fix(actpred): ONNX opset 升级 + NumPy 兼容 + 断点续训 + MobileNetV2 离线弹性

- export_onnx: opset 17→18，dynamic_axes 改 dynamic_shapes 消除弃用警告
- predict: action_stats 改为 torch tensor，消除 NumPy 2.0 __array_wrap__ 警告
- train/pretrain: 每 epoch 保存 last.pt，新增 --resume 断点续训练
- model: MobileNetEncoder 下载失败时回退到已缓存权重
- _set.sh: 安装时预缓存 MobileNetV2 权重，失败给出明确指引
- test: 新增 TestResumeCheckpoint（2 用例），总计 58 测试全绿

actpred/pretrain_mt.py

@@ -216,12 +216,25 @@ def main():
 
     best_val_loss = float("inf")
     patience_counter = 0
+    start_epoch = 1
     best_path = os.path.join(cfg.train.checkpoint_dir, "pretrain_best.pt")
     inference_path = os.path.join(
         cfg.train.checkpoint_dir, "pretrain_best_inference.pt"
     )
+    last_path = os.path.join(cfg.train.checkpoint_dir, "pretrain_last.pt")
 
-    for epoch in range(1, cfg.train.epochs + 1):
+    if args.resume:
+        resume_ckpt = torch.load(args.resume, map_location=device, weights_only=True)
+        model.load_state_dict(resume_ckpt["model_state_dict"])
+        optimizer.load_state_dict(resume_ckpt["optimizer_state_dict"])
+        scheduler.load_state_dict(resume_ckpt["scheduler_state_dict"])
+        start_epoch = resume_ckpt["epoch"] + 1
+        best_val_loss = resume_ckpt.get("best_val_loss", float("inf"))
+        patience_counter = resume_ckpt.get("patience_counter", 0)
+        print(f"Resumed from {args.resume} at epoch {start_epoch} "
+              f"(best_val_loss={best_val_loss:.6f})")
+
+    for epoch in range(start_epoch, cfg.train.epochs + 1):
         print(f"\nEpoch {epoch}/{cfg.train.epochs}  "
               f"lr={optimizer.param_groups[0]['lr']:.6f}")
         train_loss = pretrain_one_epoch(
@@ -262,6 +275,20 @@ def main():
                       f"(patience={cfg.train.patience})")
                 break
 
+        # 每 epoch 保存 last.pt，支持 --resume 断点续训练
+        torch.save(
+            {
+                "epoch": epoch,
+                "model_state_dict": model.state_dict(),
+                "optimizer_state_dict": optimizer.state_dict(),
+                "scheduler_state_dict": scheduler.state_dict(),
+                "best_val_loss": best_val_loss,
+                "patience_counter": patience_counter,
+                "config": cfg.to_dict(),
+            },
+            last_path,
+        )
+
     print(f"\nPre-training done. Best val_loss={best_val_loss:.6f}")
 
     if best_val_loss == float("inf"):