9cae9a438f
* Enable DDP training with batch_size=None and add validity check for split sizes * Refactoring SolverInterfaces (#435) * Solver update + weighting * Updating PINN for 0.2 * Modify GAROM + tests * Adding more versatile loggers * Disable compilation when running on Windows * Fix tests --------- Co-authored-by: giovanni <giovanni.canali98@yahoo.it> Co-authored-by: FilippoOlivo <filippo@filippoolivo.com>
117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
""" Module for SupervisedSolver """
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import torch
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from torch.nn.modules.loss import _Loss
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from .solver import SingleSolverInterface
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from ..utils import check_consistency
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from ..loss.loss_interface import LossInterface
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from ..condition import InputOutputPointsCondition
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class SupervisedSolver(SingleSolverInterface):
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r"""
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SupervisedSolver solver class. This class implements a SupervisedSolver,
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using a user specified ``model`` to solve a specific ``problem``.
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The Supervised Solver class aims to find
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a map between the input :math:`\mathbf{s}:\Omega\rightarrow\mathbb{R}^m`
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and the output :math:`\mathbf{u}:\Omega\rightarrow\mathbb{R}^m`. The input
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can be discretised in space (as in :obj:`~pina.solvers.rom.ROMe2eSolver`),
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or not (e.g. when training Neural Operators).
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Given a model :math:`\mathcal{M}`, the following loss function is
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minimized during training:
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.. math::
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\mathcal{L}_{\rm{problem}} = \frac{1}{N}\sum_{i=1}^N
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\mathcal{L}(\mathbf{u}_i - \mathcal{M}(\mathbf{v}_i))
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where :math:`\mathcal{L}` is a specific loss function,
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default Mean Square Error:
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.. math::
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\mathcal{L}(v) = \| v \|^2_2.
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In this context :math:`\mathbf{u}_i` and :math:`\mathbf{v}_i` means that
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we are seeking to approximate multiple (discretised) functions given
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multiple (discretised) input functions.
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"""
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accepted_conditions_types = InputOutputPointsCondition
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def __init__(self,
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problem,
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model,
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loss=None,
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optimizer=None,
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scheduler=None,
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weighting=None,
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use_lt=True):
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"""
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:param AbstractProblem problem: The formualation of the problem.
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:param torch.nn.Module model: The neural network model to use.
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:param torch.nn.Module loss: The loss function used as minimizer,
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default :class:`torch.nn.MSELoss`.
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:param torch.optim.Optimizer optimizer: The neural network optimizer to
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use; default is :class:`torch.optim.Adam`.
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:param torch.optim.LRScheduler scheduler: Learning
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rate scheduler.
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:param WeightingInterface weighting: The loss weighting to use.
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:param bool use_lt: Using LabelTensors as input during training.
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"""
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if loss is None:
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loss = torch.nn.MSELoss()
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super().__init__(model=model,
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problem=problem,
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optimizer=optimizer,
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scheduler=scheduler,
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weighting=weighting,
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use_lt=use_lt)
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# check consistency
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check_consistency(loss, (LossInterface, _Loss, torch.nn.Module),
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subclass=False)
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self._loss = loss
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def optimization_cycle(self, batch):
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"""
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Perform an optimization cycle by computing the loss for each condition
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in the given batch.
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:param batch: A batch of data, where each element is a tuple containing
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a condition name and a dictionary of points.
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:type batch: list of tuples (str, dict)
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:return: The computed loss for the all conditions in the batch,
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cast to a subclass of `torch.Tensor`. It should return a dict
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containing the condition name and the associated scalar loss.
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:rtype: dict(torch.Tensor)
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"""
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condition_loss = {}
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for condition_name, points in batch:
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input_pts, output_pts = points['input_points'], points['output_points']
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condition_loss[condition_name] = self.loss_data(
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input_pts=input_pts, output_pts=output_pts)
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return condition_loss
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def loss_data(self, input_pts, output_pts):
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"""
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The data loss for the Supervised solver. It computes the loss between
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the network output against the true solution. This function
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should not be override if not intentionally.
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:param input_pts: The input to the neural networks.
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:type input_pts: LabelTensor | torch.Tensor
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:param output_pts: The true solution to compare the
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network solution.
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:type output_pts: LabelTensor | torch.Tensor
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:return: The residual loss.
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:rtype: torch.Tensor
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"""
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return self._loss(self.forward(input_pts), output_pts)
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@property
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def loss(self):
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"""
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Loss for training.
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"""
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return self._loss |