#!/usr/bin/env bash
# v13 training — Fix SD302 score anchoring + noise concept + occlusion.
#
# Root cause analysis of v12 failures:
#
#   T20 (new) — SD302 scores anchored at ~28 instead of spread [10,90].
#               T17 fix (L_deg on all datasets) used synthetic degradation as
#               ordinal anchor for SD302. But SD302 images look like "level 1-2
#               degraded FVC" to the model → they anchor at ~28 (near degraded floor).
#               Per-sensor data: R/S slap (non-segmented) std=0.0 (fully collapsed),
#               roll sensors mean=27-28 std=6-7, flat sensors mean=35-40 std=10-15.
#               KS still 0.51 (target <0.10) because sensors differ by capture TYPE.
#
#   T21 (new) — Non-segmented slap images (R_*_slap, S_*_slap) std=0.0.
#               Full-hand slap images are visually homogeneous → same score.
#               Not fixable by ordinal grounding — they genuinely have no variation.
#
#   T22 (new) — Noise concept regression: noise_level: +0.188 (v11) → -0.168 (v12).
#               Old c_idx==3 special case pushed noise_level to INCREASE with noise.
#               But ScoreAggregator (unconstrained MLP) can satisfy L_rank by
#               making noise_level DECREASE + assigning positive weight → conflict
#               resolved by inverting the concept direction.
#               Fix: remove special case, noise_level decreases with noise like others.
#
#   T23 (T19 insufficient) — Occlusion still dead (contin=+0.023, minutiae=+0.075).
#               40% coverage insufficient; only minutiae supervised (not continuity).
#               Fix: add continuity (c_idx=2) to occlusion map; increase coverage 55%.
#
# Code changes (v13):
#   T27 — Revert T17: L_deg back to FVC-only.
#          Per-dataset L_spread for SD302 subset added to force SD302 images
#          to span [10,90] within each batch, preventing collapse without synthetic
#          degradation on SD302. Both global L_spread and per-dataset L_spread
#          run with --spread-weight each step.
#   T25 — Remove c_idx==3 special case in DegradationRankingLoss.
#          All concepts now decrease with degradation (high = better quality).
#   T26 — Occlusion: add continuity (c_idx=2) to DEGRADATION_CONCEPT_MAP.
#          Coverage: 40% → 55% at level 3.
#
# Training: resume from v12 last.pt (T18/T19 concept fixes already internalized).
# --spread-weight 3.0 (reduced from 5.0 since two spread terms are now summed).
# --deg-every-n-steps 2 (back to 2; FVC-only L_deg = ~15 images, not 96).
# No --deg-max-images needed (FVC-only is small enough, no OOM risk).
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 "${REPO_ROOT}/sifq/checkpoints_full_v12/last.pt" \
  --epochs 80 \
  --batch-size 96 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 3.0 \
  --deg-every-n-steps 2 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0,1 \
  --save-dir "${REPO_ROOT}/sifq/checkpoints_full_v13"