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weighting refactory

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Co-authored-by: Dario Coscia <dariocos99@gmail.com>
This commit is contained in:
giovanni
2025-09-01 11:00:14 +02:00
committed by Giovanni Canali
parent c42bdd575c
commit 96402baf20
12 changed files with 214 additions and 388 deletions
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2
pina/callback/__init__.py
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@@ -4,11 +4,9 @@ __all__ = [
"SwitchOptimizer",
"MetricTracker",
"PINAProgressBar",
"LinearWeightUpdate",
"R3Refinement",
]
from .optimizer_callback import SwitchOptimizer
from .processing_callback import MetricTracker, PINAProgressBar
from .linear_weight_update_callback import LinearWeightUpdate
from .refinement import R3Refinement

87
pina/callback/linear_weight_update_callback.py
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@@ -1,87 +0,0 @@
"""Module for the LinearWeightUpdate callback."""
import warnings
from lightning.pytorch.callbacks import Callback
from ..utils import check_consistency
from ..loss import ScalarWeighting
class LinearWeightUpdate(Callback):
"""
Callback to linearly adjust the weight of a condition from an
initial value to a target value over a specified number of epochs.
"""
def __init__(
self, target_epoch, condition_name, initial_value, target_value
):
"""
Callback initialization.
:param int target_epoch: The epoch at which the weight of the condition
should reach the target value.
:param str condition_name: The name of the condition whose weight
should be adjusted.
:param float initial_value: The initial value of the weight.
:param float target_value: The target value of the weight.
"""
super().__init__()
self.target_epoch = target_epoch
self.condition_name = condition_name
self.initial_value = initial_value
self.target_value = target_value
# Check consistency
check_consistency(self.target_epoch, int, subclass=False)
check_consistency(self.condition_name, str, subclass=False)
check_consistency(self.initial_value, (float, int), subclass=False)
check_consistency(self.target_value, (float, int), subclass=False)
def on_train_start(self, trainer, pl_module):
"""
Initialize the weight of the condition to the specified `initial_value`.
:param Trainer trainer: A :class:`~pina.trainer.Trainer` instance.
:param SolverInterface pl_module: A
:class:`~pina.solver.solver.SolverInterface` instance.
"""
# Check that the target epoch is valid
if not 0 < self.target_epoch <= trainer.max_epochs:
raise ValueError(
"`target_epoch` must be greater than 0"
" and less than or equal to `max_epochs`."
)
# Check that the condition is a problem condition
if self.condition_name not in pl_module.problem.conditions:
raise ValueError(
f"`{self.condition_name}` must be a problem condition."
)
# Check that the initial value is not equal to the target value
if self.initial_value == self.target_value:
warnings.warn(
"`initial_value` is equal to `target_value`. "
"No effective adjustment will be performed.",
UserWarning,
)
# Check that the weighting schema is ScalarWeighting
if not isinstance(pl_module.weighting, ScalarWeighting):
raise ValueError("The weighting schema must be ScalarWeighting.")
# Initialize the weight of the condition
pl_module.weighting.weights[self.condition_name] = self.initial_value
def on_train_epoch_start(self, trainer, pl_module):
"""
Adjust at each epoch the weight of the condition.
:param Trainer trainer: A :class:`~pina.trainer.Trainer` instance.
:param SolverInterface pl_module: A
:class:`~pina.solver.solver.SolverInterface` instance.
"""
if 0 < trainer.current_epoch <= self.target_epoch:
pl_module.weighting.weights[self.condition_name] += (
self.target_value - self.initial_value
) / (self.target_epoch - 1)