feat(cluster): 加 --train-only，自监督训练与聚类可分两步跑
engine/simclr.py 与 engine/cluster.py 本来就互不引用，唯一接口是 simclr_backbone.pth；耦合只在 CLI——method=simclr 会训练完直接聚类。 此前"只聚类"可以用 simclr.checkpoint 做到，"只训练"没有办法。 - --train-only：训练存完权重即退出，不抽特征也不聚类，并打印下一步的聚类命令 - 与 method=pretrained / simclr.checkpoint / --sweep-k 组合时立刻报错说明原因， 而不是跑完才发现没有产出 - 典型用法：GPU 机器上 --train-only 跑几十小时，之后拿权重反复调 k，每次约 1 分钟 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
5d395b7c25
commit
0f5bb4a5c0
@@ -359,6 +359,28 @@ uv run python scripts/cluster_unlabeled.py --config configs/cluster/film.yaml \
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> **`checkpoint`** = 有权重就**跳过训练**直接聚类（要 `simclr_backbone.pth`）。
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> 拿 `simclr_backbone.pth` 去 `resume` 会报错，因为它不含投影头和优化器状态。
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#### 训练与聚类分开跑
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自监督训练和聚类在代码上本来就是解耦的（`engine/simclr.py` 与 `engine/cluster.py`
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互不引用，唯一接口是 `simclr_backbone.pth`），只是默认一条命令串起来跑。
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需要分两步时——比如在 GPU 机器上专心训练几十小时，之后在别处反复调 k——用
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`--train-only` 断开：
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```bash
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# 第一步：只训练，存完权重就退出，不抽特征也不聚类
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uv run python scripts/cluster_unlabeled.py --config configs/cluster/film_simclr_resnet18.yaml \
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--work-dir runs/simclr_r18 --train-only --set simclr.epochs=40
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# 第二步：只聚类，想换几次 k 就跑几次，每次约 1 分钟
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uv run python scripts/cluster_unlabeled.py --config configs/cluster/film_simclr_resnet18.yaml \
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--work-dir runs/simclr_r18_k4 \
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--set simclr.checkpoint=runs/simclr_r18/simclr_backbone.pth --num-clusters 4
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```
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第一步只产出 `simclr_backbone.pth` 与 `simclr_last.pth`，没有 `assignments.csv` 之类。
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`--train-only` 遇到 `method=pretrained`（没东西可训）、`simclr.checkpoint`（会跳过训练）
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或 `--sweep-k`（要靠聚类才能扫）都会直接报错，不会跑完才发现什么都没产出。
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#### 第二步：复用权重重新聚类
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想换簇数或改聚类参数时**不必重训**——把权重路径填进 `simclr.checkpoint` 就会跳过训练
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@@ -389,6 +389,11 @@ def main() -> int:
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parser.add_argument(
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"--sweep-k", default=None, metavar="A-B", help="只扫簇数（如 2-8）打印指标表，不产出分组"
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)
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parser.add_argument(
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"--train-only",
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action="store_true",
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help="只做 SimCLR 自监督训练，存完权重就退出，不抽特征也不聚类",
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)
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args = parser.parse_args()
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cfg = load_config(args.config, args.set)
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@@ -409,18 +414,46 @@ def main() -> int:
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logger.error("%s", exc)
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return 1
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method = str(cfg.get("method", "pretrained"))
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if args.train_only:
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# 这三种组合下 --train-only 要么无事可做、要么与另一个"提前结束"的开关打架，
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# 与其跑完才发现什么都没产出，不如立刻说清楚
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if method != "simclr":
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logger.error("--train-only 只对 method=simclr 有意义，当前 method=%s（没有要训练的东西）", method)
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return 1
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if cfg.get("simclr", {}).get("checkpoint"):
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logger.error(
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"--train-only 与 simclr.checkpoint 冲突：checkpoint 会跳过训练，"
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"加上 --train-only 就什么都不会做。想接着上次练请改用 simclr.resume。"
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)
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return 1
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if args.sweep_k:
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logger.error("--train-only 与 --sweep-k 不能同时用：前者不聚类，后者要靠聚类才能扫")
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return 1
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dataset = build_dataset(dict(cfg["dataset"]))
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logger.info(
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"扫描到 %d 张图，来自 %d 个目录；簇数 %d，method=%s，设备 %s",
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len(dataset),
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len({dataset.source_dir(path) for path in dataset.images}),
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num_clusters,
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cfg.get("method", "pretrained"),
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method,
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describe_device(device),
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)
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encoder = build_encoder(cfg, device, work_dir)
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if args.train_only:
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weights = work_dir / "simclr_backbone.pth"
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logger.info("--train-only：训练完成，未做聚类。权重在 %s", weights)
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logger.info(
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"接着聚类：uv run python scripts/cluster_unlabeled.py --config %s "
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"--work-dir <新目录> --set simclr.checkpoint=%s",
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cfg.get("config_path", "<配置>"),
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weights,
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)
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return 0
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from torch.utils.data import DataLoader
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loader = DataLoader(
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@@ -480,6 +480,99 @@ def test_cluster_script_simclr_branch_end_to_end(tiny_image_dir, tmp_path, monke
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assert (work_dir / "clusters" / "cluster_00").is_dir()
