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f211247 | 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 | #!/usr/bin/env python3
"""DINOv3 tabanlı uncertainty score.
Yaklaşım: DINOv3 backbone'u ile feature çıkar, her unified_label'ın centroid'ini hesapla,
her kayıt için (1 - max_similarity_to_own_centroid) → uncertainty.
Hard cases (düşük similarity) = yüksek uncertainty = tekrar annotate önerilir.
"""
import json, os, argparse, time
from pathlib import Path
import numpy as np
ROOT = Path("/arf/scratch/stakan/hitit-proje")
SOURCES = ROOT / "datasets" / "sources"
def main():
import torch
from PIL import Image
from collections import defaultdict
ap = argparse.ArgumentParser()
ap.add_argument('--batch', type=int, default=64)
ap.add_argument('--model', default='dinov2_vitb14') # DINOv3 eğer torch hub'da varsa dinov3_vitb16
ap.add_argument('--sample', type=int, default=0)
args = ap.parse_args()
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Device: {device}", flush=True)
# DINOv3 (Meta) — Py3.11 + ai-tools-kolyoz-1.0 env'i ile çalışır.
# Fallbacks: DINOv2 → timm DINOv2 (en kötü senaryo).
try:
model = torch.hub.load('facebookresearch/dinov3', 'dinov3_vitb16',
trust_repo=True).eval().to(device)
emb_dim = 768
input_size = 224
model_name = "dinov3_vitb16"
except Exception as e:
print(f"DINOv3 yüklenemedi ({type(e).__name__}: {str(e)[:80]}), DINOv2'ye düşüldü", flush=True)
try:
model = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14',
trust_repo=True).eval().to(device)
emb_dim = 768
input_size = 224
model_name = "dinov2_vitb14"
except Exception as e2:
print(f"DINOv2 de yüklenemedi ({e2}), timm DINOv2'ye düşüldü", flush=True)
import timm
model = timm.create_model('vit_base_patch14_dinov2.lvd142m',
pretrained=True, num_classes=0).eval().to(device)
emb_dim = 768
input_size = 518
model_name = "dinov2_vitb14_timm"
from torchvision import transforms
tf = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]),
])
# Topla: sadece classification
items = []
for d in sorted(SOURCES.iterdir()):
if not d.is_dir(): continue
mp = d / "manifest_classification.jsonl"
if not mp.exists(): continue
with open(mp) as f:
for line in f:
r = json.loads(line)
p = r.get('path')
ul = r.get('unified_label')
if p and ul and r.get('integrity_ok') is True and os.path.exists(p):
items.append((r['id'], p, ul))
if args.sample:
import random; random.seed(42)
items = random.sample(items, min(args.sample, len(items)))
print(f"Feature çıkarılacak: {len(items):,}", flush=True)
# Feature extraction
features = np.zeros((len(items), emb_dim), dtype=np.float32)
labels_list = [None] * len(items)
ids_list = [None] * len(items)
t0 = time.time()
idx = 0
for bi in range(0, len(items), args.batch):
batch = items[bi:bi+args.batch]
imgs = []
for rid, path, ul in batch:
try:
img = tf(Image.open(path).convert('RGB'))
imgs.append(img)
except:
imgs.append(None)
valid = [(i,img) for i,img in enumerate(imgs) if img is not None]
if not valid: continue
x = torch.stack([v[1] for v in valid]).to(device)
with torch.no_grad(), torch.amp.autocast('cuda', enabled=(device=="cuda")):
feats = model(x)
if isinstance(feats, dict): feats = feats.get('x_norm_clstoken', feats.get('cls', list(feats.values())[0]))
feats = feats.cpu().numpy().astype(np.float32)
feats /= (np.linalg.norm(feats, axis=1, keepdims=True) + 1e-8)
for k, (i, _) in enumerate(valid):
rid, path, ul = batch[i]
features[idx] = feats[k]
labels_list[idx] = ul
ids_list[idx] = rid
idx += 1
if (bi // args.batch) % 50 == 0:
print(f" {idx}/{len(items)} ({100*idx/len(items):.1f}%) {idx/max(time.time()-t0,1):.0f} img/s", flush=True)
features = features[:idx]
labels_list = labels_list[:idx]
ids_list = ids_list[:idx]
# Per-class centroid
from collections import defaultdict
sums = defaultdict(lambda: np.zeros(emb_dim, dtype=np.float32))
counts = defaultdict(int)
for f, l in zip(features, labels_list):
sums[l] += f
counts[l] += 1
centroids = {l: sums[l] / counts[l] for l in sums}
# Normalize
centroids = {l: c / (np.linalg.norm(c) + 1e-8) for l, c in centroids.items()}
# Uncertainty = 1 - cos_sim(feature, own_centroid)
uncertainty = {}
for f, l, rid in zip(features, labels_list, ids_list):
c = centroids.get(l)
if c is None: continue
sim = float(f @ c)
uncertainty[rid] = max(0.0, min(1.0, 1.0 - sim))
# Manifest güncelle
updated = 0
for d in sorted(SOURCES.iterdir()):
if not d.is_dir(): continue
for mf in ['manifest.jsonl', 'manifest_classification.jsonl', 'manifest_detection.jsonl']:
mp = d / mf
if not mp.exists(): continue
records = []
with open(mp) as f:
for line in f:
r = json.loads(line)
u = uncertainty.get(r.get('id'))
if u is not None:
r['uncertainty_score'] = round(u, 4)
r['uncertainty_source'] = f'{model_name}_centroid_v1'
updated += 1
records.append(r)
with open(mp, 'w') as f:
for r in records:
f.write(json.dumps(r, ensure_ascii=False) + '\n')
print(f"\nuncertainty_score güncellendi: {updated:,} kayıt")
# Summary
vals = list(uncertainty.values())
vals_sorted = sorted(vals)
n = len(vals_sorted)
out = {
"model": model_name,
"n_scored": n,
"percentiles": {f"p{p}": round(vals_sorted[int(n*p/100)], 4) for p in [5,25,50,75,95]} if n else {},
"n_classes_with_centroid": len(centroids),
"high_uncertainty_threshold": 0.5,
"n_above_threshold": sum(1 for v in vals if v > 0.5),
}
with open(ROOT / "datasets" / "processed" / "uncertainty_dinov3_report.json", 'w') as f:
json.dump(out, f, indent=2, ensure_ascii=False)
print(f"\n{json.dumps(out, indent=2)}")
if __name__ == '__main__':
main()
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