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>
308 lines
10 KiB
Python
308 lines
10 KiB
Python
""" Module for GAROM """
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import torch
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from .solver import MultiSolverInterface
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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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from ..utils import check_consistency
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from ..loss import LossInterface, PowerLoss
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from torch.nn.modules.loss import _Loss
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class GAROM(MultiSolverInterface):
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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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DOI: `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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accepted_conditions_types = InputOutputPointsCondition
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def __init__(
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self,
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problem,
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generator,
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discriminator,
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loss=None,
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optimizer_generator=None,
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optimizer_discriminator=None,
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scheduler_generator=None,
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scheduler_discriminator=None,
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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 loss: The loss function used as minimizer,
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default ``None``. If ``loss`` is ``None`` the defualt
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``PowerLoss(p=1)`` is used, as in the original paper.
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:param 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 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 Scheduler scheduler_generator: Learning
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rate scheduler for the generator.
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:param Scheduler 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
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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
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:param regularizer: Regularization term in the GAROM loss, defaults to False.
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:type regularizer: bool
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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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# set loss
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if loss is None:
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loss = PowerLoss(p=1)
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super().__init__(
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models=[generator, discriminator],
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problem=problem,
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optimizers=[optimizer_generator, optimizer_discriminator],
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schedulers=[
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scheduler_generator,
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scheduler_discriminator,
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],
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use_lt=False
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)
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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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# set automatic optimization for GANs
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self.automatic_optimization = False
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# check consistency
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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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# 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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"""
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Forward step for GAROM solver
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:param x: The input tensor.
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:type x: torch.Tensor
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:param mc_steps: Number of montecarlo samples to approximate the
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expected value, defaults to 20.
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:type mc_steps: int
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:param variance: Returining also the sample variance of the solution, defaults to False.
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:type variance: bool
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:return: The expected value of the generator distribution. If ``variance=True`` also the
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sample variance is returned.
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:rtype: torch.Tensor | tuple(torch.Tensor, torch.Tensor)
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"""
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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 sample(self, x):
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# sampling
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return self.generator(x)
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def _train_generator(self, parameters, snapshots):
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"""
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Private method to train the generator network.
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"""
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optimizer = self.optimizer_generator
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optimizer.zero_grad()
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generated_snapshots = self.sample(parameters)
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# generator loss
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r_loss = self._loss(snapshots, generated_snapshots)
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d_fake = self.discriminator(
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[generated_snapshots, parameters]
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)
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g_loss = (
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self._loss(d_fake, generated_snapshots) + self.regularizer * r_loss
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)
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# backward step
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g_loss.backward()
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optimizer.step()
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return r_loss, g_loss
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def on_train_batch_end(self, outputs, batch, batch_idx):
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"""
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This method is called at the end of each training batch, and ovverides
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the PytorchLightining implementation for logging the checkpoints.
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:param torch.Tensor outputs: The output from the model for the
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current batch.
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:param tuple batch: The current batch of data.
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:param int batch_idx: The index of the current batch.
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:return: Whatever is returned by the parent
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method ``on_train_batch_end``.
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:rtype: Any
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"""
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# increase by one the counter of optimization to save loggers
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(
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self.trainer.fit_loop.epoch_loop.manual_optimization
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.optim_step_progress.total.completed
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) += 1
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return super().on_train_batch_end(outputs, batch, batch_idx)
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def _train_discriminator(self, parameters, snapshots):
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"""
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Private method to train the discriminator network.
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"""
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optimizer = self.optimizer_discriminator
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optimizer.zero_grad()
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# Generate a batch of images
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generated_snapshots = self.sample(parameters)
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# Discriminator pass
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d_real = self.discriminator([snapshots, parameters])
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d_fake = self.discriminator(
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[generated_snapshots, parameters]
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)
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# evaluate loss
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d_loss_real = self._loss(d_real, snapshots)
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d_loss_fake = self._loss(d_fake, generated_snapshots.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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optimizer.step()
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return d_loss_real, d_loss_fake, d_loss
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def _update_weights(self, d_loss_real, d_loss_fake):
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"""
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Private method to Update the weights of the generator and discriminator
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networks.
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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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return diff
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def optimization_cycle(self, batch):
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"""GAROM 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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:return: The sum of the loss functions.
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:rtype: LabelTensor
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"""
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condition_loss = {}
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for condition_name, points in batch:
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parameters, snapshots = points['input_points'], points['output_points']
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d_loss_real, d_loss_fake, d_loss = self._train_discriminator(
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parameters, snapshots
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)
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r_loss, g_loss = self._train_generator(parameters, snapshots)
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diff = self._update_weights(d_loss_real, d_loss_fake)
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condition_loss[condition_name] = r_loss
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# some extra logging
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self.store_log(
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"d_loss",
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float(d_loss),
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self.get_batch_size(batch)
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)
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self.store_log(
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"g_loss",
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float(g_loss),
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self.get_batch_size(batch)
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)
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self.store_log(
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"stability_metric",
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float(d_loss_real + torch.abs(diff)),
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self.get_batch_size(batch)
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)
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return condition_loss
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def validation_step(self, batch):
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condition_loss = {}
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for condition_name, points in batch:
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parameters, snapshots = points['input_points'], points['output_points']
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snapshots_gen = self.generator(parameters)
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condition_loss[condition_name] = self._loss(snapshots, snapshots_gen)
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loss = self.weighting.aggregate(condition_loss)
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self.store_log('val_loss', loss, self.get_batch_size(batch))
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return loss
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def test_step(self, batch):
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condition_loss = {}
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for condition_name, points in batch:
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parameters, snapshots = points['input_points'], points['output_points']
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snapshots_gen = self.generator(parameters)
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condition_loss[condition_name] = self._loss(snapshots, snapshots_gen)
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loss = self.weighting.aggregate(condition_loss)
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self.store_log('test_loss', loss, self.get_batch_size(batch))
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return loss
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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].instance
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@property
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def optimizer_discriminator(self):
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return self.optimizers[1].instance
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@property
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def scheduler_generator(self):
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return self.schedulers[0].instance
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@property
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def scheduler_discriminator(self):
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return self.schedulers[1].instance
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