feat(datasets): YOLO 风格标注数据集与几何/像素增强

- yolo: images/ 与同层 labels/ 的路径约定、`class_id cx cy w h` 归一化行解析
  （兼容分割多边形取外接矩形）、data.yaml 读写；缺失或空 txt 即无缺陷样本
- film_defect: 抛出图像、框、图像级标签；label_mode 决定多热向量还是互斥下标
- transforms: 图框同步的 Resize / Letterbox / CenterCrop / RandomResizedCrop /
  翻转 / ColorJitter / GaussianBlur / PerImageStandardize
- target_encoder: 高斯半径与 heatmap / wh / offset / mask 监督信号编码

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

datasets/build.py

@@ -0,0 +1,39 @@
+"""按配置构建 Dataset / DataLoader。"""
+
+from __future__ import annotations
+
+from collections.abc import Mapping
+from typing import Any
+
+import torch
+from torch.utils.data import DataLoader, Dataset
+
+from datasets.film_defect import collate_fn
+from datasets.registry import DATASETS
+
+
+def build_dataset(cfg: Mapping[str, Any], **kwargs: Any) -> Dataset:
+    return DATASETS.build(dict(cfg), **kwargs)
+
+
+def build_dataloader(
+    dataset: Dataset,
+    batch_size: int = 8,
+    shuffle: bool = False,
+    num_workers: int = 4,
+    pin_memory: bool | None = None,
+    drop_last: bool = False,
+) -> DataLoader:
+    """`pin_memory` 缺省时只在有 CUDA 设备时开启（CPU/MPS 上开启无意义且会告警）。"""
+    if pin_memory is None:
+        pin_memory = torch.cuda.is_available()
+    return DataLoader(
+        dataset,
+        batch_size=batch_size,
+        shuffle=shuffle,
+        num_workers=num_workers,
+        pin_memory=pin_memory,
+        drop_last=drop_last,
+        collate_fn=collate_fn,
+        persistent_workers=num_workers > 0,
+    )