(self, directory, epoch=None)
| 363 | |
| 364 | |
| 365 | def load_model(self, directory, epoch=None): |
| 366 | if not directory: |
| 367 | print("Note that load_model() is skipped as no pretrained model is given") |
| 368 | return |
| 369 | |
| 370 | names = self.get_model_names() |
| 371 | |
| 372 | # By default, the best model is loaded |
| 373 | model_file = "model-best.pth.tar" |
| 374 | |
| 375 | if epoch is not None: |
| 376 | model_file = "model.pth.tar-" + str(epoch) |
| 377 | |
| 378 | for name in names: |
| 379 | model_path = osp.join(directory, name, model_file) |
| 380 | |
| 381 | if not osp.exists(model_path): |
| 382 | raise FileNotFoundError('Model not found at "{}"'.format(model_path)) |
| 383 | |
| 384 | checkpoint = load_checkpoint(model_path) |
| 385 | state_dict = checkpoint["state_dict"] |
| 386 | epoch = checkpoint["epoch"] |
| 387 | |
| 388 | # Ignore fixed token vectors |
| 389 | if "prompt_learner.token_prefix" in state_dict: |
| 390 | del state_dict["prompt_learner.token_prefix"] |
| 391 | |
| 392 | if "prompt_learner.token_suffix" in state_dict: |
| 393 | del state_dict["prompt_learner.token_suffix"] |
| 394 | |
| 395 | print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) |
| 396 | self._models[name].load_state_dict(state_dict, strict=False) |
| 397 | |
| 398 | |
| 399 |
nothing calls this directly
no outgoing calls
no test coverage detected