File size: 41,156 Bytes
6e9cb06 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 | === MDGT TEACHER ===
1 from __future__ import annotations
2
3 from pathlib import Path
4
5 import torch
6 import torch.nn as nn
7
8 from models.mdgt.pipeline import MDGTv2
9
10
11 class MDGTCheckpointTeacher(nn.Module):
12 def __init__(
13 self,
14 checkpoint_path: str,
15 device: str = "cpu",
16 ):
17 super().__init__()
18
19 ckpt_path = Path(checkpoint_path)
20 if not ckpt_path.exists():
21 raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
22
23 ckpt = torch.load(str(ckpt_path), map_location=device, weights_only=False)
24 cfg = ckpt.get("config", {})
25
26 self.mdgt = MDGTv2(
27 vit_variant=cfg.get("vit_variant", "dinov2_vits14"),
28 num_classes=cfg.get("num_classes", 2),
29 tram_k=cfg.get("tram_k", 30),
30 gnn_layers=cfg.get("gnn_layers", 2),
31 gnn_dim=cfg.get("gnn_dim", 256),
32 gnn_heads=cfg.get("gnn_heads", 4),
33 gnn_k=cfg.get("gnn_k", 9),
34 pool_heads=cfg.get("pool_heads", 4),
35 output_dim=cfg.get("output_dim", 256),
36 image_size=cfg.get("image_size", 224),
37 rpe_dim=cfg.get("rpe_dim", 64),
38 patch_size=cfg.get("patch_size", 0),
39 )
40
41 state = ckpt.get("model", ckpt)
42
43 # Checkpoint was saved from TripletMDGTv2 (self.mdgt = MDGTv2),
44 # so state dict keys are prefixed with "mdgt."
45 first_key = next(iter(state))
46 if first_key.startswith("mdgt."):
47 state = {k[len("mdgt."):]: v for k, v in state.items()}
48
49 missing, unexpected = self.mdgt.load_state_dict(state, strict=False)
50 if missing:
51 print(f"[MDGTCheckpointTeacher] {len(missing)} missing keys")
52 if unexpected:
53 print(f"[MDGTCheckpointTeacher] {len(unexpected)} unexpected keys")
54
55 for p in self.parameters():
56 p.requires_grad = False
57 self.eval()
58
59 def forward(self, images: torch.Tensor) -> torch.Tensor:
60 """Return L2-normalised embedding ``(B, output_dim)``."""
61 emb, _ = self.mdgt(images)
62 return emb
=== TRACK 1 MATCH SCORE CODE ===
1 """SIFQ Evaluation Runner — SOTA verification across 4 Tracks.
2
3 Requires:
4 - scores_sifq.jsonl : output of run_infer.py (SIFQ Q scores)
5 - MDGT checkpoint : for computing genuine/impostor match scores (Track 1)
6 - scipy, matplotlib : for plots and KS tests
7
8 Outputs (saved to --out-dir):
9 - results_track1_erc.json : AUC_ERC table (SIFQ vs random baseline)
10 - results_track2_sensor.json : KS statistics per sensor pair
11 - results_track4_concepts.json: Spearman rho crosstalk matrix
12 - plot_erc.png : ERC curve
13 - plot_sensor_hist.png : Q distribution per sensor
14 - plot_crosstalk.png : concept grounding heatmap
15
16 NFIQ2 baseline: pass --nfiq2-scores path/to/nfiq2.jsonl (same format as SIFQ
17 scores but generated externally via `nfiq2 --path ...`).
18
19 Usage:
20 python scripts/run_eval.py \\
21 --sifq-scores /tmp/sifq_scores.jsonl \\
22 --mdgt-checkpoint pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt \\
23 --out-dir eval_results/
24 """
25 from __future__ import annotations
26
27 import argparse
28 import json
29 import sys
30 from collections import defaultdict
31 from pathlib import Path
32 from itertools import combinations
33
34 import numpy as np
35 import torch
36 import torch.nn.functional as F
37 import matplotlib
38 matplotlib.use("Agg")
39 import matplotlib.pyplot as plt
40
41 ROOT = Path(__file__).resolve().parents[1]
42 SRC_ROOT = ROOT / "src"
43 if str(SRC_ROOT) not in sys.path:
44 sys.path.insert(0, str(SRC_ROOT))
45
46 from evaluation.erc import compute_erc
47 from evaluation.sensor_invariance import sensor_ks_test, cross_sensor_correlation
48 from training.mdgt_teacher import MDGTCheckpointTeacher
49
50
51 # ---------------------------------------------------------------------------
52 # Helpers
53 # ---------------------------------------------------------------------------
54
55 def load_scores(jsonl_path: str) -> list[dict]:
56 rows = []
57 with open(jsonl_path, encoding="utf-8") as f:
58 for line in f:
59 line = line.strip()
60 if line:
61 rows.append(json.loads(line))
62 return rows
63
64
65 def build_match_scores(
66 rows: list[dict],
67 mdgt: MDGTCheckpointTeacher,
68 device: torch.device,
69 image_size: int = 224,
70 max_pairs: int = 5000,
71 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
72 """Compute genuine and impostor match scores via MDGT cosine similarity.
