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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
# 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
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