Shaun ZhouandClaude Opus 5
47fb0f1125
- 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>
137 lines
5.3 KiB
Python
137 lines
5.3 KiB
Python
from pathlib import Path
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import pytest
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pytest.importorskip("yaml")
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from utils.config import Config, apply_overrides, deep_merge, load_config, parse_override # noqa: E402
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def test_deep_merge_is_recursive():
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merged = deep_merge({"a": {"b": 1, "c": 2}}, {"a": {"c": 3}, "d": 4})
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assert merged == {"a": {"b": 1, "c": 3}, "d": 4}
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def test_parse_override_literals():
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assert parse_override("train.lr=1e-3") == ("train.lr", 1e-3)
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assert parse_override("train.amp=False") == ("train.amp", False)
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assert parse_override("model.backbone.name=tiny_conv") == ("model.backbone.name", "tiny_conv")
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def test_apply_overrides_creates_missing_nodes():
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cfg = apply_overrides({}, ["a.b.c=1"])
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assert cfg == {"a": {"b": {"c": 1}}}
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def test_load_config_with_base(tmp_path):
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(tmp_path / "base.yaml").write_text("train:\n lr: 0.1\n epochs: 10\n", encoding="utf-8")
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(tmp_path / "child.yaml").write_text(
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"_base_: ./base.yaml\ntrain:\n lr: 0.01\nwork_dir: runs/x\n", encoding="utf-8"
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)
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cfg = load_config(tmp_path / "child.yaml", ["train.epochs=2"])
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assert isinstance(cfg, Config)
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assert cfg.train.lr == 0.01
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assert cfg.train.epochs == 2
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assert cfg.get_path("work_dir") == "runs/x"
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def test_load_config_detects_cycle(tmp_path):
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(tmp_path / "a.yaml").write_text("_base_: ./b.yaml\n", encoding="utf-8")
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(tmp_path / "b.yaml").write_text("_base_: ./a.yaml\n", encoding="utf-8")
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with pytest.raises(ValueError, match="循环引用"):
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load_config(tmp_path / "a.yaml")
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def test_project_configs_are_loadable():
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cfg = load_config("configs/train/baseline.yaml")
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assert cfg["model"]["name"] == "defect_net"
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assert cfg["data"]["train"]["dataset"]["name"] == "film_defect"
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assert len(cfg["classes"]) > 0
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def test_replace_key_swaps_a_whole_subdict(tmp_path):
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"""换组件时要能整块替换，否则旧组件的专有字段会残留下来传给新组件。"""
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(tmp_path / "base.yaml").write_text(
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"backbone:\n name: tiny_conv\n widths: [32, 64]\n depths: [1, 2]\n block: dw\n",
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encoding="utf-8",
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)
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(tmp_path / "child.yaml").write_text(
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"_base_: ./base.yaml\nbackbone:\n _replace_: true\n name: torchvision\n arch: resnet18\n",
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encoding="utf-8",
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)
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cfg = load_config(tmp_path / "child.yaml")
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assert cfg["backbone"] == {"name": "torchvision", "arch": "resnet18"}
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assert "_replace_" not in cfg["backbone"] # 标记本身不能漏进构造参数
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def test_without_replace_key_subdicts_still_merge(tmp_path):
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(tmp_path / "base.yaml").write_text("backbone:\n name: tiny_conv\n block: dw\n", encoding="utf-8")
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(tmp_path / "child.yaml").write_text(
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"_base_: ./base.yaml\nbackbone:\n block: res\n", encoding="utf-8"
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)
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cfg = load_config(tmp_path / "child.yaml")
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assert cfg["backbone"] == {"name": "tiny_conv", "block": "res"}
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def test_registry_reports_incompatible_fields_actionably():
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"""--set 只改 name 时旧字段会残留，报错要说清楚是哪些字段、该怎么办。"""
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import pytest
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from models.registry import BACKBONES
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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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