mirror of
https://github.com/wassname/pytorch-lightning.git
synced 2026-08-28 12:54:28 +08:00
William FalconandJ. Borovec
3431c62d41
* remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * remove error when test dataloader used in test * fix lost model reference * remove error when test dataloader used in test * fix lost model reference * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * moved optimizer types * added tests for warning * fix lost model reference * fix lost model reference * added tests for warning * added tests for warning * refactoring * refactoring * fix imports * refactoring * fix imports * refactoring * fix tests * fix mnist * flake8 * review Co-authored-by: J. Borovec <jirka.borovec@seznam.cz>
62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
from abc import ABC
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from torch import optim
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class ConfigureOptimizersPool(ABC):
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def configure_optimizers(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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return optimizer
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def configure_optimizers_empty(self):
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return None
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def configure_optimizers_lbfgs(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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optimizer = optim.LBFGS(self.parameters(), lr=self.hparams.learning_rate)
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return optimizer
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def configure_optimizers_multiple_optimizers(self):
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"""
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return whatever optimizers we want here.
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:return: list of optimizers
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"""
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# try no scheduler for this model (testing purposes)
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optimizer1 = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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optimizer2 = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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return optimizer1, optimizer2
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def configure_optimizers_single_scheduler(self):
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
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return [optimizer], [lr_scheduler]
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def configure_optimizers_multiple_schedulers(self):
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optimizer1 = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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optimizer2 = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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lr_scheduler1 = optim.lr_scheduler.StepLR(optimizer1, 1, gamma=0.1)
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lr_scheduler2 = optim.lr_scheduler.StepLR(optimizer2, 1, gamma=0.1)
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return [optimizer1, optimizer2], [lr_scheduler1, lr_scheduler2]
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def configure_optimizers_mixed_scheduling(self):
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optimizer1 = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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optimizer2 = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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lr_scheduler1 = optim.lr_scheduler.StepLR(optimizer1, 4, gamma=0.1)
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lr_scheduler2 = optim.lr_scheduler.StepLR(optimizer2, 1, gamma=0.1)
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return [optimizer1, optimizer2], \
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[{'scheduler': lr_scheduler1, 'interval': 'step'}, lr_scheduler2]
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def configure_optimizers_reduce_lr_on_plateau(self):
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optimizer = optim.Adam(self.parameters(), lr=self.hparams.learning_rate)
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lr_scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer)
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return [optimizer], [lr_scheduler]
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