benchmark.py

# Benchmark script for LightGlue on real images
import argparse
import time
from collections import defaultdict
from pathlib import Path

import matplotlib.pyplot as plt
import numpy as np
import torch
import torch._dynamo

from lightglue import LightGlue, SuperPoint
from lightglue.utils import load_image

torch.set_grad_enabled(False)


def measure(matcher, data, device="cuda", r=100):
    timings = np.zeros((r, 1))
    if device.type == "cuda":
        starter = torch.cuda.Event(enable_timing=True)
        ender = torch.cuda.Event(enable_timing=True)
    # warmup
    for _ in range(10):
        _ = matcher(data)
    # measurements
    with torch.no_grad():
        for rep in range(r):
            if device.type == "cuda":
                starter.record()
                _ = matcher(data)
                ender.record()
                # sync gpu
                torch.cuda.synchronize()
                curr_time = starter.elapsed_time(ender)
            else:
                start = time.perf_counter()
                _ = matcher(data)
                curr_time = (time.perf_counter() - start) * 1e3
            timings[rep] = curr_time
    mean_syn = np.sum(timings) / r
    std_syn = np.std(timings)
    return {"mean": mean_syn, "std": std_syn}


def print_as_table(d, title, cnames):
    print()
    header = f"{title:30} " + " ".join([f"{x:>7}" for x in cnames])
    print(header)
    print("-" * len(header))
    for k, l in d.items():
        print(f"{k:30}", " ".join([f"{x:>7.1f}" for x in l]))


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Benchmark script for LightGlue")
    parser.add_argument(
        "--device",
        choices=["auto", "cuda", "cpu", "mps"],
        default="auto",
        help="device to benchmark on",
    )
    parser.add_argument("--compile", action="store_true", help="Compile LightGlue runs")
    parser.add_argument(
        "--no_flash", action="store_true", help="disable FlashAttention"
    )
    parser.add_argument(
        "--no_prune_thresholds",
        action="store_true",
        help="disable pruning thresholds (i.e. always do pruning)",
    )
    parser.add_argument(
        "--add_superglue",
        action="store_true",
        help="add SuperGlue to the benchmark (requires hloc)",
    )
    parser.add_argument(
        "--measure", default="time", choices=["time", "log-time", "throughput"]
    )
    parser.add_argument(
        "--repeat", "--r", type=int, default=100, help="repetitions of measurements"
    )
    parser.add_argument(
        "--num_keypoints",
        nargs="+",
        type=int,
        default=[128,256, 512, 1024,2048],
        help="number of keypoints (list separated by spaces)",
    )
    parser.add_argument(
        "--matmul_precision", default="highest", choices=["highest", "high", "medium"]
    )
    parser.add_argument(
        "--save", default=None, type=str, help="path where figure should be saved"
    )
    args = parser.parse_intermixed_args()

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    if args.device != "auto":
        device = torch.device(args.device)

    print("Running benchmark on device:", device)

    images = Path("assets")
    inputs = {
        "easy": (
            load_image(images / "/home/zhaoyibin/3DRE/sfm-learn/SFM_OWN/DATA/dtu/scan10/images/00000021.jpg"),
            load_image(images / "/home/zhaoyibin/3DRE/sfm-learn/SFM_OWN/DATA/dtu/scan10/images/00000022.jpg"),
        ),
        # "difficult": (
        #     load_image(images / "/home/zhaoyibin/3DRE/sfm-learn/SFM_OWN/DATA/dtu/scan10/images/00000021.jpg"),
        #     load_image(images / "/home/zhaoyibin/3DRE/sfm-learn/SFM_OWN/DATA/dtu/scan10/images/00000022.jpg"),
        # ),
    }

    configs = {
        "LightGlue-full": {
            "depth_confidence": -1,
            "width_confidence": -1,
        },
        # 'LG-prune': {
        #     'width_confidence': -1,
        # },
        # 'LG-depth': {
        #     'depth_confidence': -1,
        # },
        "LightGlue-adaptive": {},
    }

    if args.compile:
        configs = {**configs, **{k + "-compile": v for k, v in configs.items()}}

    sg_configs = {
        # 'SuperGlue': {},
        "SuperGlue-fast": {"sinkhorn_iterations": 5}
    }

    torch.set_float32_matmul_precision(args.matmul_precision)