73
74 Returns:
75 quality_scores : [N] SIFQ Q scores for each pair member (mean of pair)
76 match_scores : [N] MDGT cosine similarity
77 labels : [N] 1=genuine, 0=impostor
78 """
79 from PIL import Image
80 from torchvision import transforms
81
82 tf = transforms.Compose([
83 transforms.Resize((image_size, image_size)),
84 transforms.ToTensor(),
85 ])
86
87 # Build embedding cache
88 print(" Computing MDGT embeddings for match scores...")
89 paths = [r["image_path"] for r in rows]
90 emb_list: list[torch.Tensor] = []
91 batch_paths: list[str] = []
92 batch_size = 16
93
94 def flush_emb(bpaths: list[str]) -> None:
95 imgs = []
96 for p in bpaths:
97 img = Image.open(p).convert("L")
98 imgs.append(tf(img))
99 batch = torch.stack(imgs, dim=0).to(device)
100 with torch.no_grad():
101 embs = mdgt(batch)
102 emb_list.extend(embs.cpu())
103
104 for row in rows:
105 batch_paths.append(row["image_path"])
106 if len(batch_paths) >= batch_size:
107 flush_emb(batch_paths)
108 batch_paths.clear()
109 if batch_paths:
110 flush_emb(batch_paths)
111
112 embs = torch.stack(emb_list, dim=0) # [N, D]
113
114 # Build genuine pairs (same identity+finger, different sensor)
115 # and impostor pairs (different identity)
116 by_subject: dict[str, list[int]] = defaultdict(list)
117 by_finger_sensor: dict[tuple[str, str], list[int]] = defaultdict(list)
118 for i, r in enumerate(rows):
119 key_fs = (r["identity_id"], r["finger_id"])
120 by_subject[r["identity_id"]].append(i)
121 by_finger_sensor[key_fs].append(i)
122
123 rng = np.random.default_rng(42)
124 genuine_pairs: list[tuple[int, int]] = []
125 for (_, _), idxs in by_finger_sensor.items():
126 for a, b in combinations(idxs, 2):
127 if rows[a]["sensor_id"] != rows[b]["sensor_id"]:
128 genuine_pairs.append((a, b))
129
130 subjects = sorted(by_subject.keys())
131 impostor_pairs: list[tuple[int, int]] = []
132 while len(impostor_pairs) < len(genuine_pairs) * 3:
133 s1, s2 = rng.choice(len(subjects), size=2, replace=False)
134 i1 = int(rng.choice(by_subject[subjects[s1]]))
135 i2 = int(rng.choice(by_subject[subjects[s2]]))
136 impostor_pairs.append((i1, i2))
137
138 all_pairs = (
139 [(a, b, 1) for a, b in genuine_pairs] +
140 [(a, b, 0) for a, b in impostor_pairs]
141 )
142 rng.shuffle(all_pairs)
143 if max_pairs > 0 and len(all_pairs) > max_pairs:
144 all_pairs = all_pairs[:max_pairs]
145
146 q_all, ms_all, lb_all = [], [], []
147 q_arr = np.array([r["q_score"] for r in rows])
148 for a, b, label in all_pairs:
149 cos = float(F.cosine_similarity(embs[a].unsqueeze(0), embs[b].unsqueeze(0)).item())
150 q_all.append((q_arr[a] + q_arr[b]) / 2.0)
151 ms_all.append(cos)
152 lb_all.append(label)
153
154 print(f" Pairs: {len(genuine_pairs)} genuine, {len(impostor_pairs)} impostor "
155 f"(using {len(all_pairs)} total)")
156 return np.array(q_all), np.array(ms_all), np.array(lb_all)
157
158
159 # ---------------------------------------------------------------------------
160 # Track 1: Error Rejection Curve
161 # ---------------------------------------------------------------------------
162
163 def run_track1(
164 rows: list[dict],
165 mdgt: MDGTCheckpointTeacher,
166 device: torch.device,
167 out_dir: Path,
168 nfiq2_rows: list[dict] | None = None,
169 image_size: int = 224,
170 ) -> dict:
171 print("[Track 1] Computing ERC...")
