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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | #!/usr/bin/env python3
"""5-fold WBF ensemble evaluation.
Strategy: each fold model evaluated on ITS OWN val set (out-of-fold prediction
is what CV gives us). For ensemble: each test image is predicted by ALL fold
models, predictions fused via WBF, mAP computed against ground truth.
For our 5-fold CV, we use the union of all val sets and predict each image
with all 5 models then WBF.
"""
import os, sys, json, time
from pathlib import Path
import numpy as np
import torch
ROOT = Path('/arf/scratch/stakan/hitit-proje')
FOLD_ROOT = ROOT / 'datasets/ready/detection_tablets'
WEIGHTS_ROOT = ROOT / 'runs/detect/hitit_ocr/runs/h100'
def log(m): print(f"[{time.strftime('%H:%M:%S')}] {m}", flush=True)
def main():
from ultralytics import YOLO
from ensemble_boxes import weighted_boxes_fusion
from PIL import Image
# Tüm val setlerini birleştir (full dataset, leakage olmadan: her image kendi fold'unun val'ında)
all_imgs = []
img_to_fold = {}
for fold in range(5):
for line in (FOLD_ROOT / f'fold_{fold}/val.txt').read_text().splitlines():
line = line.strip()
if not line: continue
all_imgs.append(line)
img_to_fold[line] = fold
log(f"Total eval images: {len(all_imgs)}")
# Modelleri yükle
models = {}
for fold in range(5):
ckpt = WEIGHTS_ROOT / f'yolo_fold{fold}/weights/best.pt'
if not ckpt.exists():
log(f"WARN: {ckpt} eksik")
continue
models[fold] = YOLO(str(ckpt))
log(f"Loaded fold {fold}: {ckpt}")
# Her image için predict (own-fold model ve ensemble karşılaştırması)
own_preds = {} # img -> (boxes, scores)
ensemble_preds = {} # img -> (boxes, scores)
iou_thr = 0.55
skip_box_thr = 0.001
log("Predicting...")
for idx, img_path in enumerate(all_imgs):
if idx % 100 == 0: log(f" {idx}/{len(all_imgs)}")
own_fold = img_to_fold[img_path]
# Image size for normalization
try:
with Image.open(img_path) as im:
W, H = im.size
except Exception:
continue
boxes_list, scores_list, labels_list = [], [], []
for fold, model in models.items():
try:
r = model.predict(img_path, conf=0.001, iou=0.7, max_det=2000,
imgsz=1280, verbose=False, device=0 if torch.cuda.is_available() else 'cpu')[0]
except Exception as e:
log(f" predict skip {Path(img_path).name}: {e}"); break
if r.boxes is None or len(r.boxes) == 0: continue
xyxy = r.boxes.xyxy.cpu().numpy() / np.array([W, H, W, H]) # normalize 0-1
xyxy = np.clip(xyxy, 0, 1)
scores = r.boxes.conf.cpu().numpy().tolist()
labels = [0] * len(scores)
boxes_list.append(xyxy.tolist())
scores_list.append(scores)
labels_list.append(labels)
if fold == own_fold:
own_preds[img_path] = (xyxy, np.array(scores))
if not boxes_list:
ensemble_preds[img_path] = (np.zeros((0, 4)), np.zeros(0))
continue
try:
wbf_boxes, wbf_scores, wbf_labels = weighted_boxes_fusion(
boxes_list, scores_list, labels_list,
weights=None, iou_thr=iou_thr, skip_box_thr=skip_box_thr)
except Exception as e:
log(f" WBF skip {Path(img_path).name}: {e}")
ensemble_preds[img_path] = (np.zeros((0, 4)), np.zeros(0))
continue
if len(wbf_boxes) == 0:
ensemble_preds[img_path] = (np.zeros((0, 4)), np.zeros(0))
continue
wbf_boxes_px = np.asarray(wbf_boxes) * np.array([W, H, W, H])
ensemble_preds[img_path] = (wbf_boxes_px, np.asarray(wbf_scores))
