| |
| import os |
| import glob |
| import torch |
| from lightning.pytorch.callbacks import Callback, ModelCheckpoint |
|
|
| class LoadLatestCheckpointCallback(Callback): |
| """ |
| 在 test 开始时,自动从 ModelCheckpoint 的 dirpath 目录里 |
| 找到最新的 .ckpt 文件并 load 到 pl_module 中。 |
| """ |
| def on_test_start(self, trainer, pl_module): |
| |
| ckpt_cb: ModelCheckpoint | None = next( |
| (cb for cb in trainer.callbacks if isinstance(cb, ModelCheckpoint)), |
| None |
| ) |
| if ckpt_cb is None: |
| raise RuntimeError("没找到 ModelCheckpoint 回调,无法定位 ckpt 目录。") |
|
|
| ckpt_dir = ckpt_cb.dirpath |
| if not os.path.isdir(ckpt_dir): |
| raise RuntimeError(f"Checkpoint 目录不存在:{ckpt_dir}") |
|
|
| |
| paths = glob.glob(os.path.join(ckpt_dir, "*.ckpt")) |
| if not paths: |
| raise RuntimeError(f"{ckpt_dir} 里没有任何 .ckpt 文件。") |
| latest_ckpt = max(paths, key=os.path.getmtime) |
|
|
| |
| |
| map_loc = {"cpu": "cpu"} |
| if pl_module.device.type == "cuda": |
| map_loc = {"cuda:0": f"cuda:{pl_module.device.index or 0}"} |
| checkpoint = torch.load(latest_ckpt, map_location=map_loc) |
| state_dict = checkpoint.get("state_dict", checkpoint) |
| pl_module.load_state_dict(state_dict) |
|
|
| |
| trainer.logger.log_metrics({"loaded_ckpt": os.path.basename(latest_ckpt)}) |
| print(f"[LoadLatestCheckpoint] loaded {latest_ckpt}") |