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[Retiarii] add validation in base trainers #3184
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ultmaster
reviewed
Dec 14, 2020
nni/retiarii/trainer/pytorch/base.py
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| self._val_dataset = getattr(datasets, dataset_cls)(train=False, | ||
| transform=get_default_transform( | ||
| dataset_cls), | ||
| **(dataset_kwargs or {})) | ||
| self._optimizer = getattr(torch.optim, optimizer_cls)( | ||
| model.parameters(), **(optimizer_kwargs or {})) | ||
| self._trainer_kwargs = trainer_kwargs or {'max_epochs': 10} | ||
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| # TODO: we will need at least two (maybe three) data loaders in future. |
| def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int) -> Dict[str, Any]: | ||
| x, y = self.training_step_before_model(batch, batch_idx) | ||
| y_hat = self.model(x) | ||
| return self.training_step_after_model(x, y, y_hat) | ||
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| def training_step_before_model(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int, device = None): | ||
| def training_step_before_model(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_idx: int, device=None): |
There was a problem hiding this comment.
Suggest using self.device
There was a problem hiding this comment.
In MultiModel, different model's input may need to be placed on different devices (called in _train). Currently, the trainer just sets one GPU per model in hard-code.
BTW, train_step and validation_step are not used in PyTorchImageClassificationTrainer. Removed.
nni/retiarii/trainer/pytorch/base.py
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| summed_loss = sum(losses) | ||
| summed_loss.backward() | ||
| for opt in self._optimizers: | ||
| opt.step() | ||
| if batch_idx % 50 == 0: | ||
| nni.report_intermediate_result(report_loss) | ||
| # if batch_idx % 50 == 0: |
There was a problem hiding this comment.
It was for debug. training_loss is not reported. Removed.
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NNI's line limit is 140. You might need to configure your autopep to avoid unwanted linebreaks. :) |
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