#!/usr/bin/env bash
# v29 training — T41 concept map + ortho-weight 3.0 + concept-spread-weight 1.0
#
# Goal: Fix concept grounding failures observed in v28 eval:
#   - continuity collapsed (mean=0.068, std=0.076) due to JPEG+dry_skin gradient conflict
#   - noise_level inverted (Spearman +0.56) due to Gaussian noise boosting texture energy
#   - orient_coh, contrast_uni saturated (~0.94, ~0.85) due to weak L_ortho
#   - clarity flat (std=0.043) due to insufficient training signal
#
# T41 concept map changes vs T39:
#   jpeg:     [2, 1] → [1]      remove continuity — JPEG artifacts go wrong direction
#   dry_skin: [4, 2, 0] → [4, 0] remove continuity — dry_skin gradient was wrong direction
#   noise:    [3] → [1, 3]      add clarity co-target — noise blurs ridges (anchor signal)
#
# New hyperparameters:
#   --ortho-weight 3.0         : 3× stronger concept de-correlation (vs 1.0 in v28)
#   --concept-spread-weight 1.0: penalise concepts with batch std < 0.20
#                               directly targets: orient_coh saturation, continuity collapse
#
# Inherited from v28 (validated):
#   --no-mat           : no MDGT teacher (KS=0.1346 vs v24=0.1263 — acceptable)
#   --spread-weight 4.0
#   --concept-deg-gamma 2.0
#   --k-cross 0
#
# Expected dynamics:
#   S1 (ep 0-9):  l_cspread decreases from ~0.1 toward 0; concepts spread to std ≥ 0.20
#   S2+ (ep 20+): orient_coh, contrast_uni no longer saturated; continuity varies with blur
#                 noise_level: co-target with clarity should reduce inversion
#   Target: KS ≤ 0.15, Pearson ≥ 0.75, concept stds all ≥ 0.15 at inference

set -euo pipefail

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

VERSION="v29"
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 "${REPO_ROOT}/dataset/PolyU" \
  --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
  --no-mat \
  --epochs 60 \
  --batch-size 96 \
  --image-size 224 \
  --lr 1e-4 \
  --spread-mode uniform \
  --spread-weight 4.0 \
  --ortho-weight 3.0 \
  --concept-spread-weight 1.0 \
  --concept-deg-gamma 2.0 \
  --sd302-concept-weight 0.0 \
  --deg-every-n-steps 2 \
  --k-cross 0 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0 \
  --save-dir "${SAVE_DIR}" \
  2>&1 | tee "${LOG_FILE}"

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

# ── Compact epoch log ──────────────────────────────────────────────────────
# Strip tqdm noise — keep only Epoch summary lines and header info.
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}"
} > "${EPOCH_LOG}" 2>/dev/null || true
echo "[train_${VERSION}] Compact epoch log → ${EPOCH_LOG}"