# SIFQ — Plan & Experiment Tracker

Dùng file này để: ghi kế hoạch trước khi thay đổi code, track tiến độ, và revert khi cần.

---

## Active Experiments

### v32 — Done ❌
**Goal:** Fix two concept grounding failures in v31 via T42 concept map
**Key changes from v31:** T42 concept map (2 changes):
- `dry_skin: [4, 2, 0] → [4, 0]` — remove continuity
- `noise: [3] → [1, 3]` — add clarity as co-target
**Script:** `scripts/run_train_v32.sh`
**Result:** KS=0.1375, Pearson=0.7892 — Track 2 regression vs v31 (KS +0.022, Pearson -0.097)
**Track 4 regressions vs v31:**
- blur→clarity: +0.046 ✗ (was -0.305 ✓)
- noise→clarity: +0.114 ✗ (was -0.236 ✓)
- noise→noise_level: +0.121 ✗ (was -0.081 ✓)
- occlusion→minutiae: +0.121 ✗ (was -0.123 ✓)
- wet_press→minutiae: +0.676 ✗ (was -0.695 ✓)
**Root cause:** Adding clarity[1] to noise map caused clarity concept to absorb noise-degradation gradient, destroying its response to blur. Removing continuity[2] from dry_skin destabilised concept interactions across other degradation types.
**Reverted:** concept map → T39 (restored `noise:[3]`, `dry_skin:[4,2,0]`)
**Status:** Failed — reverted to T39 concept map

---

### v31 — Done ✅
**Goal:** Benchmark DINOv2-ViTS/14 as public teacher replacing unpublished MDGT
**Key change from v28:** `--teacher dinov2` (frozen DINOv2-ViTS/14 CLS token [B,384])
**Script:** `scripts/run_train_v31.sh`
**Result:** KS=0.1152, Pearson=0.8858, q_std≥15 ✅ — **best Track 2 so far**
**Concept issues:** dry_skin→noise_level Spearman -0.758 (spurious, noise_level NOT in target);
blur→continuity +0.214 (wrong direction); noise→noise_level -0.081 (weak signal)
**Status:** Complete

---

### v29 — Done ❌
**Goal:** Fix concept grounding failures in v28 — T41 concept map + anti-saturation losses
**Key changes from v28:**
- T41 concept map: jpeg→[1], dry_skin→[4,0], noise→[1,3] (fix gradient conflicts + noise inversion)
- `--ortho-weight 3.0` (vs 1.0 default) — push saturated concepts apart
- `--concept-spread-weight 1.0` — penalise concepts with batch std < 0.20
**Script:** `scripts/run_train_v29.sh`
**Result:** KS=0.2985, Pearson=0.1448 — catastrophic sensor invariance regression
**Root cause of failure:** `--concept-spread-weight 1.0` forces per-batch concept diversity
which amplifies sensor-specific texture features, destroying cross-sensor score alignment.
G_roll_png and H_roll_png particularly affected (KS 0.86 and 0.77 vs other sensors).
**Status:** Failed — reverted to v28 code base (train_sifq.py, degradation_ranking.py T39)

---

### v28 — Done ✅
**Goal:** Validate matcher-free quality learning — L_mat disabled entirely (`--no-mat`)
**Key change from v26:** No MDGT teacher. Loss = L_sens + L_deg + L_ortho + L_spread only.
**Script:** `scripts/run_train_v28.sh`
**Result:** KS=0.1346, Pearson=0.7645, q_std~23.2 (60 epochs)
**Concept issues found:** continuity collapsed (mean=0.068), noise_level inverted (+0.56), orient_coh saturated (0.94), contrast_uni saturated (0.85), clarity flat (std=0.043). Only minutiae_rel discriminating.
**Paper claim:** ✅ "SIFQ quality is self-supervised — no external matcher needed" validated by KS close to v24
**Status:** Complete → concept grounding needs fix (next experiment TBD)

---

### v26 — Đang train 🚀
**Goal:** Verify v25 regression root cause = gamma=1.5 (not T40 concept map)  
**Key change from v25:** `--concept-deg-gamma 2.0` (restored), DEGRADATION_CONCEPT_MAP reverted to T39  
**Script:** `scripts/run_train_v26.sh`  
**Log:** `logs/train_v26.log`  
**Expected:** KS ≈ 0.126, Pearson ≈ 0.80 (match v24)  
**Eval:** `scripts/run_eval_v26.sh` (auto-runs after training)  
**Status:** Training in progress