    results = {k: defaultdict(list) for k, v in inputs.items()}

    extractor = SuperPoint(max_num_keypoints=None, detection_threshold=-1)
    extractor = extractor.eval().to(device)
    figsize = (len(inputs) * 4.5, 4.5)
    fig, axes = plt.subplots(1, len(inputs), sharey=True, figsize=figsize)
    axes = axes if len(inputs) > 1 else [axes]
    fig.canvas.manager.set_window_title(f"LightGlue benchmark ({device.type})")

    for title, ax in zip(inputs.keys(), axes):
        ax.set_xscale("log", base=2)
        bases = [2**x for x in range(7, 16)]
        ax.set_xticks(bases, bases)
        ax.grid(which="major")
        if args.measure == "log-time":
            ax.set_yscale("log")
            yticks = [10**x for x in range(6)]
            ax.set_yticks(yticks, yticks)
            mpos = [10**x * i for x in range(6) for i in range(2, 10)]
            mlabel = [
                10**x * i if i in [2, 5] else None
                for x in range(6)
                for i in range(2, 10)
            ]
            ax.set_yticks(mpos, mlabel, minor=True)
            ax.grid(which="minor", linewidth=0.2)
        ax.set_title(title)

        ax.set_xlabel("# keypoints")
        if args.measure == "throughput":
            ax.set_ylabel("Throughput [pairs/s]")
        else:
            ax.set_ylabel("Latency [ms]")

    for name, conf in configs.items():
        print("Run benchmark for:", name)
        torch.cuda.empty_cache()
        matcher = LightGlue(features="mega", flash=not args.no_flash, **conf)
        if args.no_prune_thresholds:
            matcher.pruning_keypoint_thresholds = {
                k: -1 for k in matcher.pruning_keypoint_thresholds
            }
        matcher = matcher.eval().to(device)
        # if name.endswith("compile"):
        #     import torch._dynamo
        #
        #     torch._dynamo.reset()  # avoid buffer overflow
        #     matcher.compile()
        for pair_name, ax in zip(inputs.keys(), axes):
            image0, image1 = [x.to(device) for x in inputs[pair_name]]
            runtimes = []
            for num_kpts in args.num_keypoints:
                extractor.conf.max_num_keypoints = num_kpts
                start = time.time()
                feats0 = extractor.extract(image0)
                feats1 = extractor.extract(image1)
                end = time.time()
                timecost=end-start
                runtime = measure(matcher,{"image0": feats0, "image1": feats1},device=device,r=args.repeat,)["mean"]
                results[pair_name][name].append(1000 / runtime if args.measure == "throughput" else runtime)
            ax.plot(args.num_keypoints, results[pair_name][name], label=name, marker="o")
        del matcher, feats0, feats1

    if args.add_superglue:
        from hloc.matchers.superglue import SuperGlue

        for name, conf in sg_configs.items():
            print("Run benchmark for:", name)
            matcher = SuperGlue(conf)
            matcher = matcher.eval().to(device)
            for pair_name, ax in zip(inputs.keys(), axes):
                image0, image1 = [x.to(device) for x in inputs[pair_name]]
                runtimes = []
                for num_kpts in args.num_keypoints:
                    extractor.conf.max_num_keypoints = num_kpts
                    feats0 = extractor.extract(image0)
                    feats1 = extractor.extract(image1)
                    data = {
                        "image0": image0[None],
                        "image1": image1[None],
                        **{k + "0": v for k, v in feats0.items()},
                        **{k + "1": v for k, v in feats1.items()},
                    }
                    data["scores0"] = data["keypoint_scores0"]
                    data["scores1"] = data["keypoint_scores1"]
                    data["descriptors0"] = (
                        data["descriptors0"].transpose(-1, -2).contiguous()
                    )
                    data["descriptors1"] = (
                        data["descriptors1"].transpose(-1, -2).contiguous()
                    )
                    runtime = measure(matcher, data, device=device, r=args.repeat)[
                        "mean"
                    ]
                    results[pair_name][name].append(
                        1000 / runtime if args.measure == "throughput" else runtime
                    )
                ax.plot(
                    args.num_keypoints, results[pair_name][name], label=name, marker="o"
                )
            del matcher, data, image0, image1, feats0, feats1

    for name, runtimes in results.items():
        print_as_table(runtimes, name, args.num_keypoints)

    axes[0].legend()
    fig.tight_layout()
    if args.save:
        plt.savefig(args.save, dpi=fig.dpi)
    plt.show()