105 lines
4.0 KiB
Python
105 lines
4.0 KiB
Python
import os
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from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping, ProgressBarBase, ProgressBar
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from pytorch_lightning.utilities.exceptions import MisconfigurationException
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from pytorch_lightning.utilities.model_utils import is_overridden
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class CallbackConnector:
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def __init__(self, trainer):
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self.trainer = trainer
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def on_trainer_init(
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self,
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callbacks,
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early_stop_callback,
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checkpoint_callback,
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progress_bar_refresh_rate,
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process_position,
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default_root_dir,
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weights_save_path,
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resume_from_checkpoint
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):
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self.trainer.resume_from_checkpoint = resume_from_checkpoint
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# init folder paths for checkpoint + weights save callbacks
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self.trainer._default_root_dir = default_root_dir or os.getcwd()
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self.trainer._weights_save_path = weights_save_path or self.trainer._default_root_dir
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# init callbacks
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self.trainer.callbacks = callbacks or []
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# configure early stop callback
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# creates a default one if none passed in
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early_stop_callback = self.configure_early_stopping(early_stop_callback)
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if early_stop_callback:
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self.trainer.callbacks.append(early_stop_callback)
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# configure checkpoint callback
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# it is important that this is the last callback to run
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# pass through the required args to figure out defaults
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checkpoint_callback = self.configure_checkpoint_callback(checkpoint_callback)
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if checkpoint_callback:
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self.trainer.callbacks.append(checkpoint_callback)
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# TODO refactor codebase (tests) to not directly reach into these callbacks
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self.trainer.checkpoint_callback = checkpoint_callback
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self.trainer.early_stop_callback = early_stop_callback
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# init progress bar
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self.trainer._progress_bar_callback = self.configure_progress_bar(
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progress_bar_refresh_rate, process_position
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)
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def configure_checkpoint_callback(self, checkpoint_callback):
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if checkpoint_callback is True:
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# when no val step is defined, use 'loss' otherwise 'val_loss'
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train_step_only = not is_overridden('validation_step', self.trainer.get_model())
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monitor_key = 'loss' if train_step_only else 'val_loss'
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checkpoint_callback = ModelCheckpoint(
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filepath=None,
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monitor=monitor_key
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)
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elif checkpoint_callback is False:
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checkpoint_callback = None
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if checkpoint_callback:
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checkpoint_callback.save_function = self.trainer.save_checkpoint
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return checkpoint_callback
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def configure_early_stopping(self, early_stop_callback):
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if early_stop_callback is True or None:
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early_stop_callback = EarlyStopping(
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monitor='val_loss',
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patience=3,
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strict=True,
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verbose=True,
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mode='min'
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)
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elif not early_stop_callback:
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early_stop_callback = None
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else:
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early_stop_callback = early_stop_callback
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return early_stop_callback
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def configure_progress_bar(self, refresh_rate=1, process_position=0):
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progress_bars = [c for c in self.trainer.callbacks if isinstance(c, ProgressBarBase)]
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if len(progress_bars) > 1:
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raise MisconfigurationException(
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'You added multiple progress bar callbacks to the Trainer, but currently only one'
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' progress bar is supported.'
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)
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elif len(progress_bars) == 1:
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progress_bar_callback = progress_bars[0]
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elif refresh_rate > 0:
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progress_bar_callback = ProgressBar(
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refresh_rate=refresh_rate,
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process_position=process_position,
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)
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self.trainer.callbacks.append(progress_bar_callback)
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else:
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progress_bar_callback = None
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return progress_bar_callback
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