### v27 — Pending ⏳
**Goal:** Validate SpatialConceptHead (14×14 spatial tokens) improves concept grounding  
**Key change from v26:** `--spatial-concept-head` flag — `SpatialConceptHead` replaces `ConceptHead`  
**Script:** `scripts/run_train_v27.sh`  
**Expected:** KS ≈ v26, Track 4 diagonal stronger (especially orientation_coherence, continuity, minutiae_reliability)  
**Blocker:** Wait for v26 to confirm KS/Pearson target first  
**Status:** Code ready, not launched

---

## Code Change Log

### 2026-06-04 — T42 concept map: 2 changes for v32

**Files changed:**
- `src/losses/degradation_ranking.py` — T42 DEGRADATION_CONCEPT_MAP:
  - `dry_skin: [4,2,0] → [4,0]` (removed continuity [2])
  - `noise: [3] → [1,3]` (added clarity [1] as co-target)

**Root cause (dry_skin):** v31 shows noise_level (c=3) Spearman -0.758 with dry_skin (NOT in target).
Continuity [2] shares backbone patch-scale features (~16×16) with noise texture → cross-activation.
Fix: use contrast[4] + orientation[0] (regional features at multi-patch scale) only.

**Root cause (noise):** v31 shows noise→noise_level Spearman only -0.081 (nearly no signal).
TinyViT 16×16 patch embed averages out pixel Gaussian noise (σ=5–30) → concept[3] gradient ≈ 0.
Adding clarity[1] anchors noise degradation to ridge-valley blur (detectable at patch scale).

**Backward compatibility:** ⚠️ Modifies shared DEGRADATION_CONCEPT_MAP.

**Revert T42 → T39:**
```python
"noise":     [3],
"dry_skin":  [4, 2, 0],
```

---

### 2026-06-03 — DINOv2Teacher: replace MDGT with public DINOv2-ViTS/14

**Files changed:**
- `src/training/mdgt_teacher.py` — Added `DINOv2Teacher` class: frozen DINOv2-ViTS/14 (via `torch.hub`), handles grayscale→RGB channel repeat + ImageNet normalization internally, returns L2-normalized [B, 384] CLS embeddings. `MDGTCheckpointTeacher` unchanged.
- `src/train.py` — Added `--teacher {dinov2,mdgt}` arg (default=`dinov2`). Import `DINOv2Teacher`. Instantiation in `main()` dispatches on `args.teacher`. `--mdgt-checkpoint` arg remains but only used when `--teacher=mdgt`.

**Root cause / motivation:** MDGT is unpublished work → not reproducible by reviewers → academic integrity risk. DINOv2-ViTS/14 is public (Meta, ICLR 2024), cite-able, and `torch.hub` reproducible.

**Expected Pearson impact:** DINOv2 raw → expect Pearson ~0.55–0.70 vs 0.80 with MDGT. Run v31 to benchmark.

**Backward compatibility:** ✅ All existing scripts using `--teacher mdgt --mdgt-checkpoint <path>` unaffected.

**Revert:**
```bash
# train.py: change --teacher default back to "mdgt"
# or pass --teacher mdgt --mdgt-checkpoint <ckpt_path> explicitly
```

---

### 2026-06-02 — T41 concept map + anti-saturation losses (v29)

**Files changed:**
- `src/losses/degradation_ranking.py` — T41 DEGRADATION_CONCEPT_MAP:
  - `jpeg: [2,1] → [1]` (remove continuity — JPEG artifacts wrong gradient direction in v28)
  - `dry_skin: [4,2,0] → [4,0]` (remove continuity — gradient conflict, Spearman+0.10 wrong)
  - `noise: [3] → [1,3]` (add clarity co-target — noise blurs ridges, anchors concept 3)
- `scripts/train_sifq.py` — Added `--ortho-weight` (default 1.0) and `--concept-spread-weight` (default 0.0) args; l_orth now weighted; per-concept spread loss (std < 0.20 → penalty) added to total loss; logged as `l_cspread` in running dict and epoch print

**Root cause:** v28 concept collapse/inversion diagnosed via inference stats:
  - continuity std=0.076, mean=0.068 → dead concept (3 conflicting grad sources: blur✓, jpeg✗, dry_skin✗)
  - noise_level Spearman +0.56 → inverted (Gaussian noise increases texture energy in TinyViT)
  - orient_coh mean=0.942, contrast_uni mean=0.851 → L_ortho=1.0 too weak to break saturation

**Backward compatibility:** ✅ `--ortho-weight` default=1.0, `--concept-spread-weight` default=0.0 → all v16-v28 unaffected.

