| import json, numpy as np |
| from scipy.stats import spearmanr |
| from datetime import datetime |
|
|
| records = [json.loads(l) for l in open('eval_results/sifq_scores_v24.jsonl')] |
| CNAMES = ['orientation_coherence','ridge_valley_clarity','continuity','noise_level','contrast_uniformity','minutiae_reliability'] |
| concepts = np.array([r['concepts'] for r in records]) |
| qs = np.array([r['q_score'] for r in records]) |
|
|
| d = json.load(open('eval_results/v24/eval_summary.json')) |
| t2 = d['track2_sensor_invariance']['SIFQ'] |
| t4 = d['track4_concept_grounding'] |
|
|
| TARGETS = { |
| 'blur': ['ridge_valley_clarity', 'continuity'], |
| 'noise': ['noise_level'], |
| 'jpeg': ['continuity', 'ridge_valley_clarity'], |
| 'occlusion': ['minutiae_reliability'], |
| 'dry_skin': ['contrast_uniformity', 'continuity', 'orientation_coherence'], |
| 'wet_press': ['ridge_valley_clarity', 'minutiae_reliability', 'orientation_coherence'], |
| } |
|
|
| SEP = '=' * 70 |
| SEP2 = '-' * 70 |
|
|
| lines = [ |
| SEP, |
| 'SIFQ -- Concept Quality Model: Ly thuyet & Ket qua (v24)', |
| f'Generated: {datetime.now().strftime("%Y-%m-%d %H:%M")}', |
| SEP, |
| '', |
| '1. DINH NGHIA 6 CONCEPTS', |
| SEP2, |
| 'Tat ca concepts: cao = chat luong tot hon, dau ra trong [0, 1].', |
| '', |
| ' # Concept Y nghia vat ly', |
| ' -- -------------------------- -----------------------------------------------', |
| ' 0 orientation_coherence Ridge flow nhat quan, local orientation field smooth', |
| ' 1 ridge_valley_clarity Bien ridge-valley sac net, contrast cuc bo cao', |
| ' 2 continuity Ridge lines lien tuc, khong bi dut gay', |
| ' 3 noise_level It nhieu ngau nhien (cao = it noise = tot)', |
| ' 4 contrast_uniformity Contrast deu tren toan foreground', |
| ' 5 minutiae_reliability Minutiae co the trich xuat chinh xac', |
| '', |
| ' Degradation Concept Map (v25 -- T40):', |
| ' blur -> [clarity[1], continuity[2], orient_coh[0]]', |
| ' noise -> [noise_level[3], contrast_u[4]]', |
| ' jpeg -> [continuity[2], clarity[1], contrast_u[4]]', |
| ' occlusion -> [minutiae_reliability[5]]', |
| ' dry_skin -> [contrast_u[4], continuity[2], orient_coh[0]]', |
| ' wet_press -> [clarity[1], minutiae_rel[5], orient_coh[0]]', |
| '', |
| ' Ly do thiet ke map nhu vay:', |
| ' - Moi concept phai duoc giam sat boi >= 2 loai degradation (tranh single-', |
| ' point-of-failure: 1 degradation target 1 concept -> signal yeu, de bi', |
| ' gradient interference invert chieu).', |
| ' - Chon degradation phu hop vat ly: jpeg blocking -> contrast bands (khong', |
| ' chi ridge artifacts), blur -> orientation blur (khong chi clarity loss).', |
| '', |
| '', |
| '2. CO CHE TRAINING CONCEPT (L_concept)', |
| SEP2, |
| 'Voi moi cap anh (mild degradation vs severe degradation cung loai):', |
| '', |
| ' L_concept = SUM Huber( c_mild[c], c_severe[c] + 0.1 )', |
| ' c in targets(degradation_type)', |
| '', |
| ' -> Anh degradation nhe phai co concept cao hon anh degradation nang >= 0.1', |
| ' -> Model hoc tung concept phan ung dung chieu voi loai hu hong tuong ung', |
| '', |
| 'Ket hop ranking loss:', |
| ' L_rank = relu(Q_severe - Q_mild + m) + relu(Q_mild - Q_clean + m)', |
| ' -> Q giam theo thu tu: clean > mild_deg > severe_deg', |
| '', |
| ' L_deg = L_rank + gamma * L_concept', |
| ' gamma = 2.0 (v24) -> 1.5 (v25)', |
| ' - gamma qua cao (2.0): gradient conflict qua manh -> noise_level inversion', |
| ' - gamma qua thap (0.5, v22): blur->continuity FAIL (+0.261)', |
| ' - gamma = 1.5: compromise, du manh cho blur/jpeg, khong gay inversion', |
| '', |
| '', |
| '3. VAN DE CONCEPT SATURATION', |
| SEP2, |
| 'Nguyen nhan goc: L_concept chi train tren synthetic degradation pairs.', |
| 'Voi real fingerprint images, KHONG co gradient dinh huong concept.', |
| '-> Concept troi ve gia tri mac dinh cua backbone features.', |
| '', |
| 'Hau qua trong v24 (42,683 real fingerprint images):', |
| '', |
| ' Concept mean std rho_Q Tinh trang', |
| ' ---------------------------- ------ ----- ------ ---------------------------', |
| ] |
|
|
| STATUS = { |
| 'noise_level': '!! DOMINATES Q (rho=-0.989)', |
| 'minutiae_reliability': '!! Saturated HIGH -- dead (range 0.84-0.93)', |
