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William Falcon
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README.md
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README.md
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@ -143,186 +143,4 @@ python fully_featured_trainer.py --gpus "0;1"
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python fully_featured_trainer.py --gpus "0;1" --interactive
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```
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#### Basic trainer example
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See [this demo](https://github.com/williamFalcon/pytorch-lightning/blob/master/docs/source/examples/fully_featured_trainer.py) for a more robust trainer example.
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```python
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import os
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import sys
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from test_tube import HyperOptArgumentParser, Experiment
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from pytorch_lightning.models.trainer import Trainer
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from pytorch_lightning.utils.arg_parse import add_default_args
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from pytorch_lightning.utils.pt_callbacks import EarlyStopping, ModelCheckpoint
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from demo.example_model import ExampleModel
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def main(hparams):
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"""
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Main training routine specific for this project
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:param hparams:
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:return:
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"""
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# init experiment
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exp = Experiment(
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name=hparams.tt_name,
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debug=hparams.debug,
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save_dir=hparams.tt_save_path,
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version=hparams.hpc_exp_number,
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autosave=False,
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description=hparams.tt_description
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)
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exp.argparse(hparams)
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exp.save()
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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# build model
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model = ExampleModel(hparams)
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# callbacks
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early_stop = EarlyStopping(monitor='val_acc', patience=3, mode='min', verbose=True)
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checkpoint = ModelCheckpoint(filepath=model_save_path, save_function=None, save_best_only=True, verbose=True, monitor='val_acc', mode='min')
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# configure trainer
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trainer = Trainer(experiment=exp, checkpoint_callback=checkpoint, early_stop_callback=early_stop)
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# train model
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trainer.fit(model)
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if __name__ == '__main__':
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# use default args given by lightning
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root_dir = os.path.split(os.path.dirname(sys.modules['__main__'].__file__))[0]
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parent_parser = HyperOptArgumentParser(strategy='random_search', add_help=False)
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add_default_args(parent_parser, root_dir)
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# allow model to overwrite or extend args
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parser = ExampleModel.add_model_specific_args(parent_parser)
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hyperparams = parser.parse_args()
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# train model
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main(hyperparams)
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```
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#### Basic model example
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Here we only show the method signatures. It's up to you to define the content.
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```python
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from torch import nn
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class My_Model(RootModule):
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def __init__(self):
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# define model
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self.l1 = nn.Linear(200, 10)
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# ---------------
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# TRAINING
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def training_step(self, data_batch):
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x, y = data_batch
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y_hat = self.l1(x)
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loss = some_loss(y_hat)
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return loss_val, {'train_loss': loss}
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def validation_step(self, data_batch):
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x, y = data_batch
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y_hat = self.l1(x)
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loss = some_loss(y_hat)
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return loss_val, {'val_loss': loss}
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def validation_end(self, outputs):
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total_accs = []
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for output in outputs:
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total_accs.append(output['val_acc'].item())
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# return a dict
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return {'total_acc': np.mean(total_accs)}
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# ---------------
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# SAVING
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def get_save_dict(self):
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# lightning saves for you. Here's your chance to say what you want to save
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checkpoint = {'state_dict': self.state_dict()}
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return checkpoint
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def load_model_specific(self, checkpoint):
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# lightning loads for you. Here's your chance to say what you want to load
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self.load_state_dict(checkpoint['state_dict'])
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# ---------------
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# TRAINING CONFIG
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def configure_optimizers(self):
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# give lightning the list of optimizers you want to use.
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# lightning will call automatically
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optimizer = self.choose_optimizer('adam', self.parameters(), {'lr': self.hparams.learning_rate}, 'optimizer')
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return [optimizer]
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@property
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def tng_dataloader(self):
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return pytorch_dataloader('train')
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@property
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def val_dataloader(self):
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return pytorch_dataloader('val')
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@property
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def test_dataloader(self):
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return pytorch_dataloader('test')
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# ---------------
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# MODIFY YOUR COMMAND LINE ARGS
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
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parser.add_argument('--out_features', default=20)
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return parser
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```
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### Details
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#### Model definition
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| Name | Description | Input | Return |
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|---|---|---|---|
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| training_step | Called with a batch of data during training | data from your dataloaders | tuple: scalar, dict |
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| validation_step | Called with a batch of data during validation | data from your dataloaders | tuple: scalar, dict |
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| validation_end | Collate metrics from all validation steps | outputs: array where each item is the output of a validation step | dict: for logging |
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| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
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#### Model training
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| Name | Description | Input | Return |
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| configure_optimizers | called during training setup | None | list: optimizers you want to use |
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| tng_dataloader | called during training | None | pytorch dataloader |
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| val_dataloader | called during validation | None | pytorch dataloader |
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| test_dataloader | called during testing | None | pytorch dataloader |
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| add_model_specific_args | called with args you defined in your main. This lets you tailor args for each model and keep main the same | argparse | argparse |
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#### Model Saving/Loading
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| Name | Description | Input | Return |
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| get_save_dict | called when your model needs to be saved (checkpoints, hpc save, etc...) | None | dict to be saved |
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| load_model_specific | called when loading a model | checkpoint: dict you created in get_save_dict | dict: modified in whatever way you want |
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## Optional model hooks.
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Add these to the model whenever you want to configure training behavior.
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### Model lifecycle hooks
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Use these hooks to customize functionality
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| Method | Purpose | Input | Output | Required |
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| on_batch_start() | called right before the batch starts | - | - | N |
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| on_batch_end() | called right after the batch ends | - | - | N |
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| on_epoch_start() | called right before the epoch starts | - | - | N |
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| on_epoch_end() | called right afger the epoch ends | - | - | N |
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| on_pre_performance_check() | called right before the performance check starts | - | - | N |
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| on_post_performance_check() | called right after the batch starts | - | - | N |
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