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🎨 Format Python code with psf/black (#297)

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Co-authored-by: dario-coscia <dario-coscia@users.noreply.github.com>
This commit is contained in:
github-actions[bot]
2024-05-10 14:08:01 +02:00
committed by GitHub
parent e0429bb445
commit 9463ae4b15
11 changed files with 169 additions and 160 deletions
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1
pina/solvers/__init__.py
View File

@@ -16,4 +16,3 @@ from .pinns import *
from .supervised import SupervisedSolver
from .rom import ReducedOrderModelSolver
from .garom import GAROM

5
pina/solvers/pinns/basepinn.py
View File

@@ -12,6 +12,7 @@ from torch.nn.modules.loss import _Loss
torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732
class PINNInterface(SolverInterface, metaclass=ABCMeta):
"""
Base PINN solver class. This class implements the Solver Interface
@@ -195,7 +196,7 @@ class PINNInterface(SolverInterface, metaclass=ABCMeta):
:param torch.Tensor loss_value: The value of the loss.
"""
self.log(
self.__logged_metric+'_loss',
self.__logged_metric + "_loss",
loss_value,
prog_bar=True,
logger=True,
@@ -211,7 +212,7 @@ class PINNInterface(SolverInterface, metaclass=ABCMeta):
"""
if self.__logged_res_losses:
# storing mean loss
self.__logged_metric = 'mean'
self.__logged_metric = "mean"
self.store_log(
sum(self.__logged_res_losses) / len(self.__logged_res_losses)
)

6
pina/solvers/pinns/causalpinn.py
View File

@@ -114,8 +114,10 @@ class CausalPINN(PINN):
check_consistency(eps, (int, float))
self._eps = eps
if not isinstance(self.problem, TimeDependentProblem):
raise ValueError('Casual PINN works only for problems'
'inheritig from TimeDependentProblem.')
raise ValueError(
"Casual PINN works only for problems"
"inheritig from TimeDependentProblem."
)
def loss_phys(self, samples, equation):
"""

31
pina/solvers/pinns/competitive_pinn.py
View File

@@ -117,7 +117,7 @@ class CompetitivePINN(PINNInterface):
optimizer_discriminator_kwargs,
],
extra_features=None, # CompetitivePINN doesn't take extra features
loss=loss
loss=loss,
)
# set automatic optimization for GANs
@@ -131,9 +131,7 @@ class CompetitivePINN(PINNInterface):
# assign schedulers
self._schedulers = [
scheduler_model(
self.optimizers[0], **scheduler_model_kwargs
),
scheduler_model(self.optimizers[0], **scheduler_model_kwargs),
scheduler_discriminator(
self.optimizers[1], **scheduler_discriminator_kwargs
),
@@ -195,8 +193,11 @@ class CompetitivePINN(PINNInterface):
:rtype: torch.Tensor
"""
self.optimizer_model.zero_grad()
loss_val = super().loss_data(
input_tensor, output_tensor).as_subclass(torch.Tensor)
loss_val = (
super()
.loss_data(input_tensor, output_tensor)
.as_subclass(torch.Tensor)
)
loss_val.backward()
self.optimizer_model.step()
return loss_val
@@ -235,7 +236,9 @@ class CompetitivePINN(PINNInterface):
:rtype: Any
"""
# increase by one the counter of optimization to save loggers
self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed += 1
self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed += (
1
)
return super().on_train_batch_end(outputs, batch, batch_idx)
def _train_discriminator(self, samples, equation, discriminator_bets):
@@ -252,13 +255,14 @@ class CompetitivePINN(PINNInterface):
self.optimizer_discriminator.zero_grad()
# compute residual, we detach because the weights of the generator
# model are fixed
residual = self.compute_residual(samples=samples,
equation=equation).detach()
residual = self.compute_residual(
samples=samples, equation=equation
).detach()
# compute competitive residual, the minus is because we maximise
competitive_residual = residual * discriminator_bets
loss_val = -self.loss(
torch.zeros_like(competitive_residual, requires_grad=True),
competitive_residual
competitive_residual,
).as_subclass(torch.Tensor)
# backprop
self.manual_backward(loss_val)
@@ -283,16 +287,13 @@ class CompetitivePINN(PINNInterface):
residual = self.compute_residual(samples=samples, equation=equation)
# store logging
with torch.no_grad():
loss_residual = self.loss(
torch.zeros_like(residual),
residual
)
loss_residual = self.loss(torch.zeros_like(residual), residual)
# compute competitive residual, discriminator_bets are detached becase
# we optimize only the generator model
competitive_residual = residual * discriminator_bets.detach()
loss_val = self.loss(
torch.zeros_like(competitive_residual, requires_grad=True),
competitive_residual
competitive_residual,
).as_subclass(torch.Tensor)
# backprop
self.manual_backward(loss_val)

