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- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """gpu 后端 A/B 等价测试：同一进程先后用 milvus / gpu 后端跑同一批图，
- 输出（predicted_card_id + Top-1 的 id/fusion/upper/lower）必须逐字段一致。"""
- import os
- import sys
- sys.stdout.reconfigure(encoding="utf-8")
- _ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
- sys.path.insert(0, _ROOT)
- sys.path.insert(0, os.path.join(_ROOT, "scripts"))
- import torch
- from scripts.cascade_match import DualCascadeMatcher
- IMGS = [
- "data/audit_imgs/multicard_2up.jpg",
- "data/audit_imgs/raspi_front_2026-09-01_13-22-48_529284.jpg",
- "data/audit_imgs/raspi_front_2026-09-01_11-24-01_965039.jpg",
- ]
- def run(backend):
- dev = torch.device("cuda:0")
- m = DualCascadeMatcher(backend=backend, device=dev, detector_device=dev,
- milvus_alias="ab_" + backend)
- out = {}
- for p in IMGS:
- path = p if os.path.isabs(p) else os.path.join(_ROOT, p)
- r = m.query_all(path)
- out[p] = [(x["predicted_card_id"],
- [(t["card_id"], t["fusion"], t["upper"], t["lower"]) for t in x["top_k"]])
- for x in r]
- print(f"[{backend}] {os.path.basename(p)}: {out[p]}", flush=True)
- del m
- torch.cuda.empty_cache()
- return out
- a = run("milvus")
- b = run("gpu")
- same = a == b
- print(f"\n[A/B] milvus vs gpu 输出全等: {same}", flush=True)
- if not same:
- for p in a:
- if a[p] != b.get(p):
- print(f" 差异 {p}:\n milvus={a[p]}\n gpu ={b.get(p)}", flush=True)
- sys.exit(0 if same else 1)
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