172 q_sifq, ms, labels = build_match_scores(rows, mdgt, device, image_size=image_size)
173
174 rejection_ratios = np.linspace(0.0, 0.5, 50)
175 fnmr_sifq, auc_sifq = compute_erc(q_sifq, ms, labels, rejection_ratios=rejection_ratios)
176
177 # Random baseline: random quality assignment
178 rng = np.random.default_rng(0)
179 q_rand = rng.uniform(0, 100, size=len(q_sifq))
180 fnmr_rand, auc_rand = compute_erc(q_rand, ms, labels, rejection_ratios=rejection_ratios)
181
182 results = {
183 "SIFQ": {"auc_erc": round(auc_sifq, 4)},
184 "Random": {"auc_erc": round(auc_rand, 4)},
185 }
186
187 # NFIQ2 baseline if provided
188 if nfiq2_rows:
189 nfiq2_map = {r["image_path"]: r["q_score"] for r in nfiq2_rows}
190 q_nfiq2 = np.array([nfiq2_map.get(r["image_path"], 50.0) for r in rows])
191 # Re-build pairs using same MDGT match scores by pairing via indices
192 q_nfiq2_pairs = (q_nfiq2[[a for a, _, _ in [(0,0,0)]]] + q_nfiq2) / 2 # placeholder
193 fnmr_nfiq2, auc_nfiq2 = compute_erc(q_nfiq2, ms, labels, rejection_ratios=rejection_ratios)
194 results["NFIQ2"] = {"auc_erc": round(auc_nfiq2, 4)}
195
196 # Plot
197 fig, ax = plt.subplots(figsize=(7, 5))
198 ax.plot(rejection_ratios, fnmr_sifq, label=f"SIFQ (AUC={auc_sifq:.4f})", linewidth=2, color="steelblue")
199 ax.plot(rejection_ratios, fnmr_rand, label=f"Random (AUC={auc_rand:.4f})", linewidth=1.5,
200 linestyle="--", color="gray")
201 if nfiq2_rows:
202 ax.plot(rejection_ratios, fnmr_nfiq2, label=f"NFIQ2 (AUC={auc_nfiq2:.4f})",
203 linewidth=1.5, linestyle=":", color="orangered")
204 ax.set_xlabel("Rejection ratio")
205 ax.set_ylabel("FNMR @ FMR=1e-4")
206 ax.set_title("Error Rejection Curve — lower AUC is better")
207 ax.legend()
208 ax.grid(True, alpha=0.3)
209 fig.tight_layout()
210 fig.savefig(out_dir / "plot_erc.png", dpi=150)
211 plt.close(fig)
212 print(f" ERC plot saved. AUC_ERC: {results}")
213 return results
214
215
216 # ---------------------------------------------------------------------------
217 # Track 2: Sensor Invariance
218 # ---------------------------------------------------------------------------
219
220 def run_track2(rows: list[dict], out_dir: Path, nfiq2_rows: list[dict] | None = None) -> dict:
221 print("[Track 2] Computing sensor invariance...")
222
223 scores_by_sensor: dict[str, np.ndarray] = {}
224 tmp: dict[str, list[float]] = defaultdict(list)
225 for r in rows:
226 tmp[r["sensor_id"]].append(r["q_score"])
227 for sid, vals in tmp.items():
228 scores_by_sensor[sid] = np.array(vals)
229
230 ks_rows = sensor_ks_test(scores_by_sensor)
=== DATASET AND TRANSFORM REFERENCES ===
sifq/src/configs/default.py:56: image_size: tuple[int, int] = (256, 256) # (H, W)
sifq/src/configs/default.py:109: image_size: tuple[int, int] = (256, 256)
sifq/src/configs/default.py:172: With image_size=224 and patch_size=14 → 16×16 = 256 patch tokens.
sifq/src/configs/default.py:177: image_size: int = 224
sifq/src/configs/default.py:268: image_size: int = 224
sifq/src/data/polyu_loader.py:7:from torchvision import transforms
sifq/src/data/polyu_loader.py:51: def __init__(self, image_size: int = 224):
sifq/src/data/polyu_loader.py:52: self.transform = transforms.Compose(
sifq/src/data/polyu_loader.py:54: transforms.Resize((image_size, image_size)),
sifq/src/data/polyu_loader.py:55: transforms.ToTensor(),
sifq/src/data/polyu_loader.py:85: tensor = self.transform(image)
sifq/src/data/joint_augmentor.py:3:Applies the same geometric transform to both the image and minutiae
sifq/src/data/joint_augmentor.py:23: """Applies the same geometric transform to image and minutiae simultaneously.
sifq/src/data/fvc_loader.py:9:from torchvision import transforms
sifq/src/data/fvc_loader.py:50: def __init__(self, image_size: int = 224):
sifq/src/data/fvc_loader.py:51: self.transform = transforms.Compose(
sifq/src/data/fvc_loader.py:53: transforms.Resize((image_size, image_size)),
sifq/src/data/fvc_loader.py:54: transforms.ToTensor(),
sifq/src/data/fvc_loader.py:80: tensor = self.transform(image)
sifq/src/data/augmentation.py:20:from torchvision import transforms as T
sifq/src/data/augmentation.py:69:def build_train_transform(image_size: int = 224, profile: str = "standard") -> T.Compose:
sifq/src/data/augmentation.py:72: Returns a ``torchvision.transforms.Compose`` that takes a PIL image
sifq/src/data/augmentation.py:80: T.Resize((image_size, image_size)),
sifq/src/data/augmentation.py:83: T.RandomResizedCrop(
sifq/src/data/augmentation.py:84: image_size,
sifq/src/data/augmentation.py:98: T.Resize((image_size, image_size)),
sifq/src/data/augmentation.py:111: T.RandomResizedCrop(image_size, scale=(0.8, 1.0), ratio=(0.95, 1.05)),
sifq/src/data/augmentation.py:123: T.ToTensor(),
sifq/src/data/augmentation.py:129:def build_val_transform(image_size: int = 224) -> T.Compose:
sifq/src/data/augmentation.py:130: """Validation transform: resize + to tensor (no augmentation)."""
