minor fix

pina/callbacks/__init__.py

@@ -0,0 +1,7 @@
+__all__ = [
+    'SwitchOptimizer',
+    'R3Refinement',
+]
+
+from .optimizer_callbacks import SwitchOptimizer
+from .adaptive_refinment_callbacks import R3Refinement

pina/callbacks/adaptive_refinment_callbacks.py

@@ -0,0 +1,134 @@
+'''PINA Callbacks Implementations'''
+
+from lightning.pytorch.callbacks import Callback
+import torch
+from ..utils import check_consistency
+
+
+
+class R3Refinement(Callback):
+    """
+    PINA implementation of a R3 Refinement Callback.
+
+    .. seealso::
+
+        **Original reference**: Daw, Arka, et al. "Mitigating Propagation Failures
+        in Physics-informed Neural Networks using
+        Retain-Resample-Release (R3) Sampling." (2023).
+        DOI: `10.48550/arXiv.2207.02338
+        < https://doi.org/10.48550/arXiv.2207.02338>`_
+    """
+
+    def __init__(self, sample_every):
+        """
+        R3 routine for sampling new points based on
+        adpative search. The algorithm incrementally
+        accumulate collocation points in regions of
+        high PDE residuals, and release the one which
+        have low residual. Points are sampled uniformmaly
+        in all region where sampling is needed.
+
+        :param int sample_every: Frequency for sampling.
+        """
+        super().__init__()
+
+        # sample every
+        check_consistency(sample_every, int)
+        self._sample_every = sample_every
+            
+    def _compute_residual(self, trainer):
+        """
+        Computes the residuals for a PINN object.
+
+        :return: the total loss, and pointwise loss.
+        :rtype: tuple
+        """
+
+        # extract the solver and device from trainer
+        solver = trainer._model
+        device = trainer._accelerator_connector._accelerator_flag
+
+        # compute residual
+        res_loss = {}
+        tot_loss = []
+        for location in self._sampling_locations:
+            condition = solver.problem.conditions[location]
+            pts = solver.problem.input_pts[location]
+            # send points to correct device
+            pts = pts.to(device)
+            pts = pts.requires_grad_(True)
+            pts.retain_grad()
+            # PINN loss: equation evaluated only on locations where sampling is needed
+            target = condition.equation.residual(pts, solver.forward(pts))
+            res_loss[location] = torch.abs(target)
+            tot_loss.append(torch.abs(target))
+    
+        return torch.vstack(tot_loss), res_loss
+
+    def _r3_routine(self, trainer):
+        """
+        R3 refinement main routine.
+
+        :param Trainer trainer: PINA Trainer.
+        """
+        # compute residual (all device possible)
+        tot_loss, res_loss = self._compute_residual(trainer)
+
+        # !!!!!! From now everything is performed on CPU !!!!!!
+
+        # average loss
+        avg = (tot_loss.mean()).to('cpu') 
+
+        # points to keep
+        old_pts = {}
+        tot_points = 0
+        for location in self._sampling_locations:
+            pts = trainer._model.problem.input_pts[location]
+            labels = pts.labels
+            pts = pts.cpu().detach()
+            residuals = res_loss[location].cpu()
+            mask = (residuals > avg).flatten()
+            # TODO masking remove labels
+            pts = pts[mask]
+            pts.labels = labels
+            ####
+            old_pts[location] = pts
+            tot_points += len(pts)
+
+        # extract new points to sample uniformally for each location
+        n_points = (self._tot_pop_numb - tot_points ) // len(self._sampling_locations)
+        remainder = (self._tot_pop_numb - tot_points ) % len(self._sampling_locations)
+        n_uniform_points = [n_points] * len(self._sampling_locations)
+        n_uniform_points[-1] += remainder
+
+        # sample new points
+        for numb_pts, loc in zip(n_uniform_points, self._sampling_locations):
+            trainer._model.problem.discretise_domain(numb_pts,
+                                                    'random',
+                                                    locations=[loc])
+        # adding previous population points
+        trainer._model.problem.add_points(old_pts)
+
+        # update dataloader
+        trainer._create_or_update_loader()
+
+    def on_train_start(self, trainer, _):
+        # extract locations for sampling
+        problem = trainer._model.problem
+        locations = []
+        for condition_name in problem.conditions:
+            condition = problem.conditions[condition_name]
+            if hasattr(condition, 'location'):
+                locations.append(condition_name)
+        self._sampling_locations = locations
+        
+        # extract total population
+        total_population = 0
+        for location in self._sampling_locations:
+            pts = trainer._model.problem.input_pts[location]
+            total_population += len(pts)
+        self._tot_pop_numb = total_population
+
+    def on_train_epoch_end(self, trainer, __):
+        if trainer.current_epoch % self._sample_every == 0:
+            self._r3_routine(trainer)

pina/callbacks/optimizer_callbacks.py

@@ -0,0 +1,62 @@
+'''PINA Callbacks Implementations'''
+
+from lightning.pytorch.callbacks import Callback
+import torch
+from ..utils import check_consistency
+
+
+class SwitchOptimizer(Callback):
+    """
+    PINA implementation of a Lightining Callback to switch
+    optimizer during training. The rouutine can be used to
+    try multiple optimizers during the training, without the
+    need to stop training.
+    """
+    def __init__(self, new_optimizers, new_optimizers_kwargs, epoch_switch):
+        """
+        SwitchOptimizer is a routine for switching optimizer during training.
+
+        :param torch.optim.Optimizer | list new_optimizers: The model optimizers to
+            switch to. It must be a list of :class:`torch.optim.Optimizer` or list of
+            :class:`torch.optim.Optimizer` for multiple model solvers.
+        :param dict| list new_optimizers: The model optimizers keyword arguments to
+            switch use. It must be a dict or list of dict for multiple optimizers.
+        :param int epoch_switch: Epoch for switching optimizer.
+        """
+        super().__init__()
+
+        # check type consistency
+        check_consistency(new_optimizers, torch.optim.Optimizer, subclass=True)
+        check_consistency(new_optimizers_kwargs, dict)
+        check_consistency(epoch_switch, int)
+
+        if epoch_switch < 1:
+            raise ValueError('epoch_switch must be greater than one.')
+
+        if not isinstance(new_optimizers, list):
+            new_optimizers = [new_optimizers]
+            new_optimizers_kwargs = [new_optimizers_kwargs]
+        len_optimizer = len(new_optimizers)
+        len_optimizer_kwargs = len(new_optimizers_kwargs)
+
+        if len_optimizer_kwargs != len_optimizer:
+            raise ValueError('You must define one dictionary of keyword'
+                             ' arguments for each optimizers.'
+                             f' Got {len_optimizer} optimizers, and'
+                             f' {len_optimizer_kwargs} dicitionaries')
+        
+        # save new optimizers
+        self._new_optimizers = new_optimizers
+        self._new_optimizers_kwargs = new_optimizers_kwargs
+        self._epoch_switch = epoch_switch
+
+    def on_train_epoch_start(self, trainer, __):
+        if trainer.current_epoch == self._epoch_switch:
+            optims = []
+            for idx, (optim, optim_kwargs) in enumerate(
+                zip(self._new_optimizers,
+                    self._new_optimizers_kwargs)
+            ):
+                optims.append(optim(trainer._model.models[idx].parameters(), **optim_kwargs))
+
+            trainer.optimizers = optims