src/latency_test.py

#!/usr/bin/env python
"""512x512 fp16 时延测速: 加载 torch.jit 模型, warmup + N 次前向。
用法: python src/latency_test.py --model model_dir/your_model.pt [--osediff_latency 0.168]
"""
import argparse, time, statistics
import torch

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--model", required=True)
    ap.add_argument("--n", type=int, default=100)
    ap.add_argument("--warmup", type=int, default=10)
    ap.add_argument("--osediff_latency", type=float, default=0.0, help="秒; 提供则打印加速比")
    args = ap.parse_args()
    device = "cuda" if torch.cuda.is_available() else "cpu"
    m = torch.jit.load(args.model, map_location=device)
    m.eval()
    x = torch.randn(1, 3, 512, 512, device=device).half()
    with torch.no_grad():
        for _ in range(args.warmup):
            m(x)
        torch.cuda.synchronize() if device == "cuda" else None
        times = []
        for _ in range(args.n):
            if device == "cuda":
                torch.cuda.synchronize()
            t0 = time.perf_counter()
            with torch.no_grad():
                m(x)
            if device == "cuda":
                torch.cuda.synchronize()
            times.append(time.perf_counter() - t0)
    mean = statistics.mean(times)
    med = statistics.median(times)
    print(f"mean {mean*1000:.3f} ms | median {med*1000:.3f} ms | n={args.n}")
    if args.osediff_latency > 0:
        print(f"speedup vs OSEDiff({args.osediff_latency*1000:.1f}ms): {args.osediff_latency/mean:.2f}x")

if __name__ == "__main__":
    main()