#!/usr/bin/env bash
# v28 training — Matcher-free: L_mat disabled (--no-mat).
#
# Goal: Validate that L_sens + L_deg + L_ortho + L_spread are sufficient
#       for sensor-invariant quality ordering without any external matcher teacher.
#
# Key change from v26:
#   --no-mat          : skip MDGT entirely (no loading, no emb_cache, no prototypes)
#                       l_mat = 0.0 for all steps/epochs; stage scheduler still runs
#                       normally for beta/gamma (sensor + degradation weights unaffected)
#   --spread-weight 4.0 : slightly stronger spread to compensate for lost L_mat anchor
#   NOTE: do NOT add --fixed-alpha 0.0 — that overrides beta/gamma too (-1.0 default),
#
# Paper argument if this works:
#   "SIFQ quality emerges from degradation ordering and sensor consistency alone,
#    without any external matcher supervision. This scorer-free quality generalises
#    across matchers (Track 1 ERC) with no matcher-specific bias."
#
# Expected dynamics:
#   S1 (ep 0-9):  q_std grows from L_deg + L_spread (no L_mat anchor — may be slower)
#   S2+ (ep 20+): L_sens stabilises sensor gap; L_deg maintains ordinal grounding
#   Risk: without L_mat, collapse is possible if L_deg/L_spread insufficient.
#         Watch q_std and l_spread in logs. If q_std < 5 at ep15, training failed.

set -euo pipefail

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

VERSION="v28"
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 "${REPO_ROOT}/dataset/PolyU" \
  --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
  --no-mat \
  --epochs 60 \
  --batch-size 96 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 4.0 \
  --concept-deg-gamma 2.0 \
  --sd302-concept-weight 0.0 \
  --deg-every-n-steps 2 \
  --k-cross 0 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0 \
  --save-dir "${SAVE_DIR}" \
  2>&1 | tee "${LOG_FILE}"

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