File size: 3,480 Bytes
cfc7a54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | #!/usr/bin/env bash
# v19 training — T34: true revert to v14 hyperparameters (fix v18 misconfiguration).
#
# Root cause analysis (v18 failure):
#
# v18 claimed to "revert to v14 design" but used WRONG hyperparameters:
# v18: --spread-weight 2.0 (v14 actual: 3.0 ← wrong)
# v18: --deg-every-n-steps 4 (v14 actual: 2 ← wrong)
# v18 comments were incorrect — v14 actually used spread-weight=3.0 and
# deg-every-n-steps=2 (confirmed via diff of original run_train_v14.sh).
#
# These weaker settings were the root cause of v18 score collapse (q_std≈0.01):
# - L_spread at 2.0 was insufficient to force per-image quality differentiation
# - L_deg at every 4 steps provided half the degradation grounding vs v14
# - Combined result: backbone learned no quality-discriminative features → inference collapse
#
# v19 fix (T34): use v14 ACTUAL hyperparameters
#
# T34a — --spread-weight 3.0: Restore v14 value (v18 used 2.0, docs erroneously said "v14=~2.0").
# Stronger L_spread gradient forces backbone to differentiate quality across images.
#
# T34b — --deg-every-n-steps 2: Restore v14 value (v18 used 4).
# 2× more frequent L_deg provides stronger ordinal quality signal.
#
# KEPT from v18:
# - --no-mat-stats: Required to reproduce v14 pre-T31 behavior with current code.
# v14 predated per-identity cosine stats (T31). Current code default tries to compute
# stats; --no-mat-stats disables this, giving same raw cosine L_mat as original v14.
# - --sd302-concept-weight 0.0: disable SD302 concept-only L_deg (v14 default)
# - --concept-deg-gamma 0.5: v14 default
# - SD302-A included (302a challengers): added in v18, kept here for better sensor coverage
# - --batch-size 128 on --gpus 0 (v14 used 96×2 GPU, effective per-GPU is similar)
#
# Expected improvements vs v18:
# - Score spread: q_std > 10 at inference (like v14 ~15.5)
# - Track 2 mean_KS: ~0.26 (v14 baseline, not false positive like v18)
# - Track 2 Pearson: ~0.29 (v14 baseline)
# - Score range: 10–90 (vs v18: 54.62–54.65 collapsed)
#
# Train from scratch, 60 epochs, LR=1e-4 cosine.
set -euo pipefail
REPO_ROOT="$(cd "$(dirname "$0")/../.." && pwd)"
export PATH="/home/aiserver/miniconda3/bin:$PATH"
VERSION="v19"
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 "${REPO_ROOT}/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt" \
--epochs 60 \
--batch-size 128 \
--image-size 224 \
--lr 1e-4 \
--spread-mode uniform \
--spread-weight 3.0 \
--concept-deg-gamma 0.5 \
--sd302-concept-weight 0.0 \
--deg-every-n-steps 2 \
--no-mat-stats \
--max-train-samples -1 \
--num-workers 8 \
--gpus 0 \
--save-dir "${SAVE_DIR}"
echo "[auto-eval] Training done. Starting eval ${VERSION}..."
bash "${EVAL_SCRIPT}" >> "${LOG_FILE}" 2>&1
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