sifq/src/data/augmentation.py:132: T.Resize((image_size, image_size)),
sifq/src/data/augmentation.py:133: T.ToTensor(),
sifq/src/data/enhanced_sampler.py:17:from .image_dataset import ImageDataset, ImageListDataset
sifq/src/data/enhanced_sampler.py:30: dataset : ImageDataset
sifq/src/data/enhanced_sampler.py:52: dataset: ImageDataset,
sifq/src/data/enhanced_sampler.py:266: dataset: ImageDataset | ImageListDataset,
sifq/src/data/nist302_loader.py:9:from torchvision import transforms
sifq/src/data/nist302_loader.py:33: def __init__(self, image_size: int = 224):
sifq/src/data/nist302_loader.py:34: self.transform = transforms.Compose(
sifq/src/data/nist302_loader.py:36: transforms.Resize((image_size, image_size)),
sifq/src/data/nist302_loader.py:37: transforms.ToTensor(),
sifq/src/data/nist302_loader.py:58: tensor = self.transform(image)
sifq/src/data/dataset.py:27:from torchvision import transforms
sifq/src/data/dataset.py:49: image_size: tuple[int, int] = (256, 256),
sifq/src/data/dataset.py:56: self.image_size = image_size
sifq/src/data/dataset.py:64: self.to_tensor = transforms.ToTensor()
sifq/src/data/dataset.py:167: target_h, target_w = self.image_size
sifq/src/data/dataset.py:199: (self.image_size[1], self.image_size[0]), Image.BILINEAR
sifq/src/data/dataset.py:297: image_size=cfg.image_size,
sifq/src/data/image_dataset.py:24:from .augmentation import build_train_transform, build_val_transform
sifq/src/data/image_dataset.py:44:class ImageDataset(Dataset):
sifq/src/data/image_dataset.py:55: image_size: int = 224,
sifq/src/data/image_dataset.py:61: self.transform = (
sifq/src/data/image_dataset.py:62: build_train_transform(image_size, profile=augment_profile) if augment
sifq/src/data/image_dataset.py:63: else build_val_transform(image_size)
sifq/src/data/image_dataset.py:143: image = self.transform(pil_img)
sifq/src/data/image_dataset.py:157: image_size: int = 224,
sifq/src/data/image_dataset.py:162: self.transform = (
sifq/src/data/image_dataset.py:163: build_train_transform(image_size, profile=augment_profile) if augment
sifq/src/data/image_dataset.py:164: else build_val_transform(image_size)
sifq/src/data/image_dataset.py:182: image = self.transform(pil_img)
sifq/src/data/image_dataset.py:189: def __init__(self, dataset: ImageDataset, p: int = 8, k: int = 4, device_aware: bool = False):
sifq/src/losses/degradation_ranking.py:24:# (mean=0.952, std=0.135, p5=0.936) because concept[0] has NO L_deg target.
sifq/src/train.py:17:import torchvision.transforms.functional as TF
sifq/src/train.py:62: image_size: int,
sifq/src/train.py:67: self.nist_loader = NIST302Loader(image_size=image_size)
sifq/src/train.py:68: self.fvc_loader = FVCLoader(image_size=image_size)
sifq/src/train.py:73: self._preload_images(image_size)
sifq/src/train.py:75: def _preload_images(self, image_size: int) -> None:
sifq/src/train.py:77: cache = np.empty((N, image_size, image_size), dtype=np.uint8)
sifq/src/train.py:529: nist_loader = NIST302Loader(image_size=args.image_size)
sifq/src/train.py:538: fvc_loader = FVCLoader(image_size=args.image_size)
sifq/src/train.py:545: polyu_loader = PolyULoader(image_size=args.image_size)
sifq/src/train.py:568: train_ds = RecordDataset(records=records, image_size=args.image_size, sensor_to_idx=sensor_to_idx)
sifq/src/train.py:897: f"q_mean={_q_mean:.1f} q_std={_q_std:.1f} "
sifq/src/models/mdgt/pipeline.py:80: image_size : int
sifq/src/models/mdgt/pipeline.py:105: image_size: int = 224,
sifq/src/models/mdgt/pipeline.py:119: image_size=image_size,
sifq/src/models/mdgt/pipeline.py:126: img_size=image_size,
sifq/src/models/mdgt/pipeline.py:207: nn.init.trunc_normal_(self.cls_head.weight, std=0.02)
sifq/src/models/mdgt/vit_graph.py:81: image_size=cfg.vit.image_size,
sifq/src/models/mdgt/vit_graph.py:84: self._vit_image_size = (cfg.vit.image_size, cfg.vit.image_size)
sifq/src/models/mdgt/vit_graph.py:184: # 0. Grayscale → 3-channel + resize for ViT
sifq/src/models/mdgt/vit_graph.py:187: if images.shape[-2:] != self._vit_image_size:
sifq/src/models/mdgt/vit_graph.py:189: images, size=self._vit_image_size,
sifq/src/models/mdgt/relational_pe.py:57: # Normalize spatial features so all 7 dims are ~ [-1, 1] scale.
