| 148 | // in two FullyConnected layers being created. |
| 149 | // I |
| 150 | // | |
| 151 | // M -- C |
| 152 | // / \' |
| 153 | // C-- A A -- C |
| 154 | // \ / |
| 155 | // A |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 156 | struct MatMulUsedInTwoFcFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 157 | { |
| 158 | MatMulUsedInTwoFcFixture() |
| 159 | { |
| 160 | m_Prototext = R"( |
| 161 | node { |
| 162 | name: "input" |
| 163 | op: "Placeholder" |
| 164 | attr { |
| 165 | key: "dtype" |
| 166 | value { |
| 167 | type: DT_FLOAT |
| 168 | } |
| 169 | } |
| 170 | attr { |
| 171 | key: "shape" |
| 172 | value { |
| 173 | shape { |
| 174 | dim { |
| 175 | size: 1 |
| 176 | } |
| 177 | dim { |
| 178 | size: 1 |
| 179 | } |
| 180 | } |
| 181 | } |
| 182 | } |
| 183 | } |
| 184 | node { |
| 185 | name: "Const" |
| 186 | op: "Const" |
| 187 | attr { |
| 188 | key: "dtype" |
| 189 | value { |
| 190 | type: DT_FLOAT |
| 191 | } |
| 192 | } |
| 193 | attr { |
| 194 | key: "value" |
| 195 | value { |
| 196 | tensor { |
| 197 | dtype: DT_FLOAT |
| 198 | tensor_shape { |
| 199 | dim { |
| 200 | size: 1 |
| 201 | } |
| 202 | dim { |
| 203 | size: 1 |
| 204 | } |
| 205 | } |
| 206 | float_val: 2.0 |
| 207 | } |
| 208 | } |
| 209 | } |
| 210 | } |
| 211 | node { |
| 212 | name: "MatMul" |
| 213 | op: "MatMul" |
| 214 | input: "input" |
| 215 | input: "Const" |
| 216 | attr { |
| 217 | key: "T" |
| 218 | value { |
| 219 | type: DT_FLOAT |
| 220 | } |
| 221 | } |
| 222 | attr { |
| 223 | key: "transpose_a" |
| 224 | value { |
| 225 | b: false |
| 226 | } |
| 227 | } |
| 228 | attr { |
| 229 | key: "transpose_b" |
| 230 | value { |
| 231 | b: false |
| 232 | } |
| 233 | } |
| 234 | } |
| 235 | node { |
| 236 | name: "Const_1" |
| 237 | op: "Const" |
| 238 | attr { |
| 239 | key: "dtype" |
| 240 | value { |
| 241 | type: DT_FLOAT |
| 242 | } |
| 243 | } |
| 244 | attr { |
| 245 | key: "value" |
| 246 | value { |
| 247 | tensor { |
| 248 | dtype: DT_FLOAT |
| 249 | tensor_shape { |
| 250 | dim { |
| 251 | size: 1 |
| 252 | } |
| 253 | } |
| 254 | float_val: 5.0 |
| 255 | } |
| 256 | } |
| 257 | } |
| 258 | } |
| 259 | node { |
| 260 | name: "Const_2" |
| 261 | op: "Const" |
| 262 | attr { |
| 263 | key: "dtype" |
| 264 | value { |
| 265 | type: DT_FLOAT |
| 266 | } |
| 267 | } |
| 268 | attr { |
| 269 | key: "value" |
| 270 | value { |
| 271 | tensor { |
| 272 | dtype: DT_FLOAT |
| 273 | tensor_shape { |
| 274 | dim { |
| 275 | size: 1 |
| 276 | } |
| 277 | } |
| 278 | float_val: 15.0 |
| 279 | } |
| 280 | } |
| 281 | } |
| 282 | } |
| 283 | node { |
| 284 | name: "Add" |
| 285 | op: "Add" |
| 286 | input: "MatMul" |
| 287 | input: "Const_1" |
| 288 | attr { |
| 289 | key: "T" |
| 290 | value { |
| 291 | type: DT_FLOAT |
| 292 | } |
| 293 | } |
| 294 | } |
| 295 | node { |
| 296 | name: "Add_1" |
| 297 | op: "Add" |
| 298 | input: "MatMul" |
| 299 | input: "Const_2" |
| 300 | attr { |
| 301 | key: "T" |
| 302 | value { |
| 303 | type: DT_FLOAT |
| 304 | } |
| 305 | } |
| 306 | } |
| 307 | node { |
| 308 | name: "output" |
| 309 | op: "Add" |
| 310 | input: "Add" |
| 311 | input: "Add_1" |
| 312 | attr { |
| 313 | key: "T" |
| 314 | value { |
| 315 | type: DT_FLOAT |
| 316 | } |
| 317 | } |
| 318 | } |
| 319 | )"; |
