Modify tuner best.pt logic to train first (ultralytics#8792)

ultralytics/engine/tuner.py

@@ -188,11 +188,11 @@ def __call__(self, model=None, iterations=10, cleanup=True):
             train_args = {**vars(self.args), **mutated_hyp}
             save_dir = get_save_dir(get_cfg(train_args))
             weights_dir = save_dir / "weights"
-            ckpt_file = weights_dir / ("best.pt" if (weights_dir / "best.pt").exists() else "last.pt")
             try:
                 # Train YOLO model with mutated hyperparameters (run in subprocess to avoid dataloader hang)
                 cmd = ["yolo", "train", *(f"{k}={v}" for k, v in train_args.items())]
                 return_code = subprocess.run(cmd, check=True).returncode
+                ckpt_file = weights_dir / ("best.pt" if (weights_dir / "best.pt").exists() else "last.pt")
                 metrics = torch.load(ckpt_file)["train_metrics"]
                 assert return_code == 0, "training failed"