File size: 2,435 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 | #!/usr/bin/env bash
# v25 training — T40 fix: address 3 remaining Track 4 failures from v24.
#
# Changes from v24:
# 1. DEGRADATION_CONCEPT_MAP (T40 in src/losses/degradation_ranking.py):
# blur: [1, 2] → [1, 2, 0] (+orient_coh: blur smears orientation fields)
# noise: [3] → [3, 4] (+contrast_u: noise disrupts local contrast)
# jpeg: [2, 1] → [2, 1, 4] (+contrast_u: JPEG blocking creates contrast bands)
# dry_skin/wet_press/occlusion: unchanged from T39
#
# 2. --concept-deg-gamma 1.5 (down from 2.0)
# gamma=2.0 caused noise→noise_lv REGRESSION (+0.365 in v24).
# gamma=1.5 keeps enough signal for blur/jpeg (needed >0.5) while
# reducing saturation pressure that triggered noise_lv inversion.
#
# Expected improvements:
# noise → noise_lv: +0.365 → negative (gamma reduced + noise now 2-concept)
# dry_skin → contrast_u: +0.051 → negative (3 degradations now target contrast_u)
# dry_skin → orient_coh: +0.008 → negative (blur gives orient_coh strong signal)
set -euo pipefail
REPO_ROOT="$(cd "$(dirname "$0")/../.." && pwd)"
export PATH="/home/aiserver/miniconda3/bin:$PATH"
VERSION="v25"
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 "${REPO_ROOT}/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt" \
--epochs 60 \
--batch-size 96 \
--image-size 224 \
--lr 1e-4 \
--spread-mode uniform \
--spread-weight 3.0 \
--concept-deg-gamma 1.5 \
--sd302-concept-weight 0.0 \
--deg-every-n-steps 2 \
--no-mat-stats \
--proto-max-batches 0 \
--k-cross 0 \
--max-train-samples -1 \
--num-workers 8 \
--gpus 0 \
--save-dir "${SAVE_DIR}"
echo "[auto-eval] Training done. Starting eval ${VERSION}..."
bash "${EVAL_SCRIPT}" >> "${LOG_FILE}" 2>&1
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