Source code for pina.trainer
""" Trainer module. """
import torch
import pytorch_lightning
from .utils import check_consistency
from .dataset import SamplePointDataset, SamplePointLoader, DataPointDataset
from .solvers.solver import SolverInterface
[docs]
class Trainer(pytorch_lightning.Trainer):
def __init__(self, solver, batch_size=None, **kwargs):
"""
PINA Trainer class for costumizing every aspect of training via flags.
:param solver: A pina:class:`SolverInterface` solver for the differential problem.
:type solver: SolverInterface
:param batch_size: How many samples per batch to load. If ``batch_size=None`` all
samples are loaded and data are not batched, defaults to None.
:type batch_size: int | None
:Keyword Arguments:
The additional keyword arguments specify the training setup
and can be choosen from the `pytorch-lightning
Trainer API <https://lightning.ai/docs/pytorch/stable/common/trainer.html#trainer-class-api>`_
"""
super().__init__(**kwargs)
# check inheritance consistency for solver and batch size
check_consistency(solver, SolverInterface)
if batch_size is not None:
check_consistency(batch_size, int)
self._model = solver
self.batch_size = batch_size
# create dataloader
if solver.problem.have_sampled_points is False:
raise RuntimeError(
f"Input points in {solver.problem.not_sampled_points} "
"training are None. Please "
"sample points in your problem by calling "
"discretise_domain function before train "
"in the provided locations."
)
self._create_or_update_loader()
def _create_or_update_loader(self):
"""
This method is used here because is resampling is needed
during training, there is no need to define to touch the
trainer dataloader, just call the method.
"""
devices = self._accelerator_connector._parallel_devices
if len(devices) > 1:
raise RuntimeError("Parallel training is not supported yet.")
device = devices[0]
dataset_phys = SamplePointDataset(self._model.problem, device)
dataset_data = DataPointDataset(self._model.problem, device)
self._loader = SamplePointLoader(
dataset_phys, dataset_data, batch_size=self.batch_size, shuffle=True
)
pb = self._model.problem
if hasattr(pb, "unknown_parameters"):
for key in pb.unknown_parameters:
pb.unknown_parameters[key] = torch.nn.Parameter(
pb.unknown_parameters[key].data.to(device)
)
[docs]
def train(self, **kwargs):
"""
Train the solver method.
"""
return super().fit(
self._model, train_dataloaders=self._loader, **kwargs
)
@property
def solver(self):
"""
Returning trainer solver.
"""
return self._model