File size: 4,352 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 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | #!/usr/bin/env bash
# v20 training — T35: full prototypes + L_deg on SD302.
#
# Root cause analysis (v19 failure — same collapse as v15–v18):
#
# v19 was supposed to reproduce v14 exactly but STILL collapsed (q_std≈0.01
# at inference, all scores 53.1x). This means the "v14 hyperparameter diff"
# explanation (spread-weight and deg-every-n-steps) was wrong. Two deeper root
# causes identified by analysing every code change since v14:
#
# Root cause 1 — Truncated prototypes (T35a):
# v14's code had NO --proto-max-batches cap; prototypes were computed on the
# full dataset (42,683 images, ~334 batches). When this parameter was added
# (default 150 batches = ~19,200 images, 45% of data), SD302 identities went
# from having full 19-sensor multi-sensor prototypes to 2–3 sensor partial
# prototypes.
# Full prototype → raw cosine varies with image quality across all sensors
# (quality-discriminative, sensor-invariant).
# Partial proto → raw cosine correlated with which sensors are in the
# prototype window (sensor-biased, quality-agnostic) →
# no per-image quality gradient for SD302 → collapse.
#
# Root cause 2 — FVC-only L_deg (T27, unchanged since v13):
# T27 reverted T17 (L_deg on all images) because clean SD302 images anchored
# at ~28 when there was no per-dataset spread. T27 itself introduced
# per-dataset L_spread_ds. Now that L_spread_ds forces SD302 to span [10,90],
# re-enabling full L_rank for SD302 is safe: L_spread_ds prevents the
# anchoring, and L_rank orders SD302 images by their synthetic degradation
# response (a proxy for ridge clarity / quality). This gives the ONLY stable
# per-image quality signal for SD302 at inference.
#
# v20 fixes (T35):
# T35a — --proto-max-batches 0: Compute prototypes on the full training set.
# Full multi-sensor prototypes restore per-image L_mat quality signal
# for SD302 (raw cosine varies with quality, not sensor).
# Cost: ~10–15 extra minutes at epoch 0 for prototype computation.
#
# T35b — --deg-include-sd302: Apply full L_deg (L_rank + L_concept) to SD302
# images in addition to FVC. L_spread_ds (present since v13) prevents
# score anchoring at ~28. Provides robust per-image ordinal quality
# grounding for SD302 independent of root cause 1.
#
# All other settings kept from v19 (which correctly reproduced v14 hyperparams):
# --spread-weight 3.0 --deg-every-n-steps 2 --no-mat-stats
# --concept-deg-gamma 0.5 --sd302-concept-weight 0.0
# --batch-size 128 --gpus 0 --epochs 60
#
# Expected improvements vs v19:
# q_std (inference) : >12 (v19: ~0.01)
# Score range : 10–90 (v19: 53.10–53.16)
# Track 2 mean_KS : ≤0.27 real (v19: 0.3706 with collapsed distributions)
# Track 2 Pearson : ≥0.25 (v19: -0.0123)
set -euo pipefail
REPO_ROOT="$(cd "$(dirname "$0")/../.." && pwd)"
export PATH="/home/aiserver/miniconda3/bin:$PATH"
VERSION="v20"
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 "${REPO_ROOT}/pad/TRAM-downstream/checkpoint/checkpoints_dinov2_tram/best_eer.pt" \
--epochs 60 \
--batch-size 128 \
--image-size 224 \
--lr 1e-4 \
--spread-mode uniform \
--spread-weight 3.0 \
--concept-deg-gamma 0.5 \
--sd302-concept-weight 0.0 \
--deg-every-n-steps 2 \
--deg-include-sd302 \
--no-mat-stats \
--proto-max-batches 0 \
--max-train-samples -1 \
--num-workers 8 \
--gpus 0 \
--save-dir "${SAVE_DIR}"
echo "[auto-eval] Training done. Starting eval ${VERSION}..."
bash "${EVAL_SCRIPT}" >> "${LOG_FILE}" 2>&1
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