88 lines
3.4 KiB
Python
88 lines
3.4 KiB
Python
"""PINA Callbacks Implementations"""
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import warnings
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from lightning.pytorch.callbacks import Callback
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from ..utils import check_consistency
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from ..loss import ScalarWeighting
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class LinearWeightUpdate(Callback):
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"""
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Callback to linearly adjust the weight of a condition from an
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initial value to a target value over a specified number of epochs.
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"""
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def __init__(
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self, target_epoch, condition_name, initial_value, target_value
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):
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"""
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Callback initialization.
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:param int target_epoch: The epoch at which the weight of the condition
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should reach the target value.
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:param str condition_name: The name of the condition whose weight
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should be adjusted.
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:param float initial_value: The initial value of the weight.
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:param float target_value: The target value of the weight.
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"""
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super().__init__()
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self.target_epoch = target_epoch
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self.condition_name = condition_name
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self.initial_value = initial_value
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self.target_value = target_value
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# Check consistency
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check_consistency(self.target_epoch, int, subclass=False)
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check_consistency(self.condition_name, str, subclass=False)
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check_consistency(self.initial_value, (float, int), subclass=False)
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check_consistency(self.target_value, (float, int), subclass=False)
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def on_train_start(self, trainer, pl_module):
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"""
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Initialize the weight of the condition to the specified `initial_value`.
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:param Trainer trainer: A :class:`~pina.trainer.Trainer` instance.
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:param SolverInterface pl_module: A
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:class:`~pina.solver.solver.SolverInterface` instance.
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"""
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# Check that the target epoch is valid
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if not 0 < self.target_epoch <= trainer.max_epochs:
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raise ValueError(
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"`target_epoch` must be greater than 0"
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" and less than or equal to `max_epochs`."
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)
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# Check that the condition is a problem condition
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if self.condition_name not in pl_module.problem.conditions:
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raise ValueError(
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f"`{self.condition_name}` must be a problem condition."
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)
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# Check that the initial value is not equal to the target value
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if self.initial_value == self.target_value:
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warnings.warn(
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"`initial_value` is equal to `target_value`. "
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"No effective adjustment will be performed.",
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UserWarning,
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)
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# Check that the weighting schema is ScalarWeighting
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if not isinstance(pl_module.weighting, ScalarWeighting):
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raise ValueError("The weighting schema must be ScalarWeighting.")
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# Initialize the weight of the condition
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pl_module.weighting.weights[self.condition_name] = self.initial_value
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def on_train_epoch_start(self, trainer, pl_module):
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"""
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Adjust at each epoch the weight of the condition.
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:param Trainer trainer: A :class:`~pina.trainer.Trainer` instance.
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:param SolverInterface pl_module: A
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:class:`~pina.solver.solver.SolverInterface` instance.
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"""
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if 0 < trainer.current_epoch <= self.target_epoch:
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pl_module.weighting.weights[self.condition_name] += (
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self.target_value - self.initial_value
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) / (self.target_epoch - 1)
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