| 320 | SetupSingleInputSingleOutput({ 1, 1 }, "input", "output"); |
| 321 | } |
| 322 | }; |
| 323 | |
| 324 | BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFc, MatMulUsedInTwoFcFixture) |
| 325 | { |
| 326 | RunTest<1>({ 3 }, { 32 }); |
| 327 | // Ideally we would check here that the armnn network has 5 layers: |
| 328 | // Input, 2 x FullyConnected (biased), Add and Output. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 329 | // This would make sure the parser hasn't incorrectly added some unconnected layers corresponding to the MatMul. |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 330 | } |
| 331 | |
| 332 | // Similar to MatMulUsedInTwoFc, but this time the Adds are 'staggered' (see diagram), which means that only one |
| 333 | // FullyConnected layer can be created (the other should just be an Add). |
| 334 | // I |
| 335 | // | |
| 336 | // M -- C1 |
| 337 | // / \' |
| 338 | // C2 -- A | |
| 339 | // \ / |
| 340 | // A |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 341 | struct MatMulUsedInTwoFcStaggeredFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 342 | { |
| 343 | MatMulUsedInTwoFcStaggeredFixture() |
| 344 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 345 | // Input = tf.placeholder(tf.float32, shape=[1,1], name = "input") |
| 346 | // Const1 = tf.constant([17], tf.float32, [1,1]) |
| 347 | // Mul = tf.matmul(input, const1) |
| 348 | // Monst2 = tf.constant([7], tf.float32, [1]) |
| 349 | // Fc = tf.add(mul, const2) |
| 350 | // Output = tf.add(mul, fc, name="output") |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 351 | m_Prototext = R"( |
| 352 | node { |
| 353 | name: "input" |
| 354 | op: "Placeholder" |
| 355 | attr { |
| 356 | key: "dtype" |
| 357 | value { |
| 358 | type: DT_FLOAT |
| 359 | } |
| 360 | } |
| 361 | attr { |
| 362 | key: "shape" |
| 363 | value { |
| 364 | shape { |
| 365 | dim { |
| 366 | size: 1 |
| 367 | } |
| 368 | dim { |
| 369 | size: 1 |
| 370 | } |
| 371 | } |
| 372 | } |
| 373 | } |
| 374 | } |
| 375 | node { |
| 376 | name: "Const" |
| 377 | op: "Const" |
| 378 | attr { |
| 379 | key: "dtype" |
| 380 | value { |
| 381 | type: DT_FLOAT |
| 382 | } |
| 383 | } |
| 384 | attr { |
| 385 | key: "value" |
| 386 | value { |
| 387 | tensor { |
| 388 | dtype: DT_FLOAT |
| 389 | tensor_shape { |
| 390 | dim { |
| 391 | size: 1 |
| 392 | } |
| 393 | dim { |
| 394 | size: 1 |
| 395 | } |
| 396 | } |
| 397 | float_val: 17.0 |
| 398 | } |
| 399 | } |
| 400 | } |
| 401 | } |
| 402 | node { |
| 403 | name: "MatMul" |
| 404 | op: "MatMul" |
| 405 | input: "input" |
| 406 | input: "Const" |
| 407 | attr { |
| 408 | key: "T" |
| 409 | value { |
| 410 | type: DT_FLOAT |
| 411 | } |
| 412 | } |
| 413 | attr { |
| 414 | key: "transpose_a" |
| 415 | value { |
| 416 | b: false |
| 417 | } |
| 418 | } |
| 419 | attr { |
| 420 | key: "transpose_b" |
| 421 | value { |
| 422 | b: false |
| 423 | } |
| 424 | } |
| 425 | } |
| 426 | node { |
| 427 | name: "Const_1" |
| 428 | op: "Const" |
| 429 | attr { |
| 430 | key: "dtype" |
| 431 | value { |
| 432 | type: DT_FLOAT |
| 433 | } |
| 434 | } |
| 435 | attr { |
| 436 | key: "value" |
| 437 | value { |
| 438 | tensor { |
| 439 | dtype: DT_FLOAT |
| 440 | tensor_shape { |
| 441 | dim { |
| 442 | size: 1 |
| 443 | } |
| 444 | } |
