feat(scripts): 数据转换、ONNX 导出与推理性能分析
- convert_annotations: labelme / VOC -> YOLO txt，顺带生成 data.yaml
- split_dataset: 切分成 images/{train,val} 与 labels/{train,val}，默认建软链不复制数据
- export_onnx: 导出部署用 ONNX，CPU 可跑，缺依赖时提示 `uv sync --group export`
- profile_inference: torch.profiler 测单帧延迟/FPS、CUDA 显存峰值与算子级明细
- check_env: Python / PyTorch / Nvidia GPU 自检
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
Shaun Zhou
co-authored by
Claude Opus 5
Claude Opus 5
parent
b0d0dfffbb
commit
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Executable
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#!/usr/bin/env python
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"""推理性能与显存分析（torch.profiler）。
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部署在实时设备上，显存占用和单帧耗时是硬指标，因此这个脚本在推理路径上跑
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`torch.profiler`，给出算子级的显存与耗时排行，外加 CUDA 显存峰值统计。
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# 用配置里的输入尺寸，batch=1 跑 50 次
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python scripts/profile_inference.py --config configs/train/baseline.yaml \
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--checkpoint runs/baseline/best.pth
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# 扫不同 batch，并导出 chrome trace（chrome://tracing 或 https://ui.perfetto.dev 打开）
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python scripts/profile_inference.py --config configs/train/baseline.yaml \
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--batch-size 1 4 8 --trace runs/baseline/trace.json
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无 GPU 时自动退回 CPU（只有耗时与 CPU 内存，没有显存数据）。
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"""
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from __future__ import annotations
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import argparse
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import json
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import sys
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import time
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
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import torch # noqa: E402
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from torch.profiler import ProfilerActivity, profile, record_function # noqa: E402
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from datasets import load_classes # noqa: E402
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from models import build_model, decode_heatmap # noqa: E402
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from utils import load_config, setup_logging # noqa: E402
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logger = setup_logging()
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def parse_args() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter
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)
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parser.add_argument("--config", required=True)
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parser.add_argument("--checkpoint", default=None, help="不给则用随机初始化的权重，只看结构开销")
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parser.add_argument("--batch-size", type=int, nargs="+", default=[1])
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parser.add_argument("--image-size", type=int, nargs=2, default=None, help="默认取配置的 image_size")
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parser.add_argument("--iters", type=int, default=50, help="计时轮数")
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parser.add_argument("--warmup", type=int, default=10, help="预热轮数（不计入统计）")
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parser.add_argument("--device", default=None, help="默认有 GPU 用 cuda，否则 cpu")
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parser.add_argument("--amp", action="store_true", help="用 float16 autocast 推理")
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parser.add_argument("--postprocess", action="store_true", help="把 heatmap 解码也计入")
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parser.add_argument("--rows", type=int, default=15, help="算子排行显示条数")
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parser.add_argument("--trace", default=None, help="导出 chrome trace 的路径")
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parser.add_argument("--out", default=None, help="把汇总结果写入 json")
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return parser.parse_args()
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def build(args: argparse.Namespace, device: torch.device) -> torch.nn.Module:
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cfg = load_config(args.config)
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model_cfg = dict(cfg["model"])
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model_cfg.setdefault("num_classes", len(load_classes(cfg["classes"])))
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model_cfg.setdefault("label_mode", str(cfg.get("label_mode", "multilabel")))
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model = build_model(model_cfg)
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if args.checkpoint:
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from engine import load_checkpoint
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load_checkpoint(args.checkpoint, model, map_location="cpu")
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model.to(device).eval()
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model.cfg = cfg
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return model
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@torch.no_grad()
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def run_once(model: torch.nn.Module, images: torch.Tensor, args: argparse.Namespace) -> None:
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device_type = images.device.type
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with torch.autocast(device_type, dtype=torch.float16, enabled=args.amp and device_type == "cuda"):
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with record_function("forward"):
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outputs = model(images)
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if args.postprocess:
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with record_function("postprocess"):
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decode_heatmap(
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outputs["heatmap"].float(),
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outputs["wh"].float(),
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outputs["offset"].float(),
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stride=model.out_stride,
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)
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def measure(model: torch.nn.Module, images: torch.Tensor, args: argparse.Namespace) -> dict:
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"""预热后计时，并统计 CUDA 显存峰值。"""
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device = images.device
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is_cuda = device.type == "cuda"
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for _ in range(args.warmup):
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run_once(model, images, args)
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if is_cuda:
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torch.cuda.synchronize(device)
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torch.cuda.reset_peak_memory_stats(device)
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start = time.perf_counter()
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for _ in range(args.iters):