**Revert T41 → T39:**
```python
# degradation_ranking.py DEGRADATION_CONCEPT_MAP:
"noise":     [3],
"jpeg":      [2, 1],
"dry_skin":  [4, 2, 0],
```

---

### 2026-06-02 — --no-mat flag (v28 teacher-free experiment)

**Files changed:**
- `scripts/train_sifq.py` — Added `--no-mat` flag; MDGT teacher/emb_cache/prototypes wrapped in `if not args.no_mat`; training loop forces `l_mat = 0.0` when flag is set

**Backward compatibility:** ✅ Default `--no-mat=False` — all existing versions unaffected.

**Revert:** Remove `--no-mat` block in `parse_args()` and restore unconditional MDGT instantiation in `main()`.

---

### 2026-06-01 — SpatialConceptHead

**Files changed:**
- `src/models/concept_head.py` — Added `SpatialConceptHead` class
- `src/models/sifq.py` — `SIFQ.forward()` dispatches via `concept_head.uses_spatial`
- `src/models/__init__.py` — Export `SpatialConceptHead`
- `scripts/train_sifq.py` — `--spatial-concept-head` flag (default=False)
- `src/train.py` — same flag
- `scripts/run_eval.py` — auto-detect from `ckpt["config"]["spatial_concept_head"]`
- `scripts/run_infer.py` — same auto-detect
- `rules/SIFQ_explained.md` — Section 2.2 updated, diagram updated, File Map updated


**Backward compatibility:** ✅ All v16–v26 checkpoints load cleanly without flag.

---

### 2026-06-01 — T39 concept map revert + gamma restore (v26)

**Problem:** v25 used gamma=1.5 AND T40 concept map. Regression KS 0.126→0.213.  
**Root cause identified:** gamma=1.5 too weak during S1→S2 ramp.  
  - v24 (gamma=2.0): spread stable ~0.003, q_std grows 18→22 monotonically
  - v25 (gamma=1.5): spread spikes to 0.028, q_std collapses 22→10 (epochs 13–16)

**Files changed:**
- `src/losses/degradation_ranking.py` — T40 reverted to T39 DEGRADATION_CONCEPT_MAP
- `scripts/run_train_v26.sh` — `--concept-deg-gamma 2.0`

**DEGRADATION_CONCEPT_MAP T39 (current, correct):**
```python
"blur":      [1, 2]
"noise":     [3]
"jpeg":      [2, 1]
"occlusion": [5]
"dry_skin":  [4, 2, 0]
"wet_press": [1, 5, 0]
```

**Revert target:** T40 map (v25):
```python
"blur":      [1, 2, 0]    # + orient_coh
"noise":     [3, 4]       # + contrast_u
"jpeg":      [2, 1, 4]    # + contrast_u
"occlusion": [5]
"dry_skin":  [4, 2, 0]
"wet_press": [1, 5, 0]
```
**Do NOT revert to T40** unless v26 confirms gamma=2.0 alone is insufficient and T39 concept grounding is weaker than expected.

---

## Revert Cookbook

### Revert concept map to previous version
```bash
# Check what the map looked like in a specific git commit:
git log --oneline src/losses/degradation_ranking.py
git show <commit>:sifq/src/losses/degradation_ranking.py | grep -A 30 "DEGRADATION_CONCEPT_MAP"

# Edit directly:
# src/losses/degradation_ranking.py — DEGRADATION_CONCEPT_MAP dict
```

### Revert to v24 hyperparameters (known good baseline)
```bash
# Key v24 flags (from scripts/run_train_v24.sh):
--concept-deg-gamma 2.0
--spread-weight 3.0
--spread-mode uniform
--deg-every-n-steps 2
--no-mat-stats
--proto-max-batches 0
--k-cross 0
--batch-size 96
```

