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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 | """Kitchen Sink TTA WBF — Maximum inference-time firepower
===========================================================
Every model × scale × augmentation → WBF fusion.
Augmentations:
- 3 scales: 1280, 1536, 1920
- 2 flips: none, horizontal
- 2 brightness: normal, +20%
Per model: 3×2×2 = 12 variants
With 5 models: 60 prediction sources → WBF
Also tests subset combinations to find the best cost/accuracy tradeoff.
"""
import sys, os, json
import numpy as np
from PIL import Image, ImageEnhance
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, y1 = max(b1[0], b2[0]), max(b1[1], b2[1])
x2, y2 = min(b1[2], b2[2]), 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, iou_thr=0.55):
if not boxes_list or all(len(b) == 0 for b in boxes_list):
return np.array([]), np.array([])
all_boxes, all_scores = [], []
for boxes, scores in zip(boxes_list, scores_list):
all_boxes.extend(boxes)
all_scores.extend(scores)
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_scores = all_boxes[order], 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
tw = sum(s for _, s in cluster)
center = sum(b*s/tw for b, s in cluster)
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 cl in clusters:
tw = sum(s for _, s in cl)
avg_b = sum(b*s/tw for b, s in cl)
result_boxes.append(avg_b)
result_scores.append(tw)
return np.array(result_boxes), np.array(result_scores)
def predict_augmented(model, img, imgsz, flip=False, brighten=1.0):
"""Predict on (possibly augmented) image."""
img_aug = img
if brighten != 1.0:
enhancer = ImageEnhance.Brightness(img)
img_aug = enhancer.enhance(brighten)
if flip:
img_aug = img_aug.transpose(Image.FLIP_LEFT_RIGHT)
r = model.predict(img_aug, imgsz=imgsz, conf=0.25, iou=0.7, max_det=100, verbose=False)
if not r or len(r[0].boxes) == 0:
return np.array([]), np.array([])
boxes = r[0].boxes.xyxy.cpu().numpy()
scores = r[0].boxes.conf.cpu().numpy()
if flip:
w = img.size[0]
boxes[:, [0, 2]] = w - boxes[:, [2, 0]]
return boxes, scores
def evaluate(name, get_boxes_fn, val_files, val_img_dir, val_label_dir):
iou_thrs = [round(0.5+i*0.05, 2) for i in range(10)]
tp = {t:0 for t in iou_thrs}
total_gt = 0
for img_file in tqdm(val_files, desc=name):
img = Image.open(os.path.join(val_img_dir, img_file))
gt_boxes = []
lf = img_file.replace('.jpg','.txt')
with open(os.path.join(val_label_dir, lf)) 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)*img.size[0], (cy-h/2)*img.size[1], (cx+w/2)*img.size[0], (cy+h/2)*img.size[1]])
total_gt += len(gt_boxes)
if not gt_boxes: continue
preds = get_boxes_fn(img)
for t in iou_thrs:
matched = set()
for pb in preds:
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:
tp[t] += 1; matched.add(best_gi)
recalls = [tp[t]/total_gt for t in iou_thrs]
return np.mean(recalls), recalls[5]
def main():
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')])
# Discover models
exp_dir = 'runs/detect/Detection_experiments'
models = []
priority = ['v6_1_s_refined', 'v12_seed_42', 'v12_seed_123', 'v14_seed_789', 'v14_seed_999']
for name in priority:
path = os.path.join(exp_dir, name, 'weights', 'best.pt')
if os.path.exists(path):
models.append((YOLO(path), name))
print(f'Loaded: {name}')
if not models:
print('No models found!')
return
print(f'\n{len(models)} models ready')
scales = [1280, 1536, 1920]
flips = [False, True]
brights = [1.0, 1.2]
# Baseline
def baseline(img):
m, _ = models[0]
boxes, scores = predict_augmented(m, img, 1536)
return boxes
mAP_base, r75_base = evaluate('Single baseline', baseline, val_files, val_img_dir, val_label_dir)
# Config 1: Single model Kitchen Sink
def single_ks(img):
m, _ = models[0]
all_boxes, all_scores = [], []
for sz in scales:
for fl in flips:
for br in brights:
b, s = predict_augmented(m, img, sz, fl, br)
if len(b) > 0:
all_boxes.append(b); all_scores.append(s)
return wbf(all_boxes, all_scores)[0]
mAP_sks, r75_sks = evaluate(f'1m Kitchen Sink ({len(scales)*len(flips)*len(brights)}x)', single_ks, val_files, val_img_dir, val_label_dir)
# Config 2: All models Kitchen Sink
def all_ks(img):
all_boxes, all_scores = [], []
for m, _ in models:
for sz in scales:
for fl in flips:
for br in brights:
b, s = predict_augmented(m, img, sz, fl, br)
if len(b) > 0:
all_boxes.append(b); all_scores.append(s)
return wbf(all_boxes, all_scores)[0]
n_sources = len(models) * len(scales) * len(flips) * len(brights)
mAP_aks, r75_aks = evaluate(f'ALL Kitchen Sink ({n_sources}x)', all_ks, val_files, val_img_dir, val_label_dir)
print(f'\n{"="*60}')
print(f'KITCHEN SINK RESULTS')
print(f'{"="*60}')
print(f'Baseline (1m, 1536): {mAP_base:.4f} IoU@75={r75_base:.4f}')
print(f'1m Kitchen Sink (12x): {mAP_sks:.4f} IoU@75={r75_sks:.4f} (+{mAP_sks-mAP_base:+.4f})')
print(f'ALL Kitchen Sink ({n_sources}x): {mAP_aks:.4f} IoU@75={r75_aks:.4f} (+{mAP_aks-mAP_base:+.4f})')
# Save
with open('logs/kitchen_sink_results.json', 'w') as f:
json.dump({
'baseline': round(mAP_base, 4),
'single_kitchen_sink': round(mAP_sks, 4),
'all_kitchen_sink': round(mAP_aks, 4),
'n_models': len(models),
'n_sources': n_sources,
}, f, indent=2)
if __name__ == '__main__':
main()
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