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>

tests/test_models.py

@@ -0,0 +1,92 @@
+import pytest
+
+torch = pytest.importorskip("torch")
+
+from models import build_model  # noqa: E402
+from models.postprocess import decode_heatmap  # noqa: E402
+
+
+@pytest.fixture
+def model_cfg():
+    return {
+        "name": "defect_net",
+        "num_classes": 3,
+        "backbone": {"name": "tiny_conv", "widths": [8, 16, 32], "depths": [1, 1, 1]},
+        "neck": {"name": "upsample", "channels": 16, "num_upsamples": 2},
+        "head": {"hidden_channels": 16},
+    }
+
+
+def test_forward_shapes_and_stride(model_cfg):
+    model = build_model(model_cfg).eval()
+    # stem(2) * 2 个下采样 stage = 8，再上采样 4 倍 => stride 2
+    assert model.out_stride == 2
+
+    with torch.no_grad():
+        out = model(torch.randn(2, 3, 64, 64))
+
+    size = 64 // model.out_stride
+    assert out["heatmap"].shape == (2, 3, size, size)
+    assert out["wh"].shape == (2, 2, size, size)
+    assert out["offset"].shape == (2, 2, size, size)
+    assert out["logits"].shape == (2, 3)
+
+
+def test_backbone_is_convolution_only(model_cfg):
+    model = build_model(model_cfg)
+    forbidden = (torch.nn.MultiheadAttention, torch.nn.Transformer, torch.nn.LSTM, torch.nn.GRU)
+    assert not any(isinstance(m, forbidden) for m in model.modules())
+
+
+def test_without_classification_head(model_cfg):
+    model = build_model({**model_cfg, "with_classification": False}).eval()
+    with torch.no_grad():
+        out = model(torch.randn(1, 3, 64, 64))
+    assert "logits" not in out
+
+
+def test_decode_heatmap_recovers_planted_box():
+    heatmap = torch.full((1, 2, 32, 32), -10.0)
+    heatmap[0, 1, 8, 10] = 10.0
+    wh = torch.zeros(1, 2, 32, 32)
+    wh[0, :, 8, 10] = torch.tensor([4.0, 6.0])
+    offset = torch.zeros(1, 2, 32, 32)
+
+    (result,) = decode_heatmap(heatmap, wh, offset, stride=4, topk=5, score_threshold=0.5)
+
+    assert result["labels"].tolist() == [1]
+    assert result["boxes"][0].tolist() == pytest.approx([32.0, 20.0, 48.0, 44.0])
+
+
+def test_losses_decrease_on_a_single_batch(model_cfg):
+    from engine.losses import DetectionLoss
+
+    torch.manual_seed(0)
+    model = build_model(model_cfg)
+    criterion = DetectionLoss()
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-2)
+
+    images = torch.randn(2, 3, 64, 64)
+    size = 64 // model.out_stride
+    target = {
+        "heatmap": torch.zeros(2, 3, size, size),
+        "wh": torch.zeros(2, 2, size, size),
+        "offset": torch.zeros(2, 2, size, size),
+        "mask": torch.zeros(2, size, size),
+    }
+    target["heatmap"][0, 1, 5, 5] = 1.0
+    target["mask"][0, 5, 5] = 1.0
+    target["wh"][0, :, 5, 5] = 3.0
+    batch = {"target": target, "image_labels": torch.zeros(2, 3)}
+
+    first = last = None
+    for step in range(5):
+        losses = criterion(model(images), batch)
+        optimizer.zero_grad()
+        losses["total"].backward()
+        optimizer.step()
+        total = float(losses["total"].detach())
+        first = total if step == 0 else first
+        last = total
+
+    assert last < first