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Documentation for v0.1 version (#199)

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* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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114
pina/solvers/garom.py
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@@ -4,7 +4,7 @@ import torch
try:
from torch.optim.lr_scheduler import LRScheduler # torch >= 2.0
except ImportError:
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler # torch < 2.0
from torch.optim.lr_scheduler import _LRScheduler as LRScheduler # torch < 2.0
from torch.optim.lr_scheduler import ConstantLR
from .solver import SolverInterface
@@ -22,28 +22,36 @@ class GAROM(SolverInterface):
.. seealso::
**Original reference**: Coscia, D., Demo, N., & Rozza, G. (2023).
Generative Adversarial Reduced Order Modelling.
arXiv preprint arXiv:2305.15881.
*Generative Adversarial Reduced Order Modelling*.
DOI: `arXiv preprint arXiv:2305.15881.
<https://doi.org/10.48550/arXiv.2305.15881>`_.
"""
def __init__(self,
problem,
generator,
discriminator,
extra_features=None,
loss = None,
optimizer_generator=torch.optim.Adam,
optimizer_generator_kwargs={'lr' : 0.001},
optimizer_discriminator=torch.optim.Adam,
optimizer_discriminator_kwargs={'lr' : 0.001},
scheduler_generator=ConstantLR,
scheduler_generator_kwargs={"factor": 1, "total_iters": 0},
scheduler_discriminator=ConstantLR,
scheduler_discriminator_kwargs={"factor": 1, "total_iters": 0},
gamma = 0.3,
lambda_k = 0.001,
regularizer = False,
):
def __init__(
self,
problem,
generator,
discriminator,
extra_features=None,
loss=None,
optimizer_generator=torch.optim.Adam,
optimizer_generator_kwargs={'lr': 0.001},
optimizer_discriminator=torch.optim.Adam,
optimizer_discriminator_kwargs={'lr': 0.001},
scheduler_generator=ConstantLR,
scheduler_generator_kwargs={
"factor": 1,
"total_iters": 0
},
scheduler_discriminator=ConstantLR,
scheduler_discriminator_kwargs={
"factor": 1,
"total_iters": 0
},
gamma=0.3,
lambda_k=0.001,
regularizer=False,
):
"""
:param AbstractProblem problem: The formualation of the problem.
:param torch.nn.Module generator: The neural network model to use
@@ -77,11 +85,11 @@ class GAROM(SolverInterface):
rate scheduler for the discriminator.
:param dict scheduler_discriminator_kwargs: LR scheduler constructor keyword args.
:param gamma: Ratio of expected loss for generator and discriminator, defaults to 0.3.
:type gamma: float, optional
:type gamma: float
:param lambda_k: Learning rate for control theory optimization, defaults to 0.001.
:type lambda_k: float, optional
:type lambda_k: float
:param regularizer: Regularization term in the GAROM loss, defaults to False.
:type regularizer: bool, optional
:type regularizer: bool
.. warning::
The algorithm works only for data-driven model. Hence in the ``problem`` definition
@@ -90,22 +98,27 @@ class GAROM(SolverInterface):
"""
if isinstance(extra_features, dict):
extra_features = [extra_features['generator'], extra_features['discriminator']]
extra_features = [
extra_features['generator'], extra_features['discriminator']
]
super().__init__(
models=[generator, discriminator],
problem=problem,
extra_features=extra_features,
optimizers=[optimizer_generator, optimizer_discriminator],
optimizers_kwargs=[
optimizer_generator_kwargs, optimizer_discriminator_kwargs
])
super().__init__(models=[generator, discriminator],
problem=problem,
extra_features=extra_features,
optimizers=[optimizer_generator, optimizer_discriminator],
optimizers_kwargs=[optimizer_generator_kwargs, optimizer_discriminator_kwargs])
# set automatic optimization for GANs
self.automatic_optimization = False
# set loss
if loss is None:
loss = PowerLoss(p=1)
# check consistency
# check consistency
check_consistency(scheduler_generator, LRScheduler, subclass=True)
check_consistency(scheduler_generator_kwargs, dict)
check_consistency(scheduler_discriminator, LRScheduler, subclass=True)
@@ -134,6 +147,20 @@ class GAROM(SolverInterface):
self.regularizer = float(regularizer)
def forward(self, x, mc_steps=20, variance=False):
"""
Forward step for GAROM solver
:param x: The input tensor.
:type x: torch.Tensor
:param mc_steps: Number of montecarlo samples to approximate the
expected value, defaults to 20.
:type mc_steps: int
:param variance: Returining also the sample variance of the solution, defaults to False.
:type variance: bool
:return: The expected value of the generator distribution. If ``variance=True`` also the
sample variance is returned.
:rtype: torch.Tensor | tuple(torch.Tensor, torch.Tensor)
"""
# sampling
field_sample = [self.sample(x) for _ in range(mc_steps)]
@@ -147,10 +174,11 @@ class GAROM(SolverInterface):
return mean, var
return mean
def configure_optimizers(self):
"""Optimizer configuration for the GAROM
solver.
"""
Optimizer configuration for the GAROM
solver.
:return: The optimizers and the schedulers
:rtype: tuple(list, list)
@@ -220,7 +248,7 @@ class GAROM(SolverInterface):
return diff
def training_step(self, batch, batch_idx):
"""PINN solver training step.
"""GAROM solver training step.
:param batch: The batch element in the dataloader.
:type batch: tuple
@@ -265,27 +293,27 @@ class GAROM(SolverInterface):
self.log('stability_metric', float(d_loss_real + torch.abs(diff)), prog_bar=True, logger=True, on_epoch=True, on_step=False)
return
@property
def generator(self):
return self.models[0]
@property
def discriminator(self):
return self.models[1]
@property
def optimizer_generator(self):
return self.optimizers[0]
@property
def optimizer_discriminator(self):
return self.optimizers[1]
@property
def scheduler_generator(self):
return self._schedulers[0]
@property
def scheduler_discriminator(self):
return self._schedulers[1]