| """ |
| Prove the patched graph is numerically identical to the original. |
| |
| This is the ONLY thing standing between a subtly wrong graph and production: the |
| patched ONNX is our own artifact, no upstream validates it, and a Split tree that |
| reorders or mis-sizes a group would still produce a plausible-looking matte. |
| A quality metric cannot catch that. Bit-level agreement can. |
| |
| `patch_split.py` and `patch_deform.py` perform structural rewrites with no numerical content, so the |
| correct expectation is EXACT equality, not "close enough". The 0.9999 gate exists |
| only to absorb non-determinism in CPU kernel scheduling. |
| |
| python verify_patch.py orig.onnx patched.onnx img1.jpg img2.jpg ... |
| """ |
|
|
| import argparse |
| import sys |
|
|
| import numpy as np |
| import onnxruntime as ort |
| from PIL import Image |
|
|
| SIZE = 1024 |
| MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32) |
| STD = np.array([0.229, 0.224, 0.225], dtype=np.float32) |
|
|
|
|
| def preprocess(path): |
| |
| |
| img = Image.open(path).convert("RGB").resize((SIZE, SIZE), Image.BILINEAR) |
| x = np.asarray(img, dtype=np.float32) / 255.0 |
| x = (x - MEAN) / STD |
| return x.transpose(2, 0, 1)[None].astype(np.float32) |
|
|
|
|
| def main() -> int: |
| ap = argparse.ArgumentParser() |
| ap.add_argument("orig") |
| ap.add_argument("patched") |
| ap.add_argument("images", nargs="+") |
| ap.add_argument("--min-correlation", type=float, default=0.9999) |
| args = ap.parse_args() |
|
|
| opts = ort.SessionOptions() |
| opts.graph_optimization_level = ort.GraphOptimizationLevel.ORT_DISABLE_ALL |
| a = ort.InferenceSession(args.orig, opts, providers=["CPUExecutionProvider"]) |
| b = ort.InferenceSession(args.patched, opts, providers=["CPUExecutionProvider"]) |
|
|
| name_a = a.get_inputs()[0].name |
| name_b = b.get_inputs()[0].name |
| print(f"inputs: {name_a} / {name_b}") |
|
|
| worst_corr, worst_absdiff, failed = 1.0, 0.0, False |
| for path in args.images: |
| x = preprocess(path) |
| oa = np.asarray(a.run(None, {name_a: x})[0], dtype=np.float64).ravel() |
| ob = np.asarray(b.run(None, {name_b: x})[0], dtype=np.float64).ravel() |
|
|
| if oa.shape != ob.shape: |
| print(f"FAIL {path}: shape {oa.shape} vs {ob.shape}") |
| failed = True |
| continue |
|
|
| absdiff = float(np.max(np.abs(oa - ob))) |
| corr = 1.0 if absdiff == 0.0 else float(np.corrcoef(oa, ob)[0, 1]) |
| worst_corr = min(worst_corr, corr) |
| worst_absdiff = max(worst_absdiff, absdiff) |
| exact = "EXACT" if absdiff == 0.0 else f"max|diff| {absdiff:.3e}" |
| print(f" {path.split('/')[-1]:50s} corr {corr:.8f} {exact}") |
|
|
| print(f"\nworst correlation {worst_corr:.8f}, worst max|diff| {worst_absdiff:.3e}") |
| if failed or worst_corr < args.min_correlation: |
| print("REJECTED — do not ship this graph.") |
| return 1 |
| if worst_absdiff == 0.0: |
| print("ACCEPTED — bit-identical, as a structural rewrite should be.") |
| else: |
| print("ACCEPTED — within tolerance, but NOT bit-identical; investigate before shipping.") |
| return 0 |
|
|
|
|
| if __name__ == "__main__": |
| sys.exit(main()) |
|
|
