| 372 | return input, label |
| 373 | |
| 374 | def load_model(self, directory, epoch=None): |
| 375 | if not directory: |
| 376 | print("Note that load_model() is skipped as no pretrained model is given") |
| 377 | return |
| 378 | |
| 379 | names = self.get_model_names() |
| 380 | |
| 381 | # By default, the best model is loaded |
| 382 | model_file = "model-best.pth.tar" |
| 383 | |
| 384 | if epoch is not None: |
| 385 | model_file = "model.pth.tar-" + str(epoch) |
| 386 | |
| 387 | for name in names: |
| 388 | model_path = osp.join(directory, name, model_file) |
| 389 | |
| 390 | if not osp.exists(model_path): |
| 391 | raise FileNotFoundError('Model not found at "{}"'.format(model_path)) |
| 392 | |
| 393 | checkpoint = load_checkpoint(model_path) |
| 394 | state_dict = checkpoint["state_dict"] |
| 395 | epoch = checkpoint["epoch"] |
| 396 | |
| 397 | # Ignore fixed token vectors |
| 398 | if "token_prefix" in state_dict: |
| 399 | del state_dict["token_prefix"] |
| 400 | |
| 401 | if "token_suffix" in state_dict: |
| 402 | del state_dict["token_suffix"] |
| 403 | |
| 404 | print("Loading weights to {} " 'from "{}" (epoch = {})'.format(name, model_path, epoch)) |
| 405 | self._models[name].load_state_dict(state_dict, strict=False) |