Documentation for v0.1 version (#199)
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
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Dario Coscia
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Nicola Demo
Nicola Demo
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3f9305d475
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8b7b61b3bd
@@ -6,37 +6,37 @@ import torch
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from ..utils import check_consistency
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class R3Refinement(Callback):
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"""
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PINA implementation of a R3 Refinement Callback.
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.. seealso::
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**Original reference**: Daw, Arka, et al. "Mitigating Propagation Failures
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in Physics-informed Neural Networks using
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Retain-Resample-Release (R3) Sampling." (2023).
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DOI: `10.48550/arXiv.2207.02338
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< https://doi.org/10.48550/arXiv.2207.02338>`_
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"""
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def __init__(self, sample_every):
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"""
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R3 routine for sampling new points based on
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adpative search. The algorithm incrementally
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accumulate collocation points in regions of
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high PDE residuals, and release the one which
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have low residual. Points are sampled uniformmaly
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in all region where sampling is needed.
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PINA Implementation of an R3 Refinement Callback.
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This callback implements the R3 (Retain-Resample-Release) routine for sampling new points based on adaptive search.
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The algorithm incrementally accumulates collocation points in regions of high PDE residuals, and releases those
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with low residuals. Points are sampled uniformly in all regions where sampling is needed.
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.. seealso::
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Original Reference: Daw, Arka, et al. *Mitigating Propagation Failures in Physics-informed Neural Networks
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using Retain-Resample-Release (R3) Sampling. (2023)*.
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DOI: `10.48550/arXiv.2207.02338
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<https://doi.org/10.48550/arXiv.2207.02338>`_
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:param int sample_every: Frequency for sampling.
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:raises ValueError: If `sample_every` is not an integer.
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Example:
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>>> r3_callback = R3Refinement(sample_every=5)
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"""
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super().__init__()
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# sample every
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check_consistency(sample_every, int)
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self._sample_every = sample_every
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def _compute_residual(self, trainer):
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"""
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Computes the residuals for a PINN object.
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@@ -63,7 +63,7 @@ class R3Refinement(Callback):
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target = condition.equation.residual(pts, solver.forward(pts))
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res_loss[location] = torch.abs(target).as_subclass(torch.Tensor)
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tot_loss.append(torch.abs(target))
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return torch.vstack(tot_loss), res_loss
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def _r3_routine(self, trainer):
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@@ -79,7 +79,7 @@ class R3Refinement(Callback):
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# !!!!!! From now everything is performed on CPU !!!!!!
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# average loss
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avg = (tot_loss.mean()).to('cpu')
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avg = (tot_loss.mean()).to('cpu')
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# points to keep
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old_pts = {}
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@@ -97,16 +97,18 @@ class R3Refinement(Callback):
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tot_points += len(pts)
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# extract new points to sample uniformally for each location
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n_points = (self._tot_pop_numb - tot_points ) // len(self._sampling_locations)
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remainder = (self._tot_pop_numb - tot_points ) % len(self._sampling_locations)
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n_points = (self._tot_pop_numb - tot_points) // len(
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self._sampling_locations)
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remainder = (self._tot_pop_numb - tot_points) % len(
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self._sampling_locations)
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n_uniform_points = [n_points] * len(self._sampling_locations)
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n_uniform_points[-1] += remainder
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# sample new points
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for numb_pts, loc in zip(n_uniform_points, self._sampling_locations):
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trainer._model.problem.discretise_domain(numb_pts,
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'random',
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locations=[loc])
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'random',
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locations=[loc])
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# adding previous population points
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trainer._model.problem.add_points(old_pts)
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@@ -114,6 +116,18 @@ class R3Refinement(Callback):
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trainer._create_or_update_loader()
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def on_train_start(self, trainer, _):
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"""
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Callback function called at the start of training.
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This method extracts the locations for sampling from the problem conditions and calculates the total population.
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:param trainer: The trainer object managing the training process.
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:type trainer: pytorch_lightning.Trainer
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:param _: Placeholder argument (not used).
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:return: None
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:rtype: None
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"""
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# extract locations for sampling
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problem = trainer._model.problem
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locations = []
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@@ -122,7 +136,7 @@ class R3Refinement(Callback):
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if hasattr(condition, 'location'):
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locations.append(condition_name)
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self._sampling_locations = locations
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# extract total population
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total_population = 0
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for location in self._sampling_locations:
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@@ -131,5 +145,17 @@ class R3Refinement(Callback):
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self._tot_pop_numb = total_population
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def on_train_epoch_end(self, trainer, __):
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"""
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Callback function called at the end of each training epoch.
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This method triggers the R3 routine for refinement if the current epoch is a multiple of `_sample_every`.
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:param trainer: The trainer object managing the training process.
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:type trainer: pytorch_lightning.Trainer
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:param __: Placeholder argument (not used).
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:return: None
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:rtype: None
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"""
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if trainer.current_epoch % self._sample_every == 0:
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self._r3_routine(trainer)
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self._r3_routine(trainer)
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