#!/usr/bin/env bash
# v12 training — Fix score collapse + wet_press concept grounding.
#
# Changes vs v11:
#   T17 — L_deg applied to ALL datasets (SD302 + FVC + PolyU), not FVC-only.
#          Root cause of v11 score collapse: 95% of SD302 images stuck at ~58.3
#          because they had no ordinal grounding from L_deg.  Synthetic degradation
#          on SD302 is artificial but necessary to prevent collapse on majority data.
#   T18 — MorphologicalDilator fixed: cv2.erode instead of cv2.dilate.
#          NIST fingerprints have DARK ridges.  Erode expands dark ridges (wet smear).
#          Previous dilate shrank ridges → paradoxically increased clarity score.
#          Fixes wet_press → clarity = +0.219 (wrong) → should be negative.
#   T19 — DegradationRankingLoss gamma: 0.5 → 1.0 (stronger concept supervision).
#          Occlusion block coverage: 30% → 40% at level 3.
#          Fixes jpeg/occlusion concept signal near zero (rho ≈ -0.05, -0.03 in v11).
#
#   Training: resume from v11 last.pt (model weights only, not optimizer).
#   --spread-weight 5.0 (up from 2.0) to force wider score distribution.
#   --deg-every-n-steps 4 (up from 2) to keep compute balanced since L_deg
#     now processes full batch (96 images) instead of ~15 FVC images.
#
# Expected improvements:
#   Track 2 mean_KS: 0.5566 → < 0.15 (SD302 images should spread, less sensor clustering)
#   Track 4 wet_press → clarity: +0.219 (❌) → negative (✓)
#   Track 4 occlusion → minutiae: -0.032 (weak) → < -0.2 (strong)
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" \
  --resume "${REPO_ROOT}/sifq/checkpoints_full_v11/last.pt" \
  --epochs 80 \
  --batch-size 96 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 5.0 \
  --deg-every-n-steps 4 \
  --deg-max-images 32 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0,1 \
  --save-dir "${REPO_ROOT}/sifq/checkpoints_full_v12"