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>
This commit is contained in:
Shaun Zhou
co-authored by
Claude Opus 5
Claude Opus 5
parent
e581d9b3d7
commit
47fb0f1125
@@ -89,6 +89,24 @@ uv run python scripts/export_onnx.py --config configs/train/baseline.yaml \
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--checkpoint runs/baseline/best.pth --output runs/baseline/model.onnx
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```
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换骨架用对应的配置对（训练与评估必须成对，否则构建出的骨架与权重对不上）：
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| 骨架 | 训练 | 评估 | work_dir |
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| --- | --- | --- | --- |
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| `tiny_conv`（默认） | `configs/train/baseline.yaml` | `configs/eval/baseline.yaml` | `runs/baseline` |
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| `resnet18` | `configs/train/resnet18.yaml` | `configs/eval/resnet18.yaml` | `runs/resnet18` |
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```bash
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uv run python train.py --config configs/train/resnet18.yaml
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uv run python eval.py --config configs/eval/resnet18.yaml
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uv run python scripts/export_onnx.py --config configs/train/resnet18.yaml \
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--checkpoint runs/resnet18/best.pth --output runs/resnet18/model.onnx
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```
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要加别的骨架（mobilenet / shufflenet），照 [configs/model/resnet18.yaml](configs/model/resnet18.yaml)
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复制一份改 `arch` 即可——注意保留里面的 `_replace_: true`，否则 `tiny_conv` 的
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`widths/depths/block` 会残留下来传给新骨架。
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产物写在 `work_dir`（默认 `runs/<name>/`）：`train.log`、`config.yaml`、`last.pth`、`best.pth`、`swanlog/`。
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## 多类 / 单类分类
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@@ -0,0 +1,13 @@
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# resnet18 版本的评估配置。
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# 必须和训练时用的模型配置一致，否则构建出来的骨架与权重对不上。
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_base_:
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- ../train/resnet18.yaml
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device: auto
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checkpoint: runs/resnet18/best.pth
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eval:
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split: val
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score_threshold: 0.3
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iou_threshold: 0.5
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topk: 50
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@@ -2,6 +2,9 @@
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model:
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name: defect_net
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backbone:
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# 整块替换：不加这行的话，从 tiny_conv 继承来的 widths/depths/block 会残留，
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# 传给 TorchvisionBackbone 直接报错。见 utils/config.py::deep_merge
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_replace_: true
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name: torchvision
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arch: resnet18
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pretrained: true
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@@ -1,3 +1,5 @@
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from pathlib import Path
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import pytest
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pytest.importorskip("yaml")
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@@ -83,3 +85,52 @@ def test_registry_reports_incompatible_fields_actionably():
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with pytest.raises(TypeError, match=r"不接受的字段.*widths"):
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BACKBONES.build({"name": "torchvision", "arch": "resnet18", "widths": [32]})
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@pytest.mark.parametrize(
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"config_path",
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sorted(str(p) for p in Path("configs").rglob("*.yaml")),
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)
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def test_every_shipped_config_builds_its_components(config_path):
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"""仓库里每个配置都必须能真的构建出组件。
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回归测试：configs/train/resnet18.yaml 曾经无法使用——它同时继承 tiny_conv 与
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resnet18 的模型配置，合并后 backbone 混了两边的字段，构建时直接报错。
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"""
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torch = pytest.importorskip("torch")
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from datasets.yolo import load_classes
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from models import build_model
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from models.registry import BACKBONES
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cfg = load_config(config_path)
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if "model" in cfg:
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if "classes" not in cfg:
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# configs/model/*.yaml 是片段，只能被 _base_ 组合，单独用没有 classes
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pytest.skip(f"{config_path} 是配置片段，不含 classes")
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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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# 单测里不下载 ImageNet 权重
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if model_cfg.get("backbone", {}).get("name") == "torchvision":
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model_cfg["backbone"] = {**model_cfg["backbone"], "pretrained": False}
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model = build_model(model_cfg)
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with torch.no_grad():
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out = model(torch.randn(1, 3, 64, 64))
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assert "heatmap" in out
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simclr_backbone = cfg.get_path("simclr.backbone")
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if simclr_backbone:
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backbone_cfg = dict(simclr_backbone)
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if backbone_cfg.get("name") == "torchvision":
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backbone_cfg["pretrained"] = False
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backbone = BACKBONES.build(backbone_cfg)
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assert backbone.out_channels > 0
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def test_eval_config_matches_its_training_backbone():
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"""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。"""
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for train_cfg, eval_cfg in [
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("configs/train/baseline.yaml", "configs/eval/baseline.yaml"),
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("configs/train/resnet18.yaml", "configs/eval/resnet18.yaml"),
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]:
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assert load_config(train_cfg)["model"] == load_config(eval_cfg)["model"], eval_cfg
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+14
-9
@@ -64,21 +64,25 @@ def deep_merge(base: Mapping, override: Mapping) -> dict:
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merged = dict(base)
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for key, value in override.items():
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if isinstance(value, Mapping) and value.get(REPLACE_KEY):
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merged[key] = _strip_replace_key(value)
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merged[key] = dict(value) # 整块替换；标记本身留到最后统一剥掉
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elif key in merged and isinstance(merged[key], Mapping) and isinstance(value, Mapping):
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merged[key] = deep_merge(merged[key], value)
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else:
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merged[key] = _strip_replace_key(value) if isinstance(value, Mapping) else value
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merged[key] = value
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return merged
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def _strip_replace_key(value: Mapping) -> dict:
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"""去掉 `_replace_` 标记本身，避免它漏进组件的构造参数。"""
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return {
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k: (_strip_replace_key(v) if isinstance(v, Mapping) else v)
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for k, v in value.items()
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if k != REPLACE_KEY
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}
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def strip_replace_keys(value: Any) -> Any:
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"""剥掉所有 `_replace_` 标记，避免它漏进组件的构造参数。
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必须等**全部合并做完**再剥：`_load_raw` 对每个 base 都会先 `deep_merge({}, data)`
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一次，若在那时就剥掉，标记还没来得及参与真正的替换判断就没了。
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"""
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if isinstance(value, Mapping):
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return {k: strip_replace_keys(v) for k, v in value.items() if k != REPLACE_KEY}
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if isinstance(value, list):
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return [strip_replace_keys(v) for v in value]
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return value
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def _load_raw(path: Path, seen: set[Path]) -> dict:
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@@ -132,5 +136,6 @@ def apply_overrides(cfg: dict, overrides: Iterable[str]) -> dict:
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def load_config(path: str | Path, overrides: Iterable[str] = ()) -> Config:
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cfg = _load_raw(Path(path), set())
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cfg = apply_overrides(cfg, overrides)
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cfg = strip_replace_keys(cfg)
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cfg.setdefault("config_path", str(Path(path).resolve()))
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return Config(cfg)
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