File size: 8,613 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | 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')
|