83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
import os
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from abc import ABC
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from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping
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from pytorch_lightning.logging import TensorBoardLogger
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class TrainerCallbackConfigMixin(ABC):
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def __init__(self):
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# this is just a summary on variables used in this abstract class,
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# the proper values/initialisation should be done in child class
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self.default_save_path = None
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self.save_checkpoint = None
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self.slurm_job_id = None
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def configure_checkpoint_callback(self):
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"""
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Weight path set in this priority:
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Checkpoint_callback's path (if passed in).
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User provided weights_saved_path
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Otherwise use os.getcwd()
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"""
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if self.checkpoint_callback is True:
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# init a default one
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if self.logger is not None:
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ckpt_path = os.path.join(
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self.default_save_path,
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self.logger.name,
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f'version_{self.logger.version}',
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"checkpoints"
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)
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else:
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ckpt_path = os.path.join(self.default_save_path, "checkpoints")
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self.checkpoint_callback = ModelCheckpoint(
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filepath=ckpt_path
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)
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elif self.checkpoint_callback is False:
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self.checkpoint_callback = None
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if self.checkpoint_callback:
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# set the path for the callbacks
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self.checkpoint_callback.save_function = self.save_checkpoint
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# if checkpoint callback used, then override the weights path
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self.weights_save_path = self.checkpoint_callback.filepath
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# if weights_save_path is still none here, set to current working dir
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if self.weights_save_path is None:
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self.weights_save_path = self.default_save_path
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def configure_early_stopping(self, early_stop_callback, logger):
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if early_stop_callback is True:
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self.early_stop_callback = EarlyStopping(
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monitor='val_loss',
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patience=3,
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verbose=True,
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mode='min'
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)
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self.enable_early_stop = True
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elif not early_stop_callback:
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self.early_stop_callback = None
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self.enable_early_stop = False
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else:
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self.early_stop_callback = early_stop_callback
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self.enable_early_stop = True
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# configure logger
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if logger is True:
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# default logger
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self.logger = TensorBoardLogger(
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save_dir=self.default_save_path,
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version=self.slurm_job_id,
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name='lightning_logs'
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)
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self.logger.rank = 0
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elif logger is False:
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self.logger = None
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else:
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self.logger = logger
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self.logger.rank = 0
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