File size: 32,220 Bytes
4367e37 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 | UnifiedForCausalLM(
(model): UnifiedModel(
(embed_tokens): Embedding(100289, 2048, padding_idx=100277)
(layers): ModuleList(
(0-15): 16 x Olmo2DecoderLayer(
(self_attn): Olmo2Attention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=2048, bias=False)
(v_proj): Linear(in_features=2048, out_features=2048, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(q_norm): Olmo2RMSNorm((2048,), eps=1e-06)
(k_norm): Olmo2RMSNorm((2048,), eps=1e-06)
)
(mlp): Olmo2MLP(
(gate_proj): Linear(in_features=2048, out_features=8192, bias=False)
(up_proj): Linear(in_features=2048, out_features=8192, bias=False)
(down_proj): Linear(in_features=8192, out_features=2048, bias=False)
(act_fn): SiLU()
)
(post_attention_layernorm): Olmo2RMSNorm((2048,), eps=1e-06)
(post_feedforward_layernorm): Olmo2RMSNorm((2048,), eps=1e-06)
)
)
(norm): Olmo2RMSNorm((2048,), eps=1e-06)
(rotary_emb): Olmo2RotaryEmbedding()
(visual_encoder): MultiPathCLIPVisionTower(
(slow_vision_tower): ConvNextVisionTower(
(vision_tower): ConvNeXt(
(stem): Sequential(
(0): Conv2d(3, 192, kernel_size=(4, 4), stride=(4, 4))
(1): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
)
(stages): Sequential(
(0): ConvNeXtStage(
(downsample): Identity()
(blocks): Sequential(
(0): ConvNeXtBlock(
(conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
(norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=192, out_features=768, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=768, out_features=192, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(1): ConvNeXtBlock(
(conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
(norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=192, out_features=768, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=768, out_features=192, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(2): ConvNeXtBlock(
(conv_dw): Conv2d(192, 192, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=192)
(norm): LayerNorm((192,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=192, out_features=768, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=768, out_features=192, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
)
)
(1): ConvNeXtStage(
(downsample): Sequential(
(0): LayerNorm2d((192,), eps=1e-06, elementwise_affine=True)
(1): Conv2d(192, 384, kernel_size=(2, 2), stride=(2, 2))
)
(blocks): Sequential(
(0): ConvNeXtBlock(
(conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
(norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=384, out_features=1536, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=1536, out_features=384, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(1): ConvNeXtBlock(
(conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
(norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=384, out_features=1536, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=1536, out_features=384, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(2): ConvNeXtBlock(
(conv_dw): Conv2d(384, 384, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=384)
(norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=384, out_features=1536, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=1536, out_features=384, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
)
)
(2): ConvNeXtStage(
(downsample): Sequential(
(0): LayerNorm2d((384,), eps=1e-06, elementwise_affine=True)
(1): Conv2d(384, 768, kernel_size=(2, 2), stride=(2, 2))
)
(blocks): Sequential(
(0): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(1): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(2): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(3): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(4): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(5): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(6): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(7): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(8): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(9): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(10): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(11): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(12): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(13): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(14): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(15): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(16): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(17): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(18): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(19): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(20): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(21): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(22): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(23): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(24): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(25): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(26): ConvNeXtBlock(
(conv_dw): Conv2d(768, 768, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=768)
(norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=768, out_features=3072, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=3072, out_features=768, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
)
)
(3): ConvNeXtStage(
(downsample): Sequential(
(0): LayerNorm2d((768,), eps=1e-06, elementwise_affine=True)
(1): Conv2d(768, 1536, kernel_size=(2, 2), stride=(2, 2))
)
(blocks): Sequential(
(0): ConvNeXtBlock(
(conv_dw): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536)
(norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=1536, out_features=6144, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=6144, out_features=1536, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(1): ConvNeXtBlock(
(conv_dw): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536)
(norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=1536, out_features=6144, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=6144, out_features=1536, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
(2): ConvNeXtBlock(
(conv_dw): Conv2d(1536, 1536, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1536)
(norm): LayerNorm((1536,), eps=1e-06, elementwise_affine=True)
(mlp): Mlp(
(fc1): Linear(in_features=1536, out_features=6144, bias=True)
(act): GELU()
(drop1): Dropout(p=0.0, inplace=False)
(norm): Identity()
(fc2): Linear(in_features=6144, out_features=1536, bias=True)
(drop2): Dropout(p=0.0, inplace=False)
)
(shortcut): Identity()
(drop_path): Identity()
)
)
)
)
(norm_pre): Identity()
(head): NormMlpClassifierHead(
(global_pool): SelectAdaptivePool2d(pool_type=avg, flatten=Identity())
(norm): LayerNorm2d((1536,), eps=1e-06, elementwise_affine=True)
(flatten): Flatten(start_dim=1, end_dim=-1)
(pre_logits): Sequential(
(fc): Linear(in_features=1536, out_features=1536, bias=True)
(act): GELU()
)
(drop): Dropout(p=0.0, inplace=False)
(fc): Linear(in_features=1536, out_features=1000, bias=True)
)
)
)
(fast_vision_tower): CLIPVisionTower(
(vision_tower): CLIPVisionModel(
(vision_model): CLIPVisionTransformer(
(embeddings): CLIPVisionEmbeddings(
(patch_embedding): Conv2d(3, 1024, kernel_size=(14, 14), stride=(14, 14), bias=False)
(position_embedding): Embedding(577, 1024)
)
(pre_layrnorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(encoder): CLIPEncoder(
(layers): ModuleList(
(0-23): 24 x CLIPEncoderLayer(
(self_attn): CLIPSdpaAttention(
(k_proj): Linear(in_features=1024, out_features=1024, bias=True)
(v_proj): Linear(in_features=1024, out_features=1024, bias=True)
(q_proj): Linear(in_features=1024, out_features=1024, bias=True)
(out_proj): Linear(in_features=1024, out_features=1024, bias=True)
)
(layer_norm1): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
(mlp): CLIPMLP(
(activation_fn): QuickGELUActivation()
(fc1): Linear(in_features=1024, out_features=4096, bias=True)
(fc2): Linear(in_features=4096, out_features=1024, bias=True)
)
(layer_norm2): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
)
(post_layernorm): LayerNorm((1024,), eps=1e-05, elementwise_affine=True)
)
)
)
(align_stages_latent): ModuleList(
(0-2): 3 x S2FStitchAlignModuleV2(
(slow_conv): Conv2d(1536, 1536, kernel_size=(1, 1), stride=(1, 1))
(slow_proj): Conv2d(1536, 1024, kernel_size=(1, 1), stride=(1, 1))
(fast_conv): Conv2d(1024, 1024, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=1024)
(fast_proj): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))
(gate): Sequential(
(0): Linear(in_features=2048, out_features=512, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=512, out_features=1, bias=True)
)
)
)
(align_stages): ModuleList(
(0): MultiPathAlignModule(
(fast_proj): Linear(in_features=1024, out_features=1024, bias=True)
(slow_proj): Linear(in_features=1536, out_features=1024, bias=True)
)
)
)
(vl_projector): Sequential(
(0): Linear(in_features=1024, out_features=2048, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=2048, out_features=2048, bias=True)
)
)
(lm_head): Linear(in_features=2048, out_features=100289, bias=False)
) |