83 lines
3.6 KiB
Python
83 lines
3.6 KiB
Python
from pytorch_lightning.core.lightning import LightningModule
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities import rank_zero_warn
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class ConfigValidator(object):
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def __init__(self, trainer):
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self.trainer = trainer
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def enforce_datamodule_dataloader_override(self, train_dataloader, val_dataloaders, datamodule):
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# If you supply a datamodule you can't supply train_dataloader or val_dataloaders
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if (train_dataloader or val_dataloaders) and datamodule:
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raise MisconfigurationException(
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'You cannot pass train_dataloader or val_dataloaders to trainer.fit if you supply a datamodule'
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)
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def verify_loop_configurations(self, model: LightningModule):
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r"""
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Checks that the model is configured correctly before training or testing is started.
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Args:
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model: The model to check the configuration.
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"""
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if not self.trainer.testing:
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self.__verify_train_loop_configuration(model)
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self.__verify_eval_loop_configuration(model, 'validation')
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else:
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# check test loop configuration
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self.__verify_eval_loop_configuration(model, 'test')
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def __verify_train_loop_configuration(self, model):
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# -----------------------------------
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# verify model has a training step
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# -----------------------------------
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has_training_step = self.trainer.is_overridden('training_step', model)
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if not has_training_step:
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raise MisconfigurationException(
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'No `training_step()` method defined. Lightning `Trainer` expects as minimum a'
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' `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined.'
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)
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# -----------------------------------
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# verify model has a train dataloader
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# -----------------------------------
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has_train_dataloader = self.trainer.is_overridden('train_dataloader', model)
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if not has_train_dataloader:
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raise MisconfigurationException(
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'No `train_dataloader()` method defined. Lightning `Trainer` expects as minimum a'
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' `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined.'
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)
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# -----------------------------------
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# verify model has optimizer
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# -----------------------------------
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has_optimizers = self.trainer.is_overridden('configure_optimizers', model)
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if not has_optimizers:
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raise MisconfigurationException(
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'No `configure_optimizers()` method defined. Lightning `Trainer` expects as minimum a'
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' `training_step()`, `train_dataloader()` and `configure_optimizers()` to be defined.'
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)
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def __verify_eval_loop_configuration(self, model, eval_loop_name):
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step_name = f'{eval_loop_name}_step'
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# map the dataloader name
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loader_name = f'{eval_loop_name}_dataloader'
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if eval_loop_name == 'validation':
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loader_name = 'val_dataloader'
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has_loader = self.trainer.is_overridden(loader_name, model)
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has_step = self.trainer.is_overridden(step_name, model)
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if has_loader and not has_step:
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rank_zero_warn(
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f'you passed in a {loader_name} but have no {step_name}. Skipping {eval_loop_name} loop'
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)
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if has_step and not has_loader:
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rank_zero_warn(
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f'you defined a {step_name} but have no {loader_name}. Skipping {eval_loop_name} loop'
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)
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