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run_once(model, images, args)
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if is_cuda:
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torch.cuda.synchronize(device)
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elapsed = time.perf_counter() - start
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batch = images.shape[0]
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result = {
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"batch_size": batch,
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"latency_ms": elapsed / args.iters * 1000,
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"throughput_fps": args.iters * batch / elapsed,
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}
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if is_cuda:
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result.update(
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{
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"peak_allocated_mb": torch.cuda.max_memory_allocated(device) / 1024**2,
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"peak_reserved_mb": torch.cuda.max_memory_reserved(device) / 1024**2,
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"weights_mb": sum(p.numel() * p.element_size() for p in model.parameters()) / 1024**2,
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}
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)
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return result
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def profile_once(model: torch.nn.Module, images: torch.Tensor, args: argparse.Namespace) -> None:
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"""算子级 profiling：按显存占用与耗时各排一次序。"""
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is_cuda = images.device.type == "cuda"
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activities = [ProfilerActivity.CPU] + ([ProfilerActivity.CUDA] if is_cuda else [])
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with profile(activities=activities, profile_memory=True, record_shapes=True) as prof:
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for _ in range(max(args.iters // 5, 1)):
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run_once(model, images, args)
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if is_cuda:
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torch.cuda.synchronize(images.device)
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memory_key = "self_cuda_memory_usage" if is_cuda else "self_cpu_memory_usage"
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time_key = "self_cuda_time_total" if is_cuda else "self_cpu_time_total"
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print(f"\n===== 显存占用 Top {args.rows}（按 {memory_key}）=====")
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print(prof.key_averages().table(sort_by=memory_key, row_limit=args.rows))
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print(f"\n===== 耗时 Top {args.rows}（按 {time_key}）=====")
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print(prof.key_averages().table(sort_by=time_key, row_limit=args.rows))
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if args.trace:
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trace_path = Path(args.trace)
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trace_path.parent.mkdir(parents=True, exist_ok=True)
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prof.export_chrome_trace(str(trace_path))
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print(f"\nchrome trace 已导出: {trace_path}（用 chrome://tracing 或 ui.perfetto.dev 打开）")
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def main() -> int:
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args = parse_args()
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device = torch.device(args.device or ("cuda" if torch.cuda.is_available() else "cpu"))
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if device.type != "cuda":
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logger.warning("当前在 %s 上运行：没有显存数据，只能看耗时与 CPU 内存。", device)
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model = build(args, device)
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cfg = model.cfg
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height, width = args.image_size or cfg.get("image_size", [512, 512])
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channels = 1 if cfg.get("gray") else 3
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logger.info(
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"模型 %s | 参数量 %.2fM | 输出 stride %d | 输入 %dx%dx%d | device %s | amp=%s",
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cfg["model"]["name"],
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sum(p.numel() for p in model.parameters()) / 1e6,
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model.out_stride,
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channels,
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height,
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width,
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device,
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args.amp,
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)
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results = []
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for batch_size in args.batch_size:
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images = torch.randn(batch_size, channels, int(height), int(width), device=device)
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result = measure(model, images, args)
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results.append(result)
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memory = (
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f" | 显存峰值 allocated {result['peak_allocated_mb']:.1f} MB"
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f" / reserved {result['peak_reserved_mb']:.1f} MB"
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if "peak_allocated_mb" in result
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else ""
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)
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logger.info(
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"batch=%-3d 单次 %.2f ms | %.1f FPS%s",
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result["batch_size"],
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result["latency_ms"],
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result["throughput_fps"],
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memory,
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)
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# 算子级明细只对最大的 batch 跑一次，避免输出过长。
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images = torch.randn(max(args.batch_size), channels, int(height), int(width), device=device)
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profile_once(model, images, args)
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if args.out:
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out_path = Path(args.out)
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out_path.parent.mkdir(parents=True, exist_ok=True)
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out_path.write_text(json.dumps(results, indent=2, ensure_ascii=False), encoding="utf-8")
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logger.info("汇总已写入 %s", out_path)
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return 0
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if __name__ == "__main__":
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raise SystemExit(main())
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