74 lines
1.8 KiB
Python
74 lines
1.8 KiB
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.callbacks.pt_callbacks import EarlyStopping, ModelCheckpoint
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from docs.source.examples.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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# build model
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model = ExampleModel(hparams)
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# callbacks
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early_stop = EarlyStopping(
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monitor='val_acc',
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patience=3,
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mode='min',
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verbose=True,
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)
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model_save_path = '{}/{}/{}'.format(hparams.model_save_path, exp.name, exp.version)
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checkpoint = ModelCheckpoint(
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filepath=model_save_path,
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save_best_only=True,
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verbose=True,
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monitor='val_acc',
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mode='min'
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)
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# configure trainer
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trainer = Trainer(
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experiment=exp,
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checkpoint_callback=checkpoint,
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early_stop_callback=early_stop,
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)
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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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