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def _cluster_argv(work_dir, image_dir, *extra):
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return [
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"cluster_unlabeled.py", "--config", "configs/cluster/film.yaml",
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"--work-dir", str(work_dir), "--root", str(image_dir), "--set",
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"device=cpu", "loader.num_workers=0", "loader.batch_size=4",
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"num_clusters=2", "n_init=2", *extra,
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]
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def test_train_only_stops_before_clustering(tiny_image_dir, tmp_path, monkeypatch):
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"""--train-only 只训练存权重，不抽特征也不聚类。"""
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sys.path.insert(0, "scripts")
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import cluster_unlabeled as cu
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work_dir = tmp_path / "train_only"
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monkeypatch.setattr(
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sys, "argv",
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[*_cluster_argv(work_dir, tiny_image_dir,
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"method=simclr", "simclr.epochs=1", "simclr.batch_size=4",
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"simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
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"simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8"),
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"--train-only"],
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)
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assert cu.main() == 0
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assert (work_dir / "simclr_backbone.pth").exists()
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assert (work_dir / "simclr_last.pth").exists() # 续训状态照常落盘
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for produced_by_clustering in ("assignments.csv", "summary.json", "cluster_scatter.png", "clusters"):
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assert not (work_dir / produced_by_clustering).exists(), produced_by_clustering
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def test_train_only_then_cluster_only(tiny_image_dir, tmp_path, monkeypatch):
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"""训练与聚类可以分两次跑：第一次只训练，第二次用 checkpoint 只聚类。"""
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sys.path.insert(0, "scripts")
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import cluster_unlabeled as cu
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train_dir = tmp_path / "phase1"
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monkeypatch.setattr(
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sys, "argv",
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[*_cluster_argv(train_dir, tiny_image_dir,
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"method=simclr", "simclr.epochs=1", "simclr.batch_size=4",
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"simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]",
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"simclr.projection.hidden_channels=16", "simclr.projection.out_channels=8"),
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"--train-only"],
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)
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assert cu.main() == 0
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cluster_dir = tmp_path / "phase2"
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monkeypatch.setattr(
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sys, "argv",
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_cluster_argv(cluster_dir, tiny_image_dir,
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"method=simclr",
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f"simclr.checkpoint={train_dir / 'simclr_backbone.pth'}",
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"simclr.backbone.widths=[8,16]", "simclr.backbone.depths=[1,1]"),
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)
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assert cu.main() == 0
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assert (cluster_dir / "assignments.csv").exists()
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assert not (cluster_dir / "simclr_backbone.pth").exists(), "第二步不该重新训练"
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@pytest.mark.parametrize(
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"extra,reason",
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[
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([], "method=pretrained 没有要训练的东西"),
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(["method=simclr", "simclr.checkpoint=whatever.pth"], "checkpoint 会跳过训练"),
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],
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)
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def test_train_only_rejects_meaningless_combinations(
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tiny_image_dir, tmp_path, monkeypatch, extra, reason
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):
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"""这些组合下 --train-only 什么都不会做，应当立刻报错而不是跑完才发现没产出。"""
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sys.path.insert(0, "scripts")
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import cluster_unlabeled as cu
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monkeypatch.setattr(
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sys, "argv", [*_cluster_argv(tmp_path / "bad", tiny_image_dir, *extra), "--train-only"]
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)
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assert cu.main() == 1, reason
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def test_train_only_conflicts_with_sweep_k(tiny_image_dir, tmp_path, monkeypatch):
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sys.path.insert(0, "scripts")
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import cluster_unlabeled as cu
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monkeypatch.setattr(
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sys, "argv",
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[*_cluster_argv(tmp_path / "bad", tiny_image_dir, "method=simclr"),
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"--train-only", "--sweep-k", "2-4"],
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)
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assert cu.main() == 1
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def test_scatter_plot_is_written_next_to_previews(tiny_image_dir, tmp_path, monkeypatch):
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"""cluster_scatter.png 要和 preview 拼图落在同一级目录。"""
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sys.path.insert(0, "scripts")
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