#!/usr/bin/env bash
# v21 training — T36: full prototypes + FVC-only L_deg (remove --deg-include-sd302).
#
# Root cause analysis (v20 failure — same collapse as v15–v19):
#
#   v20 training showed healthy q_std≈18.3 across all 60 epochs but INFERENCE
#   collapsed (all SD302 sensors score ~53.38, q_std≈0.007, concepts stuck at
#   [0.994, 0.008, ...]).  The T35 diagnosis was right about truncated
#   prototypes (root cause 1) but the second fix (--deg-include-sd302) was
#   WRONG and caused the collapse:
#
#   Root cause — L_rank on SD302 creates a stable attractor at ~53.38 (T35b was wrong):
#     SD302 images are ALL high quality (controlled NIST acquisition protocol).
#     With --deg-include-sd302, L_rank says:
#         Q(clean_SD302) > Q(low_deg_SD302) + m
#         Q(low_deg_SD302) > Q(high_deg_SD302) + m
#     The model satisfies this constraint by assigning a single "clean score" (~53.38)
#     to ALL clean SD302 images and lower scores to degraded variants.  There is
#     NO gradient pushing different clean SD302 images apart — L_rank only requires
#     clean > degraded, not that clean images differ from each other.  Combined with
#     L_spread_ds (batch-level, arbitrary ordering) and L_pair (same-identity
#     same-score pressure), the model collapses all clean SD302 to ~53.38 at inference.
#
#   Why v14 worked without --deg-include-sd302:
#     FVC has GENUINE quality variation (8 impressions per subject with naturally
#     different quality — blur, dry, wet, pressure).  MDGT raw cosine to prototype
#     GENUINELY varies (~0.75 worst to ~0.93 best) for FVC subjects.  L_mat gradient
#     on FVC shapes the backbone to be quality-discriminative.  At inference on SD302,
#     the backbone's learned quality features transfer (ridge clarity, noise level,
#     ridge continuity) → SD302 images scored by intrinsic quality, not batch rank.
#     This transfer mechanism is BLOCKED when L_rank on SD302 creates the ~53.38
#     attractor that overrides the learned quality representation.
#
# v21 fix (T36):
#   T36 — Remove --deg-include-sd302. Revert to FVC-only L_deg (T27 original design).
#   Keep --proto-max-batches 0 (T35a, the correct fix from v20).
#   L_deg applied only to FVC images: genuine quality variation → genuine L_mat
#   gradient → backbone learns quality features that transfer to SD302 at inference.
#   L_spread_ds still applied to SD302 to force batch-level spread (prevents batch-
#   collapse without creating the attractor problem).
#
# All other settings kept from v20:
#   --no-mat-stats  --spread-weight 3.0  --deg-every-n-steps 2
#   --concept-deg-gamma 0.5  --sd302-concept-weight 0.0
#   --batch-size 128  --gpus 0  --epochs 60
#   SD302-A+B+D included (full 30K sensor-invariant images, 19 sensors)
#
# Expected improvements vs v20:
#   q_std (inference)  : >12   (v20: ~0.007)
#   Score range        : 10–90 (v20: 53.20–53.41)
#   Track 2 mean_KS    : ≤0.30 (v20: 0.4936 — driven by sensor clusters, not quality)
#   Track 2 Pearson    : ≥0.20 (v20: 0.0048)
#   Concept grounding  : blur→clarity<0, noise→noise_level<0, etc.

set -euo pipefail

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

VERSION="v21"
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 \
  --proto-max-batches 0 \
  --max-train-samples -1 \
  --num-workers 8 \
  --gpus 0,1 \
  --save-dir "${SAVE_DIR}"

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