Start training. Args: checkpoint_path(`str`, `optional`): The previous saving checkpoint to read, usually it's a `some-file-name.pth` file generated by this trainer. load_all_state(`bool`: `optional`): Load all state out of the `checkpoint_path` file,
(self,
checkpoint_path=None,
load_all_state=True,
*args,
**kwargs)
| 683 | load_ckpt_hooks[0].strict = strict |
| 684 | |
| 685 | def train(self, |
| 686 | checkpoint_path=None, |
| 687 | load_all_state=True, |
| 688 | *args, |
| 689 | **kwargs): |
| 690 | """Start training. |
| 691 | |
| 692 | Args: |
| 693 | checkpoint_path(`str`, `optional`): The previous saving checkpoint to read, |
| 694 | usually it's a `some-file-name.pth` file generated by this trainer. |
| 695 | load_all_state(`bool`: `optional`): Load all state out of the `checkpoint_path` file, including the |
| 696 | state dict of model, optimizer, lr_scheduler, the random state and epoch/iter number. If False, only |
| 697 | the model's state dict will be read, and model will be trained again. |
| 698 | kwargs: |
| 699 | strict(`boolean`): If strict, any unmatched keys will cause an error. |
| 700 | """ |
| 701 | |
| 702 | self._mode = ModeKeys.TRAIN |
| 703 | self.train_dataloader = self.get_train_dataloader() |
| 704 | self.data_loader = self.train_dataloader |
| 705 | self.register_optimizers_hook() |
| 706 | self.register_processors() |
| 707 | self.print_hook_info() |
| 708 | self.set_checkpoint_file_to_hook(checkpoint_path, load_all_state, |
| 709 | kwargs.get('strict', False)) |
| 710 | self.model.train() |
| 711 | |
| 712 | self.train_loop(self.train_dataloader) |
| 713 | |
| 714 | def predict(self, |
| 715 | predict_datasets: Union[Dataset, List[Dataset]], |
no test coverage detected