sifq/src/models/mdgt/vit.py:216: nn.init.trunc_normal_(self.pos_embed, std=0.02)
sifq/src/models/mdgt/vit.py:217: nn.init.trunc_normal_(self.cls_token, std=0.02)
sifq/src/models/mdgt/vit.py:220: nn.init.trunc_normal_(m.weight, std=0.02)
sifq/src/models/mdgt/vit_backbone.py:41: image_size : int
sifq/src/models/mdgt/vit_backbone.py:51: image_size: int = 224,
sifq/src/models/mdgt/vit_backbone.py:62: img_size=image_size,
sifq/src/models/mdgt/vit_backbone.py:85: Grayscale images should be repeated to 3 channels
sifq/src/models/mdgt/dinov2_backbone.py:30: image_size : int
sifq/src/models/mdgt/dinov2_backbone.py:42: image_size: int = 224,
sifq/src/models/mdgt/dinov2_backbone.py:53: # ---- Grayscale -> RGB adapter (learned, init to equal mix) ----
sifq/src/models/mdgt/dinov2_backbone.py:61: self.grid_size: tuple[int, int] = (image_size // ps, image_size // ps)
sifq/src/models/mdgt/dinov2_backbone.py:130: # Grayscale -> RGB
sifq/src/models/mdgt/pipeline_continual_pretrained.py:7: - Grayscale images are converted to RGB via channel replication
sifq/src/models/mdgt/pipeline_continual_pretrained.py:73: image_size : int
sifq/src/models/mdgt/pipeline_continual_pretrained.py:94: image_size: int = 224,
sifq/src/models/mdgt/pipeline_continual_pretrained.py:111: image_size=image_size,
sifq/src/training/mdgt_teacher.py:36: image_size=cfg.get("image_size", 224),
sifq/scripts/run_eval.py:69: image_size: int = 224,
sifq/scripts/run_eval.py:80: from torchvision import transforms
sifq/scripts/run_eval.py:82: tf = transforms.Compose([
sifq/scripts/run_eval.py:83: transforms.Resize((image_size, image_size)),
sifq/scripts/run_eval.py:84: transforms.ToTensor(),
sifq/scripts/run_eval.py:169: image_size: int = 224,
sifq/scripts/run_eval.py:172: q_sifq, ms, labels = build_match_scores(rows, mdgt, device, image_size=image_size)
sifq/scripts/run_eval.py:312: image_size: int = 224,
sifq/scripts/run_eval.py:344: img = cv2.resize(img, (image_size, image_size))
sifq/scripts/run_eval.py:439: image_size=args.image_size,
sifq/scripts/run_eval.py:443: image_size=args.image_size)
sifq/scripts/run_eval.py:452: image_size=args.image_size, max_images=args.max_concept_images)
sifq/scripts/visualize_score_milestones.py:124: norm = plt.Normalize(vmin=q_min, vmax=q_max)
sifq/scripts/visualize_score_milestones.py:189: transform=ax_img.transAxes,
sifq/scripts/gen_nfiq2_proxy_scores.py:39:def _gabor_energy(gray: np.ndarray, image_size: int = 224) -> float:
sifq/scripts/gen_nfiq2_proxy_scores.py:41: img = cv2.resize(gray, (image_size, image_size)).astype(np.float32) / 255.0
sifq/scripts/run_infer.py:95: loader = NIST302Loader(image_size=args.image_size)
sifq/scripts/train_sifq.py:17:import torchvision.transforms.functional as TF
sifq/scripts/train_sifq.py:62: image_size: int,
sifq/scripts/train_sifq.py:67: self.nist_loader = NIST302Loader(image_size=image_size)
sifq/scripts/train_sifq.py:68: self.fvc_loader = FVCLoader(image_size=image_size)
sifq/scripts/train_sifq.py:73: self._preload_images(image_size)
sifq/scripts/train_sifq.py:75: def _preload_images(self, image_size: int) -> None:
sifq/scripts/train_sifq.py:77: cache = np.empty((N, image_size, image_size), dtype=np.uint8)
sifq/scripts/train_sifq.py:559: nist_loader = NIST302Loader(image_size=args.image_size)
sifq/scripts/train_sifq.py:568: fvc_loader = FVCLoader(image_size=args.image_size)
sifq/scripts/train_sifq.py:575: polyu_loader = PolyULoader(image_size=args.image_size)
sifq/scripts/train_sifq.py:598: train_ds = RecordDataset(records=records, image_size=args.image_size, sensor_to_idx=sensor_to_idx)
sifq/scripts/train_sifq.py:933: f"q_mean={_q_mean:.1f} q_std={_q_std:.1f} "
sifq/scripts/_gen_report_v24.py:112: ' ScoreAggregator chon noise_level (std=0.340, cao nhat)',
pad/TRAM-downstream/livdev_dataloader.py:7:from torchvision import transforms
pad/TRAM-downstream/livdev_dataloader.py:190: def __init__(self, dataset, transform):
pad/TRAM-downstream/livdev_dataloader.py:192: self.transform = transform
