#!/usr/bin/env bash # v11 training — PolyU back in L_sens (contact vs contactless pairs). # # Changes vs v10: # 1. PolyU RE-INCLUDED in CrossSensorBatchSampler + build_pair_indices. # PolyU contact vs contactless = strongest cross-modal sensor signal → L_sens. # 2. PolyU EXCLUDED from L_mat (no prototype / teacher loss for PolyU images). # 3. PolyU EXCLUDED from L_deg (degradation ranking not applicable to PolyU). # → PolyU contributes ONLY to L_sens per original design. # 4. New β/α schedule: β ramps FASTER than α so β/α → 1.0 at S2 entry (ep20). # α: 0.10→0.30 β: 0.00→0.30 γ: 1.00→0.60 over ep10-19. # S2 full: α=0.30 β=0.30 γ=0.60 (β/α=1.0). # S3: α=0.25 β=0.35 γ=0.50 (β/α=1.4). # # Root cause of v9 failure: β/α=0.50 in S2 (α=0.40, β=0.20) → GRL too weak # to override L_mat's sensor-correlated gradients → L_sens stuck at 2-3 all S2/S3. # # Total dataset: SD302-A/B/D (30,567) + FVC2002+2004 (7,040) + PolyU (5,952) # = 43,559 images # L_sens anchors: SD302 (~2,276 groups) + PolyU (336 cross-modal groups) # L_mat: SD302 + FVC only (PolyU masked) # L_deg: FVC only (SD302 + PolyU masked) set -euo pipefail REPO_ROOT="$(cd "$(dirname "$0")/../.." && pwd)" source "${REPO_ROOT}/.venv/bin/activate" python "${REPO_ROOT}/sifq/scripts/train_sifq.py" \ --root-302a "${REPO_ROOT}/dataset/302a/images/challengers" \ --root-302b "${REPO_ROOT}/dataset/302b/images/baseline" \ --root-302d "${REPO_ROOT}/dataset/nist_302d/images/auxiliary" \ --root-fvc2002 "${REPO_ROOT}/dataset/FVC_Dataset/FVC2002" \ --root-fvc2004 "${REPO_ROOT}/dataset/FVC_Dataset/FVC2004" \ --root-polyu "${REPO_ROOT}/dataset/PolyU" \ --mdgt-checkpoint "${REPO_ROOT}/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt" \ --epochs 80 \ --batch-size 96 \ --image-size 224 \ --lr 1e-4 \ --spread-mode uniform \ --spread-weight 2.0 \ --deg-every-n-steps 2 \ --max-train-samples -1 \ --num-workers 8 \ --gpus 0,1 \ --save-dir "${REPO_ROOT}/sifq/checkpoints_full_v11"