| 'orientation_coherence':'!! Saturated LOW -- T39 overcorrected', |
| 'ridge_valley_clarity': '!! Near-dead -- low variance', |
| 'continuity': '!! Near-dead -- low variance', |
| 'contrast_uniformity': '!! Near-dead -- low variance', |
| } |
|
|
| for i, name in enumerate(CNAMES): |
| c = concepts[:, i] |
| rho, _ = spearmanr(c, qs) |
| status = STATUS.get(name, 'OK') |
| lines.append(f' {name:<28} {c.mean():>6.3f} {c.std():>6.3f} {rho:>+7.3f} {status}') |
|
|
| lines += [ |
| '', |
| ' Vong lap nguy hiem (self-reinforcing collapse):', |
| ' ScoreAggregator chon noise_level (std=0.340, cao nhat)', |
| ' -> gradient tap trung update noise pathway', |
| ' -> cac concept khac it duoc update -> variance thap hon', |
| ' -> cang bi bo qua -> variance cang thap (vong lap)', |
| '', |
| ' He qua: Q ≈ f(noise_level) -- model thuc chat la "noise detector",', |
| ' khong phai "quality estimator" da khai niem.', |
| '', |
| '', |
| '4. KET QUA v24', |
| SEP2, |
| ] |
|
|
| ks = t2['mean_ks_across_sensors'] |
| pearson = t2['cross_sensor_pearson'] |
| lines += [ |
| ' Track 2 -- Sensor Invariance:', |
| f' Mean KS (cross-sensor) = {ks:.4f} [goal <= 0.30] {"PASS" if ks <= 0.30 else "FAIL"}', |
| f' Cross-sensor Pearson = {pearson:.4f} [goal >= 0.20] {"PASS" if pearson >= 0.20 else "FAIL"}', |
| '', |
| ' Y nghia: cung mot ngon tay chup bang cac sensor khac nhau -> SIFQ cho', |
| ' score nhat quan. KS thap = phan bo giong nhau, Pearson cao = ranking on dinh.', |
| ' NFIQ2 thuong co KS > 0.5 cho cross-sensor pairs.', |
| '', |
| ' Track 4 -- Concept Grounding (rho < 0 = PASS):', |
| f' {"Degradation":<12} {"Concept":<28} {"rho":>7} Status', |
| f' {"------------":<12} {"----------------------------":<28} {"-------":>7} ------', |
| ] |
|
|
| pass_count = total = 0 |
| for row in t4: |
| deg = row['degradation'] |
| for concept in TARGETS.get(deg, []): |
| rho = row.get(concept) |
| if rho is None: |
| continue |
| total += 1 |
| ok = rho < 0 |
| if ok: |
| pass_count += 1 |
| status = 'PASS' if ok else 'FAIL <--' |
| lines.append(f' {deg:<12} {concept:<28} {rho:>+7.3f} {status}') |
|
|
| lines += [ |
| '', |
| f' Result: {pass_count}/{total} pairs PASS', |
| '', |
| ' 3 failures phan tich:', |
| '', |
| ' [1] noise -> noise_level (rho=+0.365):', |
| ' Regression tu v22 (-0.052, OK) -> v24 (+0.365, FAIL).', |
| ' gamma=2.0 + T39 (orient_coh added to dry_skin/wet_press) thay doi', |
| ' gradient landscape cua ScoreAggregator. Noise la degradation duy nhat', |
| ' target noise_level -> single-point pressure -> de bi invert.', |
| '', |
| ' [2] dry_skin -> contrast_u (rho=+0.051):', |
| ' Persistent qua cac phien ban (v22: +0.504). Dry_skin la degradation', |
| ' DUY NHAT target contrast_uniformity -> signal yeu, de bi gradient', |
| ' tu cac loss khac at di.', |
| '', |
| ' [3] dry_skin -> orient_coh (rho=+0.008):', |
| ' T39 moi them orient_coh vao dry_skin, nhung wet_press->orient_coh', |
| ' hoat dong tot (-0.448). Ly do: wet_press + blur deu co orientation', |
| ' disruption manh, nhung dry_skin tao ra orientation noise khong nhat', |
| ' quan -> khong du signal de orient_coh feature phan biet.', |
| '', |
| '', |
| '5. v25 -- T40 FIXES (dang chay)', |
| SEP2, |
| ' Thay doi so voi v24:', |
| '', |
| ' gamma: 2.0 -> 1.5', |
| '', |
| ' blur: [1, 2] -> [1, 2, 0] (them orient_coh[0])', |
| ' noise: [3] -> [3, 4] (them contrast_u[4])', |
| ' jpeg: [2, 1] -> [2, 1, 4] (them contrast_u[4])', |
| ' dry_skin / wet_press / occlusion: khong doi', |
| '', |
| ' Ket qua ky vong:', |
| ' - noise->noise_lv: +0.365 -> negative (gamma thap + noise 2-concept)', |
| ' - dry_skin->contrast_u: +0.051 -> negative (3 degs co-supervise contrast_u)', |
| ' - dry_skin->orient_coh: +0.008 -> negative (blur gives orient_coh strong signal)', |
| ' - Cac pairs da PASS: giu nguyen (blur/jpeg/occlusion/wet_press)', |
| ' - Track 2: giu PASS (KS, Pearson khong thay doi co ban)', |
| '', |
| SEP, |
| 'END', |
| SEP, |
| ] |
|
|
| txt = '\n'.join(lines) |
| with open('eval_results/sifq_report_v24.txt', 'w', encoding='utf-8') as f: |
| f.write(txt) |
| print(txt) |
| print() |
| print('>>> Saved: eval_results/sifq_report_v24.txt') |
|
|
