test: 单元测试与端到端冒烟

88 个测试覆盖配置继承、注册表、GPU 守卫、YOLO 格式解析、数据集、模型形状、标签模式、聚类原语与 SimCLR 损失，以及 train.py / eval.py / 聚类脚本的入口接线。含两条回归测试： - PCA 白化放大噪声方向导致簇塌陷（默认 whiten=False 的依据） - classes 写成 data.yaml 路径时 num_classes 必须是类别数而非路径字符串长度 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-09-02 17:55:47 +08:00
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+"""YOLO 风格标注的数据集读取。"""
+
+import pytest
+
+pytest.importorskip("torch")
+pytest.importorskip("PIL")
+
+from pathlib import Path  # noqa: E402
+
+import numpy as np  # noqa: E402
+import torch  # noqa: E402
+from PIL import Image  # noqa: E402
+
+from datasets import FilmDefectDataset, collate_fn  # noqa: E402
+
+CLASSES = ["scratch", "bubble"]
+TRANSFORMS = [
+    {"name": "Resize", "size": [64, 64]},
+    {"name": "ToTensor"},
+    {"name": "Normalize", "mean": [0.5, 0.5, 0.5], "std": [0.5, 0.5, 0.5]},
+]
+
+
+@pytest.fixture
+def dataset_root(tmp_path):
+    """YOLO 目录：images/ 与同层 labels/，128x128 的图，标注为归一化 cx cy w h。"""
+    images = tmp_path / "images"
+    labels = tmp_path / "labels"
+    images.mkdir()
+    labels.mkdir()
+
+    for name in ("a.png", "b.png"):
+        Image.fromarray(np.zeros((128, 128, 3), dtype=np.uint8)).save(images / name)
+    # 像素框 (10,10,50,50) -> 中心 (30,30)/128, 宽高 40/128
+    (labels / "a.txt").write_text("0 0.234375 0.234375 0.312500 0.312500\n", encoding="utf-8")
+    (labels / "b.txt").write_text("", encoding="utf-8")  # 空文件 = 无缺陷
+    return images, labels
+
+
+def build(dataset_root, **kwargs):
+    images, _ = dataset_root
+    return FilmDefectDataset(
+        img_dir=images, classes=CLASSES, transforms=TRANSFORMS, stride=4, **kwargs
+    )
+
+
+def test_label_dir_follows_yolo_convention(dataset_root):
+    images, labels = dataset_root
+    dataset = build(dataset_root)
+    assert len(dataset) == 2
+    assert dataset.label_file(images / "a.png") == labels / "a.txt"
+
+
+def test_sample_shapes_and_box_denormalization(dataset_root):
+    dataset = build(dataset_root)
+    sample = dataset[0]
+
+    assert sample["image"].shape == (3, 64, 64)
+    assert sample["target"]["heatmap"].shape == (2, 16, 16)
+    # 归一化框 -> 128px 像素框 (10,10,50,50) -> Resize 到 64 后减半
+    assert sample["boxes"][0].tolist() == pytest.approx([5.0, 5.0, 25.0, 25.0], abs=1e-3)
+    assert sample["labels"].tolist() == [0]
+    assert sample["image_label"].tolist() == [1.0, 0.0]
+
+
+def test_empty_label_file_is_defect_free(dataset_root):
+    sample = build(dataset_root)[1]
+    assert sample["boxes"].numel() == 0
+    assert sample["target"]["mask"].sum() == 0
+    assert sample["image_label"].sum() == 0
+
+
+def test_missing_label_file_is_defect_free(dataset_root):
+    _, labels = dataset_root
+    (labels / "b.txt").unlink()
+    assert build(dataset_root)[1]["boxes"].numel() == 0
+
+
+def test_explicit_label_dir(dataset_root, tmp_path):
+    images, labels = dataset_root
+    other = tmp_path / "yolo_labels"
+    other.mkdir()
+    (other / "a.txt").write_text("1 0.5 0.5 0.25 0.25\n", encoding="utf-8")
+    (other / "b.txt").write_text("", encoding="utf-8")
+
+    dataset = build(dataset_root, label_dir=other)
+    assert dataset[0]["labels"].tolist() == [1]
+
+
+def test_list_file_controls_order(dataset_root, tmp_path):
+    images, _ = dataset_root
+    list_file = tmp_path / "val.txt"
+    list_file.write_text("images/b.png\nimages/a.png\n", encoding="utf-8")
+
+    dataset = build(dataset_root, list_file=list_file)
+    assert [Path(item).name for item in [dataset[i]["meta"]["path"] for i in range(2)]] == [
+        "b.png",
+        "a.png",
+    ]
+
+
+def test_collate_keeps_boxes_as_list(dataset_root):
+    dataset = build(dataset_root)
+    batch = collate_fn([dataset[0], dataset[1]])
+
+    assert batch["images"].shape == (2, 3, 64, 64)
+    assert batch["target"]["heatmap"].shape == (2, 2, 16, 16)
+    assert isinstance(batch["boxes"], list) and len(batch["boxes"]) == 2
+    assert batch["image_labels"].shape == (2, 2)
+
+
+def test_class_id_out_of_range_raises(dataset_root):
+    _, labels = dataset_root
+    (labels / "a.txt").write_text("7 0.5 0.5 0.2 0.2\n", encoding="utf-8")
+    with pytest.raises(ValueError, match="越界"):
+        _ = build(dataset_root)[0]
+
+
+def test_gray_mode_yields_single_channel(dataset_root):
+    images, _ = dataset_root
+    transforms = [
+        {"name": "Resize", "size": [64, 64]},
+        {"name": "ToTensor"},
+        {"name": "Normalize", "mean": [0.5], "std": [0.5]},
+    ]
+    dataset = FilmDefectDataset(
+        img_dir=images, classes=CLASSES, transforms=transforms, stride=4, gray=True
+    )
+    assert dataset[0]["image"].shape == (1, 64, 64)
+
+
+def test_classes_from_data_yaml(dataset_root, tmp_path):
+    pytest.importorskip("yaml")
+    from datasets import write_data_yaml
+
+    images, _ = dataset_root
+    data_yaml = write_data_yaml(tmp_path / "data.yaml", CLASSES, root=tmp_path)
+    dataset = FilmDefectDataset(
+        img_dir=images, classes=data_yaml, transforms=TRANSFORMS, stride=4
+    )
+    assert dataset.classes == CLASSES
+
+
+def test_metrics_on_perfect_prediction():
+    from engine.metrics import DetectionMetric
+
+    metric = DetectionMetric(iou_threshold=0.5)
+    boxes = torch.tensor([[0.0, 0.0, 10.0, 10.0]])
+    metric.update(boxes, torch.tensor([0]), boxes, torch.tensor([0]))
+    result = metric.compute()
+    assert result["precision"] == 1.0 and result["recall"] == 1.0