#!/usr/bin/env bash
# v24 training — Fix orientation_coherence saturation (T39) on top of v23 fixes.
#
# v23 fix (already in code): --concept-deg-gamma 2.0
# v24 additional fix: T39 — orientation_coherence [0] added to dry_skin + wet_press
#
# Problem from v22 eval:
#   orientation_coherence: mean=0.952, std=0.135, p5=0.936, p95=0.989 — SATURATED
#   contrast_uniformity:   mean=0.038, std=0.005 — DEAD (near-constant)
#
# orientation_coherence has NO L_deg target in any degradation → no downward signal
#   → backbone's orientation features map to ~1.0 for every real fingerprint.
#
# T39 fix in src/losses/degradation_ranking.py:
#   dry_skin:  [4, 2] → [4, 2, 0]  (fragmentation → incoherent local orientations)
#   wet_press: [1, 5] → [1, 5, 0]  (ridge merging → false orientation patterns)
#
# v24 settings = v23 settings + T39 concept map update

set -euo pipefail

REPO_ROOT="$(cd "$(dirname "$0")/../.." && pwd)"
export PATH="/home/aiserver/miniconda3/bin:$PATH"

VERSION="v24"
SAVE_DIR="${REPO_ROOT}/sifq/checkpoints/${VERSION}"
LOG_FILE="${REPO_ROOT}/sifq/logs/train_${VERSION}.log"
EVAL_SCRIPT="${REPO_ROOT}/sifq/scripts/run_eval_${VERSION}.sh"

mkdir -p "${REPO_ROOT}/sifq/logs"

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 "${REPO_ROOT}/dataset/PolyU" \
  --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
  --mdgt-checkpoint "${REPO_ROOT}/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt" \
  --epochs 60 \
  --batch-size 96 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 3.0 \
  --concept-deg-gamma 2.0 \
  --sd302-concept-weight 0.0 \
  --deg-every-n-steps 2 \
  --no-mat-stats \
  --proto-max-batches 0 \
  --k-cross 0 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0 \
  --save-dir "${SAVE_DIR}"

echo "[auto-eval] Training done. Starting eval ${VERSION}..."
bash "${EVAL_SCRIPT}" >> "${LOG_FILE}" 2>&1