### Load and inspect a checkpoint
```python
import torch
ckpt = torch.load("checkpoints/v24/last.pt", map_location="cpu", weights_only=False)
print(ckpt["metrics"])     # KS, Pearson, q_std, etc.
print(ckpt["config"])      # all argparse flags used
print(ckpt["epoch"])       # which epoch
```

### Compare two checkpoints' configs
```python
import torch, json
c24 = torch.load("checkpoints/v24/last.pt", map_location="cpu", weights_only=False)["config"]
c25 = torch.load("checkpoints/v25/last.pt", map_location="cpu", weights_only=False)["config"]
for k in c24:
    if c24.get(k) != c25.get(k):
        print(f"{k}: v24={c24.get(k)}  v25={c25.get(k)}")
```

### Run eval manually on any checkpoint
```bash
cd /home/aiserver/works/fingerprint

# Step 1: Inference
python sifq/scripts/run_infer.py \
    --checkpoint sifq/checkpoints/vXX/last.pt \
    --root-302a  dataset/302a/images/challengers \
    --root-302b  dataset/302b/images/baseline \
    --root-302d  dataset/nist_302d/images/auxiliary \
    --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
    --output sifq/eval_results/sifq_scores_vXX.jsonl

# Step 2: Track 2 + Track 4
python sifq/scripts/run_eval.py \
    --sifq-scores sifq/eval_results/sifq_scores_vXX.jsonl \
    --checkpoint  sifq/checkpoints/vXX/last.pt \
    --out-dir     sifq/eval_results/vXX \
    --exclude-sensor "R_1000_slap,R_500_slap,S_500_slap" \
    --skip-track1
```

### Smoke test (~2 min)
```bash
cd /home/aiserver/works/fingerprint
bash sifq/scripts/run_smoke.sh
```

---

## Decision Log

### Why not T40 concept map?
T40 added `orient_coh` to blur/noise/jpeg. Reverted in v26 because root cause of v25 regression was **gamma=1.5**, not concept map. T40 change had no confirmed benefit. Defer until v26 evaluation is done.

### Why FVC-only L_deg (not SD302)?
SD302 images are all high quality by protocol → clean images satisfy L_rank at a single ~53 attractor → blocks quality discrimination signal from L_mat. FVC has genuine quality variation (8 impressions/subject) → MDGT cosine varies → L_mat gradient meaningful → transfers to SD302 at inference. (v20 root cause analysis)

### Why --no-mat-stats?
Per-identity cosine stats (T31) created asymmetry: FVC has stable tanh targets, SD302 gets raw cosine ~0.85 constant. Backbone learned "FVC=quality-variable, SD302=fixed" → no L_mat gradient for SD302 → GRL destroyed SD302 features. (v17 root cause)

### Why --k-cross 0?
k_cross>0 forces k guaranteed cross-sensor pairs per batch → over-constrains L_sens → backbone over-optimises sensor invariance → loses quality discrimination → Pearson collapses to ~0.09. (v21 root cause)

### Why --proto-max-batches 0?
Partial prototypes (150 batches = 45% data) → SD302 identities have 2–3 sensor prototypes instead of full 19-sensor → cosine correlates with WHICH sensors in prototype window (sensor-biased) → no quality gradient for SD302. (v19/v20 root cause)

### Why gamma must be ≥ 2.0?
During S1→S2 ramp, mat loss is introduced alongside existing deg loss. If deg loss too weak (gamma=1.5), mat loss dominates momentarily → spread spikes → model partially collapses and never fully recovers. gamma=2.0 keeps deg strong enough to maintain ordinal grounding through the ramp. (v25 root cause)

---

## Pending Research Questions

- [ ] **v26 eval:** Does restoring gamma=2.0 fully recover v24 KS/Pearson? (expected: yes)
- [ ] **v27 eval:** Does SpatialConceptHead improve Track 4 diagonal ρ vs v26?
- [ ] **noise→noise_level:** v24 Track 4 shows +0.365 (wrong direction). Defer to v28.
- [ ] **T40 revisit:** Once v26/v27 stable, evaluate if blur/noise/jpeg → orient_coh improves orientation_coherence grounding.