File size: 6,231 Bytes
6a5bb7e | 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 | """WBF Ensemble Evaluation
==========================
Weighted Box Fusion across multiple models for higher mAP50-95.
Combines predictions from different seed models using IoU-based clustering.
Models: v6_1 + v12_seed_42 + v12_seed_123
Weights: proportional to each model's mAP50-95
"""
import sys, os
import numpy as np
from pathlib import Path
from PIL import Image
from tqdm import tqdm
PROJECT_DIR = '/home/user/goat'
os.chdir(PROJECT_DIR)
sys.path.insert(0, PROJECT_DIR)
from ultralytics import YOLO
def compute_iou(b1, b2):
x1 = max(b1[0], b2[0]); y1 = max(b1[1], b2[1])
x2 = min(b1[2], b2[2]); y2 = min(b1[3], b2[3])
inter = max(0, x2 - x1) * max(0, y2 - y1)
a1 = (b1[2] - b1[0]) * (b1[3] - b1[1])
a2 = (b2[2] - b2[0]) * (b2[3] - b2[1])
return inter / (a1 + a2 - inter + 1e-8)
def wbf(boxes_list, scores_list, weights, iou_thr=0.55):
"""Weighted Box Fusion."""
if not boxes_list:
return np.array([]), np.array([])
all_boxes = []
all_scores = []
for m_idx, (boxes, scores) in enumerate(zip(boxes_list, scores_list)):
w = weights[m_idx]
for i in range(len(boxes)):
all_boxes.append(boxes[i])
all_scores.append(scores[i] * w)
if not all_boxes:
return np.array([]), np.array([])
all_boxes = np.array(all_boxes)
all_scores = np.array(all_scores)
order = np.argsort(-all_scores)
all_boxes = all_boxes[order]
all_scores = all_scores[order]
clusters = []
used = np.zeros(len(all_boxes), dtype=bool)
for i in range(len(all_boxes)):
if used[i]:
continue
cluster = [(all_boxes[i], all_scores[i])]
used[i] = True
for j in range(i + 1, len(all_boxes)):
if used[j]:
continue
total_w = sum(s for _, s in cluster)
center = np.zeros(4)
for b, s in cluster:
center += b * s / total_w
if compute_iou(center.tolist(), all_boxes[j].tolist()) > iou_thr:
cluster.append((all_boxes[j], all_scores[j]))
used[j] = True
clusters.append(cluster)
result_boxes = []
result_scores = []
for cluster in clusters:
total_w = sum(s for _, s in cluster)
avg_box = np.zeros(4)
for b, s in cluster:
avg_box += b * s / total_w
avg_score = total_w / len(weights)
result_boxes.append(avg_box)
result_scores.append(avg_score)
return np.array(result_boxes), np.array(result_scores)
def main():
# Models
model_paths = [
'runs/detect/Detection_experiments/v6_1_s_refined/weights/best.pt',
'runs/detect/Detection_experiments/v12_seed_42/weights/best.pt',
'runs/detect/Detection_experiments/v12_seed_123/weights/best.pt',
]
weights = [0.5125, 0.5097, 0.5113] # mAP50-95 of each model
weights = [w / sum(weights) for w in weights]
models = []
for i, mp in enumerate(model_paths):
if os.path.exists(mp):
models.append((YOLO(mp), weights[i]))
print(f'Loaded: {mp} (weight={weights[i]:.3f})')
else:
print(f'MISSING: {mp}')
if len(models) < 2:
print('Need at least 2 models')
return
val_img_dir = 'Data/Detection_dataset/images/val'
val_label_dir = 'Data/Detection_dataset/labels/val'
val_files = sorted([f for f in os.listdir(val_img_dir) if f.endswith('.jpg')])
# Evaluate
iou_thresholds = [round(0.5 + i * 0.05, 2) for i in range(10)]
per_iou_tp = {t: 0 for t in iou_thresholds}
total_gt = 0
for img_file in tqdm(val_files, desc='Evaluating'):
img_path = os.path.join(val_img_dir, img_file)
img = Image.open(img_path)
iw, ih = img.size
# GT
lf = img_file.replace('.jpg', '.txt')
lpath = os.path.join(val_label_dir, lf)
gt_boxes = []
if os.path.exists(lpath):
with open(lpath) as f:
for line in f:
p = line.strip().split()
if len(p) >= 5:
cx, cy, w, h = [float(x) for x in p[1:5]]
gt_boxes.append([
(cx - w/2) * iw, (cy - h/2) * ih,
(cx + w/2) * iw, (cy + h/2) * ih
])
total_gt += len(gt_boxes)
if not gt_boxes:
continue
# Get predictions from all models
boxes_list = []
scores_list = []
for model, _ in models:
results = model.predict(
source=img_path, imgsz=1536, conf=0.25, iou=0.7,
max_det=100, save=False, verbose=False
)
if results and len(results[0].boxes):
boxes_list.append(results[0].boxes.xyxy.cpu().numpy())
scores_list.append(results[0].boxes.conf.cpu().numpy())
else:
boxes_list.append(np.array([]))
scores_list.append(np.array([]))
# WBF
fused_boxes, fused_scores = wbf(boxes_list, scores_list,
[w for _, w in models])
# Evaluate
for t in iou_thresholds:
matched = set()
for pb in fused_boxes:
best_iou, best_gi = 0, -1
for gi, gb in enumerate(gt_boxes):
if gi in matched:
continue
iou = compute_iou(pb.tolist(), gb)
if iou > best_iou:
best_iou = iou
best_gi = gi
if best_iou >= t and best_gi >= 0:
per_iou_tp[t] += 1
matched.add(best_gi)
# Results
print(f'\n{"="*60}')
print(f'WBF Ensemble Results (3 models)')
print(f'{"="*60}')
recalls = []
for t in iou_thresholds:
r = per_iou_tp[t] / total_gt if total_gt else 0
recalls.append(r)
print(f' IoU@{t:.2f}: Recall={r:.4f}')
map5095 = np.mean(recalls)
print(f'\n Approx mAP50-95: {map5095:.4f}')
print(f' vs v6_1 single (0.5125): {map5095-0.5125:+.4f}')
if __name__ == '__main__':
main()
|