fix(configs): 修复 resnet18 训练配置无法构建，并补上对应的评估配置

- configs/train/resnet18.yaml 此前直接报错：它同时继承 tiny_conv 与 resnet18 的模型
  配置，深度合并后 backbone 混了两边字段。在 configs/model/resnet18.yaml 的 backbone
  上加 _replace_: true 整块替换
- _replace_ 标记此前不生效：_load_raw 对每个 base 都会先 deep_merge({}, data)，
  标记在参与替换判断之前就被剥掉了。改为合并期间保留、load_config 末尾统一剥除
- 新增 configs/eval/resnet18.yaml：评估必须与训练用同一套模型配置，否则构建出的
  骨架与权重对不上，而此前只有 baseline 一个评估配置
- 新增参数化测试：仓库里每个配置都要能真的构建出组件（片段自动跳过），
  且训练/评估配置对的 model 段必须一致
- README 补充骨架与配置对的对应表

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

tests/test_config.py

@@ -1,3 +1,5 @@
+from pathlib import Path
+
 import pytest
 
 pytest.importorskip("yaml")
@@ -83,3 +85,52 @@ def test_registry_reports_incompatible_fields_actionably():
 
     with pytest.raises(TypeError, match=r"不接受的字段.*widths"):
         BACKBONES.build({"name": "torchvision", "arch": "resnet18", "widths": [32]})
+
+
+@pytest.mark.parametrize(
+    "config_path",
+    sorted(str(p) for p in Path("configs").rglob("*.yaml")),
+)
+def test_every_shipped_config_builds_its_components(config_path):
+    """仓库里每个配置都必须能真的构建出组件。
+
+    回归测试：configs/train/resnet18.yaml 曾经无法使用——它同时继承 tiny_conv 与
+    resnet18 的模型配置，合并后 backbone 混了两边的字段，构建时直接报错。
+    """
+    torch = pytest.importorskip("torch")
+    from datasets.yolo import load_classes
+    from models import build_model
+    from models.registry import BACKBONES
+
+    cfg = load_config(config_path)
+
+    if "model" in cfg:
+        if "classes" not in cfg:
+            # configs/model/*.yaml 是片段，只能被 _base_ 组合，单独用没有 classes
+            pytest.skip(f"{config_path} 是配置片段，不含 classes")
+        model_cfg = dict(cfg["model"])
+        model_cfg.setdefault("num_classes", len(load_classes(cfg["classes"])))
+        # 单测里不下载 ImageNet 权重
+        if model_cfg.get("backbone", {}).get("name") == "torchvision":
+            model_cfg["backbone"] = {**model_cfg["backbone"], "pretrained": False}
+        model = build_model(model_cfg)
+        with torch.no_grad():
+            out = model(torch.randn(1, 3, 64, 64))
+        assert "heatmap" in out
+
+    simclr_backbone = cfg.get_path("simclr.backbone")
+    if simclr_backbone:
+        backbone_cfg = dict(simclr_backbone)
+        if backbone_cfg.get("name") == "torchvision":
+            backbone_cfg["pretrained"] = False
+        backbone = BACKBONES.build(backbone_cfg)
+        assert backbone.out_channels > 0
+
+
+def test_eval_config_matches_its_training_backbone():
+    """评估配置构建出的骨架必须与训练配置一致，否则权重根本加载不上。"""
+    for train_cfg, eval_cfg in [
+        ("configs/train/baseline.yaml", "configs/eval/baseline.yaml"),
+        ("configs/train/resnet18.yaml", "configs/eval/resnet18.yaml"),
+    ]:
+        assert load_config(train_cfg)["model"] == load_config(eval_cfg)["model"], eval_cfg