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1cf3825 | 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 | """Run the same v8 inference pipeline the deployed dashboard uses against
every sample under samples/ood/, then print a verdict table.
Pipeline mirrors dashboard.py:
- load model/best_micro.onnx (ConvNeXt-Tiny U-Net, 384 px, Tversky)
- resize -> 384, ImageNet normalise, batched 4-way flip TTA in one ORT call
- per-pixel mean probability, threshold 0.20 -> binary mask
- report tumor area (px), max prob, classifier verdict, image source
"""
from __future__ import annotations
import csv
import os
import sys
import time
from pathlib import Path
import numpy as np
import onnxruntime as ort
from PIL import Image
ROOT = Path(__file__).resolve().parent.parent
ONNX = ROOT / 'model' / 'best_micro.onnx'
SAMPLES_DIR = ROOT / 'samples' / 'ood'
SIZE = 384
THRESH = 0.20
MIN_TUMOR_AREA = 50 # match dashboard's 50-pixel minimum
IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
def load_v8() -> ort.InferenceSession:
providers = ['CPUExecutionProvider']
sess = ort.InferenceSession(str(ONNX), providers=providers)
print(f'[init] ONNX session: {ONNX.name} '
f'(input={sess.get_inputs()[0].name}, output={sess.get_outputs()[0].name})')
return sess
def preprocess(img: Image.Image) -> np.ndarray:
"""PIL -> (3, SIZE, SIZE) float32 normalised."""
img = img.convert('RGB').resize((SIZE, SIZE), Image.BILINEAR)
arr = np.asarray(img, dtype=np.float32) / 255.0
arr = (arr - IMAGENET_MEAN) / IMAGENET_STD
return arr.transpose(2, 0, 1) # CHW
def tta_predict(sess: ort.InferenceSession, chw: np.ndarray) -> np.ndarray:
"""Batched 4-way TTA: id, hflip, vflip, hvflip — single ORT call.
Returns mean tumor probability map at SIZE x SIZE.
"""
base = chw
h = base[:, :, ::-1].copy()
v = base[:, ::-1, :].copy()
hv = base[:, ::-1, ::-1].copy()
batch = np.stack([base, h, v, hv], axis=0) # (4, 3, SIZE, SIZE)
in_name = sess.get_inputs()[0].name
logits = sess.run(None, {in_name: batch})[0] # (4, 1, SIZE, SIZE)
if logits.shape[1] > 1: # 2-channel models -> take fg
logits = logits[:, 1:2]
prob = 1.0 / (1.0 + np.exp(-logits)) # sigmoid
# Undo flips before averaging.
prob[1] = prob[1, :, :, ::-1]
prob[2] = prob[2, :, ::-1, :]
prob[3] = prob[3, :, ::-1, ::-1]
return prob.mean(axis=0)[0] # (SIZE, SIZE)
def main():
if not ONNX.exists():
print(f'ERROR: {ONNX} missing — download via dashboard or upload script.')
sys.exit(2)
sess = load_v8()
rows: list[dict] = []
samples = sorted(p for p in SAMPLES_DIR.rglob('*.png'))
if not samples:
print(f'ERROR: no PNGs under {SAMPLES_DIR}')
sys.exit(2)
print(f'\n[eval] {len(samples)} OOD samples\n')
t0 = time.perf_counter()
for p in samples:
try:
img = Image.open(p)
chw = preprocess(img)
prob = tta_predict(sess, chw)
area = int((prob >= THRESH).sum())
verdict = 'TUMOR' if area >= MIN_TUMOR_AREA else 'no_tumor'
source = p.parent.name
row = {
'source': source,
'file': p.name,
'prob_max': float(prob.max()),
'prob_mean_fg': float(prob[prob >= THRESH].mean()) if area else 0.0,
'tumor_area_px': area,
'verdict': verdict,
}
rows.append(row)
except Exception as exc:
print(f' [fail] {p.name}: {type(exc).__name__}: {exc}')
elapsed = time.perf_counter() - t0
# Per-source summary
print(f'\n=== per-image verdicts (threshold={THRESH}) ===')
hdr = f'{"source":36s} {"file":48s} {"pmax":>5s} {"area":>6s} verdict'
print(hdr)
print('-' * len(hdr))
for r in rows:
print(f'{r["source"][:36]:36s} {r["file"][:48]:48s} '
f'{r["prob_max"]:.3f} {r["tumor_area_px"]:6d} {r["verdict"]}')
# Aggregate per source
print('\n=== per-source summary ===')
by_src: dict[str, list[dict]] = {}
for r in rows:
by_src.setdefault(r['source'], []).append(r)
for src in sorted(by_src):
rs = by_src[src]
n_tum = sum(1 for r in rs if r['verdict'] == 'TUMOR')
avg_pmax = np.mean([r['prob_max'] for r in rs])
print(f' {src:46s} n={len(rs):3d} tumor_called={n_tum:3d} '
f'mean(pmax)={avg_pmax:.3f}')
print(f'\n[done] {len(rows)} samples in {elapsed:.1f}s '
f'({elapsed/max(1,len(rows)):.2f} s/sample)')
# Persist for inspection.
out_csv = SAMPLES_DIR / 'eval_results.csv'
with out_csv.open('w', newline='', encoding='utf-8') as f:
w = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
w.writeheader()
w.writerows(rows)
print(f'[csv] wrote {out_csv}')
if __name__ == '__main__':
main()
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