# GT loader (YOLO normalized format)
LBL_DIR = ROOT / 'hitit_ocr/data/detection/labels/all'
def load_gt(img_path):
stem = Path(img_path).stem
lbl = LBL_DIR / f'{stem}.txt'
if not lbl.exists(): return np.zeros((0, 4))
boxes = []
with Image.open(img_path) as im: W, H = im.size
for ln in lbl.read_text().splitlines():
p = ln.split()
if len(p) < 5: continue
cx, cy, w, h = float(p[1]), float(p[2]), float(p[3]), float(p[4])
x1 = (cx - w/2) * W; y1 = (cy - h/2) * H
x2 = (cx + w/2) * W; y2 = (cy + h/2) * H
boxes.append([x1, y1, x2, y2])
return np.array(boxes) if boxes else np.zeros((0, 4))
def iou_matrix(a, b):
if len(a) == 0 or len(b) == 0: return np.zeros((len(a), len(b)))
ax1, ay1, ax2, ay2 = a[:,0:1], a[:,1:2], a[:,2:3], a[:,3:4]
bx1, by1, bx2, by2 = b[:,0], b[:,1], b[:,2], b[:,3]
inter_x1 = np.maximum(ax1, bx1); inter_y1 = np.maximum(ay1, by1)
inter_x2 = np.minimum(ax2, bx2); inter_y2 = np.minimum(ay2, by2)
iw = np.clip(inter_x2-inter_x1, 0, None); ih = np.clip(inter_y2-inter_y1, 0, None)
inter = iw * ih
a_area = (ax2-ax1) * (ay2-ay1); b_area = (bx2-bx1) * (by2-by1)
union = a_area + b_area - inter
return inter / np.clip(union, 1e-9, None)
def compute_map(pred_dict, iou_threshs=None):
if iou_threshs is None:
iou_threshs = np.arange(0.5, 1.0, 0.05)
# Per-class (single class). Collect TP/FP at each conf.
per_iou_aps = []
for iou_thr in iou_threshs:
scores_all = []; tp_all = []; n_gt = 0
for img_path, (boxes, scores) in pred_dict.items():
gt = load_gt(img_path); n_gt += len(gt)
if len(boxes) == 0: continue
ious = iou_matrix(boxes, gt) # (P, G)
# sort preds by score desc
order = np.argsort(-scores)
used_gt = np.zeros(len(gt), dtype=bool)
for i in order:
scores_all.append(scores[i])
if len(gt) == 0:
tp_all.append(0); continue
j = np.argmax(ious[i])
if ious[i, j] >= iou_thr and not used_gt[j]:
tp_all.append(1); used_gt[j] = True
else:
tp_all.append(0)
if not scores_all or n_gt == 0:
per_iou_aps.append(0.0); continue
scores_all = np.array(scores_all); tp_all = np.array(tp_all)
order = np.argsort(-scores_all)
tp_sorted = tp_all[order]
cum_tp = np.cumsum(tp_sorted); cum_fp = np.cumsum(1 - tp_sorted)
recall = cum_tp / n_gt
precision = cum_tp / np.clip(cum_tp + cum_fp, 1, None)
# 101-point AP
ap = 0.0
for r in np.linspace(0, 1, 101):
p = precision[recall >= r].max() if (recall >= r).any() else 0.0
ap += p
ap /= 101
per_iou_aps.append(ap)
return per_iou_aps
log("Computing mAP for own-fold predictions (baseline)...")
own_aps = compute_map(own_preds)
log(f" own mAP50={own_aps[0]:.4f} mAP50-95={np.mean(own_aps):.4f}")
log("Computing mAP for WBF ensemble...")
ens_aps = compute_map(ensemble_preds)
log(f" WBF mAP50={ens_aps[0]:.4f} mAP50-95={np.mean(ens_aps):.4f}")
out = {
'n_eval_images': len(all_imgs),
'n_own_pred_images': len(own_preds),
'n_ensemble_pred_images': len(ensemble_preds),
'own_fold': {'mAP50': float(own_aps[0]), 'mAP50_95': float(np.mean(own_aps)),
'per_iou': [float(x) for x in own_aps]},
'wbf_ensemble': {'mAP50': float(ens_aps[0]), 'mAP50_95': float(np.mean(ens_aps)),
'per_iou': [float(x) for x in ens_aps]},
}
out_path = WEIGHTS_ROOT.parent.parent.parent / 'wbf_ensemble_eval.json'
out_path.write_text(json.dumps(out, indent=2))
log(f"Wrote {out_path}")
if __name__ == '__main__':
main()
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