701046661f
* Solvers for multiple models - Implementing the possibility to add multiple models for solvers (e.g. GAN) - Implementing GAROM solver, see https://arxiv.org/abs/2305.15881 - Implementing tests for GAROM solver (cpu only) - Fixing docs PINNs - Creating a solver directory, for consistency in the package --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-040.eduroam.sissa.it>
261 lines
9.7 KiB
Python
261 lines
9.7 KiB
Python
""" Module for PINN """
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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 ..utils import check_consistency
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from ..loss import LossInterface, LpLoss
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from torch.nn.modules.loss import _Loss
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class GAROM(SolverInterface):
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"""
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GAROM solver class. This class implements Generative Adversarial
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Reduced Order Model solver, using user specified ``models`` to solve
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a specific order reduction``problem``.
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.. seealso::
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**Original reference**: Coscia, D., Demo, N., & Rozza, G. (2023).
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Generative Adversarial Reduced Order Modelling.
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arXiv preprint arXiv:2305.15881.
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<https://doi.org/10.48550/arXiv.2305.15881>`_.
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"""
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def __init__(self,
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problem,
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generator,
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discriminator,
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extra_features=None,
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loss = None,
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optimizer_generator=torch.optim.Adam,
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optimizer_generator_kwargs={'lr' : 0.001},
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optimizer_discriminator=torch.optim.Adam,
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optimizer_discriminator_kwargs={'lr' : 0.001},
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scheduler_generator=ConstantLR,
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scheduler_generator_kwargs={"factor": 1, "total_iters": 0},
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scheduler_discriminator=ConstantLR,
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scheduler_discriminator_kwargs={"factor": 1, "total_iters": 0},
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gamma = 0.3,
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lambda_k = 0.001,
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regularizer = False,
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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 generator: The neural network model to use
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for the generator.
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:param torch.nn.Module discriminator: The neural network model to use
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for the discriminator.
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:param torch.nn.Module extra_features: The additional input
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features to use as augmented input. It should either be a
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list of torch.nn.Module, or a dictionary. If a list it is
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passed the extra features are passed to both network. If a
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dictionary is passed, the keys must be ``generator`` and
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``discriminator`` and the values a list of torch.nn.Module
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extra features for each.
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:param torch.nn.Module loss: The loss function used as minimizer,
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default ``None``. If ``loss`` is ``None`` the defualt
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``LpLoss(p=1)`` is used, as in the original paper.
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:param torch.optim.Optimizer optimizer_generator: The neural
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network optimizer to use for the generator network
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, default is `torch.optim.Adam`.
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:param dict optimizer_generator_kwargs: Optimizer constructor keyword
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args. for the generator.
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:param torch.optim.Optimizer optimizer_discriminator: The neural
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network optimizer to use for the discriminator network
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, default is `torch.optim.Adam`.
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:param dict optimizer_discriminator_kwargs: Optimizer constructor keyword
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args. for the discriminator.
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:param torch.optim.LRScheduler scheduler_generator: Learning
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rate scheduler for the generator.
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:param dict scheduler_generator_kwargs: LR scheduler constructor keyword args.
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:param torch.optim.LRScheduler scheduler_discriminator: Learning
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rate scheduler for the discriminator.
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:param dict scheduler_discriminator_kwargs: LR scheduler constructor keyword args.
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:param gamma: Ratio of expected loss for generator and discriminator, defaults to 0.3.
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:type gamma: float, optional
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:param lambda_k: Learning rate for control theory optimization, defaults to 0.001.
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:type lambda_k: float, optional
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:param regularizer: Regularization term in the GAROM loss, defaults to False.
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:type regularizer: bool, optional
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.. warning::
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The algorithm works only for data-driven model. Hence in the ``problem`` definition
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the codition must only contain ``input_points`` (e.g. coefficient parameters, time
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parameters), and ``output_points``.
