scripts/run_train_v19.sh

#!/usr/bin/env bash
# v19 training — T34: true revert to v14 hyperparameters (fix v18 misconfiguration).
#
# Root cause analysis (v18 failure):
#
#   v18 claimed to "revert to v14 design" but used WRONG hyperparameters:
#     v18: --spread-weight 2.0  (v14 actual: 3.0  ← wrong)
#     v18: --deg-every-n-steps 4  (v14 actual: 2  ← wrong)
#   v18 comments were incorrect — v14 actually used spread-weight=3.0 and
#   deg-every-n-steps=2 (confirmed via diff of original run_train_v14.sh).
#
#   These weaker settings were the root cause of v18 score collapse (q_std≈0.01):
#   - L_spread at 2.0 was insufficient to force per-image quality differentiation
#   - L_deg at every 4 steps provided half the degradation grounding vs v14
#   - Combined result: backbone learned no quality-discriminative features → inference collapse
#
# v19 fix (T34): use v14 ACTUAL hyperparameters
#
#   T34a — --spread-weight 3.0: Restore v14 value (v18 used 2.0, docs erroneously said "v14=~2.0").
#           Stronger L_spread gradient forces backbone to differentiate quality across images.
#
#   T34b — --deg-every-n-steps 2: Restore v14 value (v18 used 4).
#           2× more frequent L_deg provides stronger ordinal quality signal.
#
#   KEPT from v18:
#   - --no-mat-stats: Required to reproduce v14 pre-T31 behavior with current code.
#     v14 predated per-identity cosine stats (T31). Current code default tries to compute
#     stats; --no-mat-stats disables this, giving same raw cosine L_mat as original v14.
#   - --sd302-concept-weight 0.0: disable SD302 concept-only L_deg (v14 default)
#   - --concept-deg-gamma 0.5: v14 default
#   - SD302-A included (302a challengers): added in v18, kept here for better sensor coverage
#   - --batch-size 128 on --gpus 0 (v14 used 96×2 GPU, effective per-GPU is similar)
#
# Expected improvements vs v18:
#   - Score spread: q_std > 10 at inference (like v14 ~15.5)
#   - Track 2 mean_KS: ~0.26 (v14 baseline, not false positive like v18)
#   - Track 2 Pearson: ~0.29 (v14 baseline)
#   - Score range: 10–90 (vs v18: 54.62–54.65 collapsed)
#
# Train from scratch, 60 epochs, LR=1e-4 cosine.

set -euo pipefail

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

VERSION="v19"
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 128 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 3.0 \
  --concept-deg-gamma 0.5 \
  --sd302-concept-weight 0.0 \
  --deg-every-n-steps 2 \
  --no-mat-stats \
  --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