pad/TRAM-downstream/livdev_dataloader.py:199: if self.transform:
pad/TRAM-downstream/livdev_dataloader.py:200: image = self.transform(image)
pad/TRAM-downstream/livdev_dataloader.py:223: transform: dict,
pad/TRAM-downstream/livdev_dataloader.py:240: train_dataset = TransformedDataset(train_subset, transform['Train'])
pad/TRAM-downstream/livdev_dataloader.py:241: val_dataset = TransformedDataset(val_subset, transform['Test'])
pad/TRAM-downstream/livdev_dataloader.py:246: test_dataset = TransformedDataset(dataset, transform['Test'])
pad/TRAM-downstream/dataloader.py:7:from torchvision import transforms
pad/TRAM-downstream/dataloader.py:190: def __init__(self, dataset, transform):
pad/TRAM-downstream/dataloader.py:192: self.transform = transform
pad/TRAM-downstream/dataloader.py:199: if self.transform:
pad/TRAM-downstream/dataloader.py:200: image = self.transform(image)
pad/TRAM-downstream/dataloader.py:223: transform: dict,
pad/TRAM-downstream/dataloader.py:240: train_dataset = TransformedDataset(train_subset, transform['Train'])
pad/TRAM-downstream/dataloader.py:241: val_dataset = TransformedDataset(val_subset, transform['Test'])
pad/TRAM-downstream/dataloader.py:246: test_dataset = TransformedDataset(dataset, transform['Test'])
pad/TRAM-downstream/infer.py:51:from torchvision import models, transforms
pad/TRAM-downstream/infer.py:60: """Lightweight STN that predicts a 4-parameter affine transform
pad/TRAM-downstream/infer.py:157: num_transformer_layers: int = 12,
pad/TRAM-downstream/infer.py:201: self.transformer = nn.Sequential(
pad/TRAM-downstream/infer.py:204: for _ in range(num_transformer_layers)
pad/TRAM-downstream/infer.py:237: tokens = self.transformer(tokens)
pad/TRAM-downstream/infer.py:261: def __init__(self, root_dir: str, transform: Optional[transforms.Compose] = None):
pad/TRAM-downstream/infer.py:264: self.transform = transform
pad/TRAM-downstream/infer.py:298: if self.transform:
pad/TRAM-downstream/infer.py:299: img = self.transform(img)
pad/TRAM-downstream/infer.py:307: def __init__(self, root_dir: str, transform: Optional[transforms.Compose] = None):
pad/TRAM-downstream/infer.py:310: self.transform = transform
pad/TRAM-downstream/infer.py:356: if self.transform:
pad/TRAM-downstream/infer.py:357: img = self.transform(img)
pad/TRAM-downstream/infer.py:550: num_transformer_layers=12,
pad/TRAM-downstream/infer.py:597:def get_transform(image_size: int = 224) -> transforms.Compose:
pad/TRAM-downstream/infer.py:598: """Grayscale -> 3-channel -> resize -> ImageNet normalize."""
pad/TRAM-downstream/infer.py:599: return transforms.Compose(
pad/TRAM-downstream/infer.py:601: transforms.Resize((image_size, image_size)),
pad/TRAM-downstream/infer.py:602: transforms.Grayscale(num_output_channels=3),
pad/TRAM-downstream/infer.py:603: transforms.ToTensor(),
pad/TRAM-downstream/infer.py:604: transforms.Normalize(
pad/TRAM-downstream/infer.py:605: mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
pad/TRAM-downstream/infer.py:769: transform = get_transform(args.image_size)
pad/TRAM-downstream/infer.py:777: dataset = FVCDataset(db_path, transform=transform)
pad/TRAM-downstream/model/pipeline.py:73: image_size : int
pad/TRAM-downstream/model/pipeline.py:92: image_size: int = 224,
pad/TRAM-downstream/model/pipeline.py:101: img_size=image_size,
pad/TRAM-downstream/model/pipeline.py:163: nn.init.trunc_normal_(self.cls_head.weight, std=0.02)
pad/TRAM-downstream/model/vit_graph.py:81: image_size=cfg.vit.image_size,
pad/TRAM-downstream/model/vit_graph.py:84: self._vit_image_size = (cfg.vit.image_size, cfg.vit.image_size)
pad/TRAM-downstream/model/vit_graph.py:184: # 0. Grayscale → 3-channel + resize for ViT
pad/TRAM-downstream/model/vit_graph.py:187: if images.shape[-2:] != self._vit_image_size:
pad/TRAM-downstream/model/vit_graph.py:189: images, size=self._vit_image_size,
pad/TRAM-downstream/model/relational_pe.py:57: # Normalize spatial features so all 7 dims are ~ [-1, 1] scale.
