scripts/run_train_v31.sh

#!/usr/bin/env bash
# v31 training — raw DINOv2-ViTS/14 teacher (replaces MDGT)
#
# Goal: Benchmark DINOv2 raw as teacher for L_mat.
#   - DINOv2-ViTS/14 is public (Meta, ICLR 2024), cite-able, fully reproducible
#   - Replaces unpublished MDGT checkpoint → paper can be submitted without IP issues
#   - Expected Pearson ~0.55–0.70 (vs 0.80 with MDGT) — establish baseline
#
# Key change vs v29:
#   --teacher dinov2_raw : frozen DINOv2-ViTS/14 CLS token [B,384] as L_mat teacher
#                          (v29 used --no-mat entirely; v31 restores L_mat with public teacher)
#   remove --no-mat      : L_mat is re-enabled
#   --no-mat-stats       : raw cosine targets (no per-identity tanh stats) — v14 behaviour
#                          avoids FVC/SD302 asymmetry that caused collapse in v16/v17
#   --proto-max-batches 0: full dataset prototypes (avoid sensor-biased partial prototypes)
#
# Inherited from v29 (validated):
#   --spread-weight 4.0
#   --concept-deg-gamma 2.0
#   --ortho-weight 3.0
#   --k-cross 0
#   --deg-every-n-steps 2
#
# Expected dynamics:
#   S1 (ep 0-9):  q_std rises 0→15+; l_mat decreases (DINOv2 cosine varies with quality)
#   S2 (ep 20+):  l_pair decreases; l_mat converging; q_std stable 15–22
#   Target:       KS ≤ 0.20, Pearson ≥ 0.55, q_std ≥ 15

set -euo pipefail

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

VERSION="v31"
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/nist302a/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 "${REPO_ROOT}/dataset/PolyU" \
  --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
  --teacher dinov2_raw \
  --dinov2-model dinov2_vits14 \
  --no-mat-stats \
  --proto-max-batches 0 \
  --epochs 60 \
  --batch-size 96 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 4.0 \
  --concept-deg-gamma 2.0 \
  --sd302-concept-weight 0.0 \
  --deg-every-n-steps 2 \
  --k-cross 0 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0 \
  --save-dir "${SAVE_DIR}" \
  --wandb \
  --wandb-offline \
  --wandb-project "sifq" \
  --wandb-run-name "${VERSION}" \
  2>&1 | tee "${LOG_FILE}"

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

# ── Compact epoch log ──────────────────────────────────────────────────────
EPOCH_LOG="${REPO_ROOT}/sifq/logs/train_${VERSION}_epochs.log"
{
  grep -E "^(Device:|SD302|FVC|PolyU|Excluded|AMP|Sensor|Stage|Pre-cach|Teacher)" "${LOG_FILE}" | head -10
  echo "---"
  grep "^Epoch" "${LOG_FILE}"
} > "${EPOCH_LOG}" 2>/dev/null || true
echo "[done] Compact epoch log: ${EPOCH_LOG}"