a9b1bd2826
Tutorial update and small fixes * Tutorials update + Tutorial FNO * Create a metric tracker callback * Update PINN for logging * Update plotter for plotting * Small fix LabelTensor * Small fix FNO --------- Co-authored-by: Dario Coscia <dariocoscia@cli-10-110-13-250.WIFIeduroamSTUD.units.it> Co-authored-by: Dario Coscia <dariocoscia@dhcp-176.eduroam.sissa.it>
134 lines
4.5 KiB
Python
134 lines
4.5 KiB
Python
""" Module for SupervisedSolver """
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import torch
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try:
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from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0
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except ImportError:
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from torch.optim.lr_scheduler import _LRScheduler as LRScheduler # torch < 2.0
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from torch.optim.lr_scheduler import ConstantLR
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from .solver import SolverInterface
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from ..label_tensor import LabelTensor
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from ..utils import check_consistency
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from ..loss import LossInterface
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from torch.nn.modules.loss import _Loss
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class SupervisedSolver(SolverInterface):
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"""
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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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"""
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def __init__(self,
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problem,
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model,
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extra_features=None,
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loss = torch.nn.MSELoss(),
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optimizer=torch.optim.Adam,
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optimizer_kwargs={'lr' : 0.001},
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scheduler=ConstantLR,
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scheduler_kwargs={"factor": 1, "total_iters": 0},
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):
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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 torch.nn.MSELoss().
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:param torch.nn.Module extra_features: The additional input
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features to use as augmented input.
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:param torch.optim.Optimizer optimizer: The neural network optimizer to
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use; default is `torch.optim.Adam`.
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:param dict optimizer_kwargs: Optimizer constructor keyword args.
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:param float lr: The learning rate; default is 0.001.
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:param torch.optim.LRScheduler scheduler: Learning
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rate scheduler.
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:param dict scheduler_kwargs: LR scheduler constructor keyword args.
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'''
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super().__init__(models=[model],
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problem=problem,
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optimizers=[optimizer],
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optimizers_kwargs=[optimizer_kwargs],
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extra_features=extra_features)
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# check consistency
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check_consistency(scheduler, LRScheduler, subclass=True)
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check_consistency(scheduler_kwargs, dict)
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check_consistency(loss, (LossInterface, _Loss), subclass=False)
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# assign variables
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self._scheduler = scheduler(self.optimizers[0], **scheduler_kwargs)
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self._loss = loss
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self._neural_net = self.models[0]
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def forward(self, x):
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"""Forward pass implementation for the solver.
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:param torch.tensor x: Input data.
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:return: Solver solution.
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:rtype: torch.tensor
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"""
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# extract labels
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x = x.extract(self.problem.input_variables)
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# perform forward pass
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output = self.neural_net(x).as_subclass(LabelTensor)
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# set the labels
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output.labels = self.problem.output_variables
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return output
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def configure_optimizers(self):
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"""Optimizer configuration for the solver.
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:return: The optimizers and the schedulers
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:rtype: tuple(list, list)
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"""
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return self.optimizers, [self.scheduler]
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def training_step(self, batch, batch_idx):
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"""Solver training step.
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:param batch: The batch element in the dataloader.
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:type batch: tuple
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:param batch_idx: The batch index.
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:type batch_idx: int
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:return: The sum of the loss functions.
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:rtype: LabelTensor
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"""
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for condition_name, samples in batch.items():
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if condition_name not in self.problem.conditions:
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raise RuntimeError('Something wrong happened.')
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condition = self.problem.conditions[condition_name]
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# data loss
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if hasattr(condition, 'output_points'):
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input_pts, output_pts = samples
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loss = self.loss(self.forward(input_pts), output_pts) * condition.data_weight
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else:
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raise RuntimeError('Supervised solver works only in data-driven mode.')
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self.log('mean_loss', float(loss), prog_bar=True, logger=True)
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return loss
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@property
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def scheduler(self):
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"""
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Scheduler for training.
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"""
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return self._scheduler
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@property
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def neural_net(self):
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"""
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Neural network for training.
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"""
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return self._neural_net
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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 |