pad/TRAM-downstream/model/flare/units.py:10:class NormalizeModule(nn.Module):
pad/TRAM-downstream/model/flare/__init__.py:4:from .units import NormalizeModule
pad/TRAM-downstream/model/vit.py:202: nn.init.trunc_normal_(self.pos_embed, std=0.02)
pad/TRAM-downstream/model/vit.py:203: nn.init.trunc_normal_(self.cls_token, std=0.02)
pad/TRAM-downstream/model/vit.py:206: nn.init.trunc_normal_(m.weight, std=0.02)
pad/TRAM-downstream/model/domain_bn.py:79: # Normalize using batch stats
pad/TRAM-downstream/model/pipeline_continual.py:65: image_size : int
pad/TRAM-downstream/model/pipeline_continual.py:87: image_size: int = 224,
pad/TRAM-downstream/model/pipeline_continual.py:101: img_size=image_size,
pad/TRAM-downstream/model/pipeline_continual.py:115: grid_size = (image_size // 16, image_size // 16) # Patch grid
pad/TRAM-downstream/model/pipeline_continual.py:168: nn.init.trunc_normal_(self.cls_head.weight, std=0.02)
pad/TRAM-downstream/model/vit_backbone.py:41: image_size : int
pad/TRAM-downstream/model/vit_backbone.py:51: image_size: int = 224,
pad/TRAM-downstream/model/vit_backbone.py:62: img_size=image_size,
pad/TRAM-downstream/model/vit_backbone.py:85: Grayscale images should be repeated to 3 channels
pad/TRAM-downstream/model/pipeline_continual_pretrained.py:7: - Grayscale images are converted to RGB via channel replication
pad/TRAM-downstream/model/pipeline_continual_pretrained.py:74: image_size : int
pad/TRAM-downstream/model/pipeline_continual_pretrained.py:95: image_size: int = 224,
pad/TRAM-downstream/model/pipeline_continual_pretrained.py:113: image_size=image_size,
pad/TRAM-downstream/eval.py:18:from fingerprint_graph.data.image_dataset import ImageDataset
pad/TRAM-downstream/eval.py:379: image_size=cfg.get("image_size", 224),
pad/TRAM-downstream/eval.py:396: val_ds = ImageDataset(args.val_image_dir, image_size=ckpt.get("config", {}).get("image_size", 224), augment=False)
=== MDGT VALIDATION DATASET IMPLEMENTATION ===
--- sifq/src/data/image_dataset.py
1 from __future__ import annotations
2
3 """Image-only dataset for the V2 (ViT + TRAM + GNN) pipeline.
4
5 Unlike ``FingerprintDataset`` which requires paired minutiae files,
6 this dataset loads only images + identity labels. No minutiae extractor
7 is needed — the ViT backbone learns features end-to-end.
8
9 Supports directory layouts:
10 1. ImageFolder: ``root/identity_name/sample.{ext}``
11 2. PolyU: ``root/{first,second}_session/finger_sample.{ext}``
12 (identity parsed from filename prefix before last underscore)
13 """
14
15 import os
16 import random
17 from collections import defaultdict
18 from pathlib import Path
19
20 import torch
21 from torch.utils.data import Dataset, Sampler
22 from PIL import Image
23
24 from .augmentation import build_train_transform, build_val_transform
25
26
27 IMAGE_EXTS = {".bmp", ".png", ".tif", ".tiff", ".jpg", ".jpeg"}
28
29
30 def infer_device_from_name(path_or_name: str) -> str | None:
31 stem = Path(path_or_name).stem
32 parts = stem.split("_")
33 if len(parts) < 4:
34 return None
35 token = parts[1]
36 token_lower = token.lower()
37 if token_lower in {"roll", "plain"}:
38 return token_lower
39 if token.isalpha() and len(token) <= 3:
40 return token
41 return None
42
43
44 class ImageDataset(Dataset):
45 """Load fingerprint images with identity labels for metric learning.
46
47 Returns:
48 image: ``(1, H, W)`` normalised [0, 1]
49 label: int
50 """
51
52 def __init__(
53 self,
54 image_dir: str,
55 image_size: int = 224,
56 augment: bool = True,
57 repeat_factor: int = 1,
58 augment_profile: str = "standard",
59 ):
60 super().__init__()
61 self.transform = (
62 build_train_transform(image_size, profile=augment_profile) if augment
63 else build_val_transform(image_size)
64 )
65 self.repeat_factor = max(1, int(repeat_factor))
66
67 self.samples: list[tuple[str, int]] = [] # (path, label)
68 self.labels: list[int] = []
69 self.devices: list[str | None] = []
70 self.label_map: dict[str, int] = {}
71
72 self._discover(image_dir)
73
74 # ------------------------------------------------------------------
75 def _discover(self, image_dir: str):
76 root = Path(image_dir)
77 if not root.exists():
78 return
79
80 subdirs = sorted([d for d in root.iterdir() if d.is_dir()])
81 session_like = subdirs and all("session" in d.name.lower() for d in subdirs)
82
83 if subdirs and not session_like:
84 has_images = any(
85 any(f.suffix.lower() in IMAGE_EXTS for f in d.iterdir() if f.is_file())
86 for d in subdirs[:5]
87 )
88 if has_images:
89 self._discover_imagefolder(root, subdirs)
90 return
91
92 self._discover_flat(root)
93
94 def _append_sample(self, img_path: Path, label: int):
95 self.samples.append((str(img_path), label))
96 self.labels.append(label)
97 self.devices.append(infer_device_from_name(img_path.name))
98
99 def _discover_imagefolder(self, root: Path, subdirs: list[Path]):
100 """``root/identity/sample.ext`` layout."""
101 for idx, identity_dir in enumerate(subdirs):
102 identity = identity_dir.name
103 self.label_map[identity] = idx
104 for img_file in sorted(identity_dir.iterdir()):
105 if img_file.suffix.lower() in IMAGE_EXTS:
106 self._append_sample(img_file, idx)
107
108 def _discover_flat(self, root: Path):
109 """Flat/PolyU layout — parse identity from filename."""
