Shaun ZhouandClaude Opus 5
b0d0dfffbb
- train.py: 先过 require_cuda() 守卫再建模型/数据，支持 --resume 与 --set 覆盖 - eval.py: 允许 CPU，输出定位与分类指标并可写 json - configs: model/dataset/train/eval 四类，通过 `_base_` 组合； classes 既可直接写列表，也可指向 YOLO data.yaml 由框架读 names Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
105 lines
3.6 KiB
Python
Executable File
105 lines
3.6 KiB
Python
Executable File
#!/usr/bin/env python
|
|
"""训练入口。
|
|
|
|
用法:
|
|
python train.py --config configs/train/baseline.yaml
|
|
python train.py --config configs/train/baseline.yaml --set train.epochs=10 train.optimizer.lr=5e-4
|
|
|
|
硬性约束：没有 Nvidia GPU 时直接退出（见 CLAUDE.MD / utils/device.py）。
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import argparse
|
|
import sys
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
|
|
from datasets import build_dataloader, build_dataset, load_classes
|
|
from engine import Trainer
|
|
from models import build_model
|
|
from utils import check_label_mode, load_config, seed_everything, setup_logging
|
|
from utils.device import NoNvidiaGpuError, require_cuda
|
|
|
|
|
|
def parse_args() -> argparse.Namespace:
|
|
parser = argparse.ArgumentParser(description="薄膜缺陷分类定位模型训练")
|
|
parser.add_argument("--config", required=True, help="yaml 配置路径")
|
|
parser.add_argument("--set", nargs="*", default=[], metavar="KEY=VALUE", help="覆盖配置项")
|
|
parser.add_argument("--work-dir", default=None, help="覆盖配置中的 work_dir")
|
|
parser.add_argument("--resume", default=None, help="从检查点恢复训练")
|
|
return parser.parse_args()
|
|
|
|
|
|
def main() -> int:
|
|
args = parse_args()
|
|
cfg = load_config(args.config, args.set)
|
|
|
|
work_dir = Path(args.work_dir or cfg.get("work_dir", "runs/default"))
|
|
work_dir.mkdir(parents=True, exist_ok=True)
|
|
logger = setup_logging(work_dir / "train.log")
|
|
|
|
try:
|
|
gpus = require_cuda()
|
|
except NoNvidiaGpuError as exc:
|
|
logger.error("%s", exc)
|
|
return 1
|
|
logger.info("检测到 GPU: %s", ", ".join(gpus))
|
|
|
|
seed_everything(int(cfg.get("seed", 42)), bool(cfg.get("deterministic", False)))
|
|
device = torch.device(cfg.get("device", "cuda"))
|
|
|
|
classes = load_classes(cfg["classes"])
|
|
# label_mode 由 yaml 决定，模型与数据集必须一致，这里统一下发。
|
|
label_mode = check_label_mode(str(cfg.get("label_mode", "multilabel")))
|
|
model_cfg = dict(cfg["model"])
|
|
model_cfg.setdefault("num_classes", len(classes))
|
|
model_cfg.setdefault("label_mode", label_mode)
|
|
model = build_model(model_cfg).to(device)
|
|
logger.info(
|
|
"模型 %s 输出 stride=%d，参数量 %.2fM",
|
|
model_cfg["name"],
|
|
model.out_stride,
|
|
sum(p.numel() for p in model.parameters()) / 1e6,
|
|
)
|
|
|
|
loaders = {}
|
|
for split in ("train", "val"):
|
|
split_cfg = cfg["data"].get(split)
|
|
if not split_cfg:
|
|
loaders[split] = None
|
|
continue
|
|
dataset = build_dataset(
|
|
split_cfg["dataset"], classes=classes, stride=model.out_stride, label_mode=label_mode
|
|
)
|
|
loaders[split] = build_dataloader(dataset, **dict(split_cfg.get("loader", {})))
|
|
logger.info("%s 集样本数: %d", split, len(dataset))
|
|
|
|
trainer = Trainer(
|
|
model=model,
|
|
train_loader=loaders["train"],
|
|
val_loader=loaders["val"],
|
|
cfg=cfg.get("train", {}),
|
|
device=device,
|
|
work_dir=work_dir,
|
|
full_config=cfg.to_dict(),
|
|
)
|
|
|
|
if args.resume:
|
|
from engine import load_checkpoint
|
|
|
|
state = load_checkpoint(args.resume, model, trainer.optimizer, map_location=device)
|
|
trainer.start_epoch = int(state.get("epoch", 0)) + 1
|
|
trainer.best_metric = float(state.get("best_metric") or float("-inf"))
|
|
logger.info("已从 %s 恢复，起始 epoch=%d", args.resume, trainer.start_epoch)
|
|
|
|
cfg.dump(work_dir / "config.yaml")
|
|
best = trainer.fit()
|
|
logger.info("训练结束，最佳 %s=%.4f", trainer.monitor, best)
|
|
return 0
|
|
|
|
|
|
if __name__ == "__main__":
|
|
sys.exit(main())
|