| 445 | float_val: 7.0 |
| 446 | } |
| 447 | } |
| 448 | } |
| 449 | } |
| 450 | node { |
| 451 | name: "Add" |
| 452 | op: "Add" |
| 453 | input: "MatMul" |
| 454 | input: "Const_1" |
| 455 | attr { |
| 456 | key: "T" |
| 457 | value { |
| 458 | type: DT_FLOAT |
| 459 | } |
| 460 | } |
| 461 | } |
| 462 | node { |
| 463 | name: "output" |
| 464 | op: "Add" |
| 465 | input: "MatMul" |
| 466 | input: "Add" |
| 467 | attr { |
| 468 | key: "T" |
| 469 | value { |
| 470 | type: DT_FLOAT |
| 471 | } |
| 472 | } |
| 473 | } |
| 474 | )"; |
| 475 | SetupSingleInputSingleOutput({ 1, 1 }, "input", "output"); |
| 476 | } |
| 477 | }; |
| 478 | |
| 479 | BOOST_FIXTURE_TEST_CASE(MatMulUsedInTwoFcStaggered, MatMulUsedInTwoFcStaggeredFixture) |
| 480 | { |
| 481 | RunTest<1>({ 2 }, { 75 }); |
| 482 | // Ideally we would check here that the armnn network has 5 layers: |
| 483 | // Input, FullyConnected (biased), FullyConnected (non biased), Add and Output. |
| 484 | } |
| 485 | |
| 486 | // A MatMul in isolation, not connected to an add. Should result in a non-biased FullyConnected layer. |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 487 | struct MatMulFixture : public armnnUtils::ParserPrototxtFixture<armnnTfParser::ITfParser> |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 488 | { |
| 489 | MatMulFixture() |
| 490 | { |
telsoa01 | c577f2c | 2018-08-31 09:22:23 +0100 | [diff] [blame] | 491 | // Input = tf.placeholder(tf.float32, shape = [1, 1], name = "input") |
| 492 | // Const = tf.constant([17], tf.float32, [1, 1]) |
| 493 | // Output = tf.matmul(input, const, name = "output") |
surmeh01 | bceff2f | 2018-03-29 16:29:27 +0100 | [diff] [blame] | 494 | m_Prototext = R"( |
| 495 | node { |
| 496 | name: "input" |
| 497 | op: "Placeholder" |
| 498 | attr { |
| 499 | key: "dtype" |
| 500 | value { |
| 501 | type: DT_FLOAT |
| 502 | } |
| 503 | } |
| 504 | attr { |
| 505 | key: "shape" |
| 506 | value { |
| 507 | shape { |
| 508 | dim { |
| 509 | size: 1 |
| 510 | } |
| 511 | dim { |
| 512 | size: 1 |
| 513 | } |
| 514 | } |
| 515 | } |
| 516 | } |
| 517 | } |
| 518 | node { |
| 519 | name: "Const" |
| 520 | op: "Const" |
| 521 | attr { |
| 522 | key: "dtype" |
| 523 | value { |
| 524 | type: DT_FLOAT |
| 525 | } |
| 526 | } |
| 527 | attr { |
| 528 | key: "value" |
| 529 | value { |
| 530 | tensor { |
| 531 | dtype: DT_FLOAT |
| 532 | tensor_shape { |
| 533 | dim { |
| 534 | size: 1 |
| 535 | } |
| 536 | dim { |
| 537 | size: 1 |
| 538 | } |
| 539 | } |
| 540 | float_val: 17.0 |
| 541 | } |
| 542 | } |
| 543 | } |
| 544 | } |
| 545 | node { |
| 546 | name: "output" |
| 547 | op: "MatMul" |
| 548 | input: "input" |
| 549 | input: "Const" |
| 550 | attr { |
| 551 | key: "T" |
| 552 | value { |
| 553 | type: DT_FLOAT |
| 554 | } |
| 555 | } |
| 556 | attr { |
| 557 | key: "transpose_a" |
| 558 | value { |
| 559 | b: false |
| 560 | } |
| 561 | } |
| 562 | attr { |
| 563 | key: "transpose_b" |
| 564 | value { |
| 565 | b: false |
| 566 | } |
| 567 | } |
| 568 | } |
| 569 | )"; |
| 570 | SetupSingleInputSingleOutput({ 1, 1 }, "input", "output"); |
| 571 | } |
| 572 | }; |
| 573 | |
| 574 | BOOST_FIXTURE_TEST_CASE(MatMul, MatMulFixture) |
| 575 | { |
| 576 | RunTest<1>({ 2 }, { 34 }); |
| 577 | } |
| 578 | |
| 579 | BOOST_AUTO_TEST_SUITE_END() |