110 all_images: list[Path] = []
111 for ext in IMAGE_EXTS:
112 all_images.extend(root.rglob(f"*{ext}"))
113 all_images = sorted(all_images)
114
115 identity_of: dict[str, str] = {}
116 for img in all_images:
117 stem = img.stem
118 parts = stem.rsplit("_", 1)
119 identity = parts[0] if len(parts) > 1 else stem
120 identity_of[str(img)] = identity
121
122 unique_ids = sorted(set(identity_of.values()))
123 id_to_label = {name: idx for idx, name in enumerate(unique_ids)}
124 self.label_map = id_to_label
125
126 for img in all_images:
127 identity = identity_of[str(img)]
128 label = id_to_label[identity]
129 self._append_sample(img, label)
130
131 # ------------------------------------------------------------------
132 @property
133 def num_classes(self) -> int:
134 return len(self.label_map)
135
136 def __len__(self) -> int:
137 return len(self.samples) * self.repeat_factor
138
139 def __getitem__(self, idx: int) -> dict[str, object]:
140 idx = idx % len(self.samples)
141 path, label = self.samples[idx]
142 pil_img = Image.open(path).convert("L")
143 image = self.transform(pil_img)
144 return {"image": image, "label": label}
145
146
147 class ImageListDataset(Dataset):
148 """Image dataset backed by an explicit list of ``(path, label)`` samples.
149
150 Useful for continual learning where the effective training set is assembled
151 dynamically from the current stage plus replay exemplars from previous stages.
152 """
153
154 def __init__(
155 self,
156 samples: list[tuple[str, int]],
157 image_size: int = 224,
158 augment: bool = True,
159 augment_profile: str = "standard",
160 ):
161 super().__init__()
162 self.transform = (
163 build_train_transform(image_size, profile=augment_profile) if augment
164 else build_val_transform(image_size)
165 )
166 self.samples = [(str(path), int(label)) for path, label in samples]
167 self.labels = [label for _path, label in self.samples]
168 self.devices = [infer_device_from_name(path) for path, _label in self.samples]
169 unique_labels = sorted(set(self.labels))
170 self.label_map = {str(label): label for label in unique_labels}
171
172 @property
173 def num_classes(self) -> int:
174 return len(self.label_map)
175
176 def __len__(self) -> int:
177 return len(self.samples)
178
179 def __getitem__(self, idx: int) -> dict[str, object]:
180 path, label = self.samples[idx]
181 pil_img = Image.open(path).convert("L")
182 image = self.transform(pil_img)
183 return {"image": image, "label": label}
184
185
186 class PKSamplerV2(Sampler):
187 """P identities × K samples per batch for metric learning."""
188
189 def __init__(self, dataset: ImageDataset, p: int = 8, k: int = 4, device_aware: bool = False):
190 self.p = p
191 self.k = k
192 self.device_aware = device_aware
193 self._len = len(dataset) // (p * k)
194
195 self.label_to_indices: dict[int, list[int]] = defaultdict(list)
196 self.label_to_device_indices: dict[int, dict[str | None, list[int]]] = defaultdict(lambda: defaultdict(list))
197 for idx, label in enumerate(dataset.labels):
198 self.label_to_indices[label].append(idx)
199 self.label_to_device_indices[label][dataset.devices[idx]].append(idx)
200
201 self.labels = sorted(self.label_to_indices.keys())
202
203 def _sample_indices(self, label: int) -> list[int]:
204 indices = self.label_to_indices[label]
205 if not self.device_aware:
206 return (
207 random.sample(indices, self.k)
208 if len(indices) >= self.k
209 else random.choices(indices, k=self.k)
210 )
211
212 by_device = self.label_to_device_indices[label]
213 distinct_devices = [dev for dev in by_device if dev is not None]
214 if len(distinct_devices) <= 1:
215 return (
216 random.sample(indices, self.k)
217 if len(indices) >= self.k
218 else random.choices(indices, k=self.k)
219 )
220
221 chosen: list[int] = []
222 device_keys = distinct_devices.copy()
223 random.shuffle(device_keys)
224 for dev in device_keys:
225 if len(chosen) >= self.k:
226 break
227 chosen.append(random.choice(by_device[dev]))
228
229 remaining_pool = [idx for idx in indices if idx not in chosen]
230 needed = self.k - len(chosen)
231 if needed > 0:
232 if len(remaining_pool) >= needed:
233 chosen.extend(random.sample(remaining_pool, needed))
234 else:
235 chosen.extend(remaining_pool)
236 if len(chosen) < self.k:
237 chosen.extend(random.choices(indices, k=self.k - len(chosen)))
238
239 return chosen
240
241 def __iter__(self):
242 pool = self.labels.copy()
243 random.shuffle(pool)
244 ptr = 0
245
246 for _ in range(self._len):
247 if ptr + self.p > len(pool):
248 pool = self.labels.copy()
249 random.shuffle(pool)
250 ptr = 0
251
252 batch_labels = pool[ptr:ptr + self.p]
253 ptr += self.p
254
255 batch: list[int] = []
256 for lbl in batch_labels:
257 batch.extend(self._sample_indices(lbl))
258 yield batch
259
260 def __len__(self) -> int:
261 return max(1, self._len)
|