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"""
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if isinstance(extra_features, dict):
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extra_features = [extra_features['generator'], extra_features['discriminator']]
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super().__init__(models=[generator, discriminator],
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problem=problem,
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extra_features=extra_features,
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optimizers=[optimizer_generator, optimizer_discriminator],
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optimizers_kwargs=[optimizer_generator_kwargs, optimizer_discriminator_kwargs])
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# set automatic optimization for GANs
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self.automatic_optimization = False
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# set loss
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if loss is None:
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loss = LpLoss(p=1)
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# check consistency
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check_consistency(scheduler_generator, LRScheduler, subclass=True)
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check_consistency(scheduler_generator_kwargs, dict)
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check_consistency(scheduler_discriminator, LRScheduler, subclass=True)
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check_consistency(scheduler_discriminator_kwargs, dict)
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check_consistency(loss, (LossInterface, _Loss))
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check_consistency(gamma, float)
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check_consistency(lambda_k, float)
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check_consistency(regularizer, bool)
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# assign schedulers
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self._schedulers = [scheduler_generator(self.optimizers[0],
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**scheduler_generator_kwargs),
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scheduler_discriminator(self.optimizers[1],
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**scheduler_discriminator_kwargs)]
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# loss and writer
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self._loss = loss
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# began hyperparameters
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self.k = 0
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self.gamma = gamma
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self.lambda_k = lambda_k
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self.regularizer = float(regularizer)
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def forward(self, x, mc_steps=20, variance=False):
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# sampling
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field_sample = [self.sample(x) for _ in range(mc_steps)]
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field_sample = torch.stack(field_sample)
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# extract mean
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mean = field_sample.mean(dim=0)
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if variance:
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var = field_sample.var(dim=0)
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return mean, var
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return mean
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def configure_optimizers(self):
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"""Optimizer configuration for the GAROM
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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._schedulers
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def sample(self, x):
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# sampling
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return self.generator(x)
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def training_step(self, batch, batch_idx):
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"""PINN 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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# for data driven mode
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if hasattr(condition, 'output_points'):
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# get data
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parameters, input_pts = samples
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# get optimizers
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opt_gen, opt_disc = self.optimizers
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# ---------------------
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# Train Discriminator
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# ---------------------
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opt_disc.zero_grad()
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# Generate a batch of images
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gen_imgs = self.generator(parameters)
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# Discriminator pass
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d_real = self.discriminator([input_pts, parameters])
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d_fake = self.discriminator([gen_imgs.detach(), parameters])
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# evaluate loss
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d_loss_real = self._loss(d_real, input_pts)
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d_loss_fake = self._loss(d_fake, gen_imgs.detach())
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d_loss = d_loss_real - self.k * d_loss_fake
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# backward step
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d_loss.backward()
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opt_disc.step()
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# -----------------
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# Train Generator
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# -----------------
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opt_gen.zero_grad()
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# Generate a batch of images
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gen_imgs = self.generator(parameters)
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# generator loss
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r_loss = self._loss(input_pts, gen_imgs)
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d_fake = self.discriminator([gen_imgs, parameters])
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g_loss = self._loss(d_fake, gen_imgs) + self.regularizer * r_loss
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# backward step
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g_loss.backward()
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opt_gen.step()
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# ----------------
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# Update weights
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# ----------------
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diff = torch.mean(self.gamma * d_loss_real - d_loss_fake)
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# Update weight term for fake samples
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self.k += self.lambda_k * diff.item()
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self.k = min(max(self.k, 0), 1) # Constraint to interval [0, 1]
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else:
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raise NotImplementedError('GAROM works only in data-driven mode.')
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return
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@property
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def generator(self):
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return self.models[0]
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@property
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def discriminator(self):
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return self.models[1]
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@property
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def optimizer_generator(self):
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return self.optimizers[0]
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@property
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def optimizer_discriminator(self):
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return self.optimizers[1]
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@property
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def scheduler_generator(self):
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return self._schedulers[0]
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@property
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def scheduler_discriminator(self):
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return self._schedulers[1] |