#!/usr/bin/env bash
# Fine-tune SIFQ from a trained checkpoint to fix bimodal Q-score distribution.
#
# Key changes vs. full training:
#   - Resumes from checkpoints_full_v2/last.pt (50-epoch trained weights)
#   - Uniformity loss replaces bimodal-prone spread loss (already in train_sifq.py)
#   - Lower LR (2e-5) to avoid disturbing learned features
#   - Fixed loss weights: α=0.4 β=0.5 γ=0.05  (quality + sensor, minimal degradation)
#   - 30 epochs → ~1.5h
#
# Usage:
#   bash sifq/scripts/run_finetune.sh
#   bash sifq/scripts/run_finetune.sh --epochs 20   # shorter run
set -euo pipefail

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
ROOT="$(cd "$SCRIPT_DIR/.." && pwd)"
VENV="$ROOT/../.venv"

[[ -f "$VENV/bin/activate" ]] && source "$VENV/bin/activate"

python "$SCRIPT_DIR/train_sifq.py" \
  --root-302a  /home/aiserver/works/fingerprint/dataset/302a/images/challengers \
  --root-302b  /home/aiserver/works/fingerprint/dataset/302b/images/baseline \
  --root-302d  /home/aiserver/works/fingerprint/dataset/nist_302d/images/auxiliary \
  --mdgt-checkpoint /home/aiserver/works/fingerprint/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt \
  --resume     "$ROOT/checkpoints_full_v2/last.pt" \
  --epochs     30 \
  --batch-size 64 \
  --image-size 224 \
  --lr         2e-5 \
  --fixed-alpha 0.4 \
  --fixed-beta  0.2 \
  --fixed-gamma 0.05 \
  --max-train-samples -1 \
  --num-workers 4 \
  --gpus "0,1" \
  --save-dir   "$ROOT/checkpoints_full_v3" \
  "$@"