fix(cluster): 配置层级写错不再静默忽略，并始终打印生效的超参

聚类配置里 optimizer/epochs 等键都在 `simclr:` 段下。写到顶层会被完全忽略、
不报任何错——用户改了 lr 却毫无反应，极难排查。同样的坑还有：改了基类
configs/cluster/film.yaml 的 simclr.optimizer，但运行的是子配置
film_simclr_resnet18.yaml，子配置里的同名键会盖掉基类的修改。

- 顶层出现 optimizer / scheduler / epochs / temperature / projection /
  save_interval / resume / checkpoint / log_interval 时警告并给出正确写法
- method != simclr 却写了 simclr: 段时提示该段不会生效
- SimCLRTrainer 初始化时无条件打印实际生效的 epochs / temperature /
  optimizer / scheduler，不再只在续训时才打印
- 修掉一处假差异：旧存档没有 scheduler_cfg 字段时会误报 "scheduler None -> cosine"，
  那是"不知道"而非"当时没有调度器"

已验证 --set 覆盖、直接改 yaml、旧格式存档三条路径下 lr/weight_decay 均正常生效。

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

tests/test_simclr.py

@@ -489,6 +489,57 @@ def _cluster_argv(work_dir, image_dir, *extra):
     ]
 
 
+def test_warns_when_simclr_keys_are_at_top_level(tiny_image_dir, caplog):
+    """顶层写 optimizer 会被静默忽略，必须警告——否则"改了配置没反应"极难排查。"""
+    import logging
+
+    sys.path.insert(0, "scripts")
+    import cluster_unlabeled as cu
+
+    cfg = {
+        "dataset": {"name": "unlabeled", "root": str(tiny_image_dir)},
+        "optimizer": {"name": "adamw", "lr": 0.9},   # ← 应该放在 simclr: 下
+        "epochs": 100,                               # ← 同上
+        "simclr": {"epochs": 2},
+    }
+    with caplog.at_level(logging.WARNING, logger="mte"):
+        cu.warn_misplaced_keys(cfg, "simclr")
+
+    assert "optimizer" in caplog.text and "epochs" in caplog.text
+    assert "simclr" in caplog.text
+
+
+def test_no_warning_when_keys_are_correctly_nested(tiny_image_dir, caplog):
+    import logging
+
+    sys.path.insert(0, "scripts")
+    import cluster_unlabeled as cu
+
+    cfg = {"dataset": {"name": "unlabeled", "root": str(tiny_image_dir)},
+           "simclr": {"optimizer": {"lr": 0.9}, "epochs": 2}}
+    with caplog.at_level(logging.WARNING, logger="mte"):
+        cu.warn_misplaced_keys(cfg, "simclr")
+    assert "会被忽略" not in caplog.text
+
+
+def test_effective_hyperparams_are_logged_on_every_run(tiny_image_dir, tmp_path, caplog):
+    """不只是续训时——每次训练开始都要打印实际生效的超参。"""
+    import logging
+
+    trainer = _make_trainer(
+        tiny_image_dir, tmp_path / "run", epochs=1,
+        optimizer={"name": "adamw", "lr": 0.00042, "weight_decay": 0.007},
+    )
+    del trainer
+    with caplog.at_level(logging.INFO, logger="mte"):
+        _make_trainer(
+            tiny_image_dir, tmp_path / "run2", epochs=1,
+            optimizer={"name": "adamw", "lr": 0.00042, "weight_decay": 0.007},
+        )
+    assert "SimCLR 超参" in caplog.text
+    assert "0.00042" in caplog.text
+
+
 def test_train_only_stops_before_clustering(tiny_image_dir, tmp_path, monkeypatch):
     """--train-only 只训练存权重，不抽特征也不聚类。"""
     sys.path.insert(0, "scripts")