13
pina/solvers/pinns/gpinn.py
View File

@@ -100,11 +100,12 @@ class GPINN(PINN):
scheduler_kwargs=scheduler_kwargs,
)
if not isinstance(self.problem, SpatialProblem):
raise ValueError('Gradient PINN computes the gradient of the '
'PINN loss with respect to the spatial '
'coordinates, thus the PINA problem must be '
'a SpatialProblem.')
raise ValueError(
"Gradient PINN computes the gradient of the "
"PINN loss with respect to the spatial "
"coordinates, thus the PINA problem must be "
"a SpatialProblem."
)
def loss_phys(self, samples, equation):
"""
@@ -126,7 +127,7 @@ class GPINN(PINN):
self.store_log(loss_value=float(loss_value))
# gradient PINN loss
loss_value = loss_value.reshape(-1, 1)
loss_value.labels = ['__LOSS']
loss_value.labels = ["__LOSS"]
loss_grad = grad(loss_value, samples, d=self.problem.spatial_variables)
g_loss_phys = self.loss(
torch.zeros_like(loss_grad, requires_grad=True), loss_grad

5
pina/solvers/pinns/pinn.py
View File

@@ -87,7 +87,7 @@ class PINN(PINNInterface):
optimizers=[optimizer],
optimizers_kwargs=[optimizer_kwargs],
extra_features=extra_features,
loss=loss
loss=loss,
)
# check consistency
@@ -131,7 +131,6 @@ class PINN(PINNInterface):
self.store_log(loss_value=float(loss_value))
return loss_value
def configure_optimizers(self):
"""
Optimizer configuration for the PINN
@@ -153,7 +152,6 @@ class PINN(PINNInterface):
)
return self.optimizers, [self.scheduler]
@property
def scheduler(self):
"""
@@ -161,7 +159,6 @@ class PINN(PINNInterface):
"""
return self._scheduler
@property
def neural_net(self):
"""

51
pina/solvers/pinns/sapinn.py
View File

@@ -14,6 +14,7 @@ from pina.problem import InverseProblem
from torch.optim.lr_scheduler import ConstantLR
class Weights(torch.nn.Module):
"""
This class aims to implements the mask model for
@@ -27,9 +28,7 @@ class Weights(torch.nn.Module):
"""
super().__init__()
check_consistency(func, torch.nn.Module)
self.sa_weights = torch.nn.Parameter(
torch.Tensor()
)
self.sa_weights = torch.nn.Parameter(torch.Tensor())
self.func = func
def forward(self):
@@ -43,6 +42,7 @@ class Weights(torch.nn.Module):
"""
return self.func(self.sa_weights)
class SAPINN(PINNInterface):
r"""
Self Adaptive Physics Informed Neural Network (SAPINN) solver class.
@@ -121,7 +121,7 @@ class SAPINN(PINNInterface):
scheduler_model=ConstantLR,
scheduler_model_kwargs={"factor": 1, "total_iters": 0},
scheduler_weights=ConstantLR,
scheduler_weights_kwargs={"factor" : 1, "total_iters" : 0}
scheduler_weights_kwargs={"factor": 1, "total_iters": 0},
):
"""
:param AbstractProblem problem: The formualation of the problem.
@@ -167,17 +167,16 @@ class SAPINN(PINNInterface):
weights_dict[condition_name] = Weights(weights_function)
weights_dict = torch.nn.ModuleDict(weights_dict)
super().__init__(
models=[model, weights_dict],
problem=problem,
optimizers=[optimizer_model, optimizer_weights],
optimizers_kwargs=[
optimizer_model_kwargs,
optimizer_weights_kwargs
optimizer_weights_kwargs,
],
extra_features=extra_features,
loss=loss
loss=loss,
)
# set automatic optimization
@@ -191,12 +190,8 @@ class SAPINN(PINNInterface):
# assign schedulers
self._schedulers = [
scheduler_model(
self.optimizers[0], **scheduler_model_kwargs
),
scheduler_weights(
self.optimizers[1], **scheduler_weights_kwargs
),
scheduler_model(self.optimizers[0], **scheduler_model_kwargs),
scheduler_weights(self.optimizers[1], **scheduler_weights_kwargs),
]
self._model = self.models[0]
@@ -327,7 +322,9 @@ class SAPINN(PINNInterface):
:rtype: Any
"""
# increase by one the counter of optimization to save loggers
self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed += 1
self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed += (
1
)
return super().on_train_batch_end(outputs, batch, batch_idx)
def on_train_start(self):
@@ -343,9 +340,8 @@ class SAPINN(PINNInterface):
self.trainer._accelerator_connector._accelerator_flag
)
for condition_name, tensor in self.problem.input_pts.items():
self.weights_dict.torchmodel[condition_name].sa_weights.data = torch.rand(
(tensor.shape[0], 1),
device = device
self.weights_dict.torchmodel[condition_name].sa_weights.data = (
torch.rand((tensor.shape[0], 1), device=device)
)
return super().on_train_start()
@@ -358,8 +354,8 @@ class SAPINN(PINNInterface):
:param dict checkpoint: Pytorch Lightning checkpoint dict.
"""
for condition_name, tensor in self.problem.input_pts.items():
self.weights_dict.torchmodel[condition_name].sa_weights.data = torch.rand(
(tensor.shape[0], 1)
self.weights_dict.torchmodel[condition_name].sa_weights.data = (
torch.rand((tensor.shape[0], 1))
)
return super().on_load_checkpoint(checkpoint)
@@ -403,12 +399,14 @@ class SAPINN(PINNInterface):
:rtype List[LabelTensor, LabelTensor]
"""
weights = self.weights_dict.torchmodel[
self.current_condition_name].forward()
loss_value = self._vectorial_loss(torch.zeros_like(
residual, requires_grad=True), residual)
self.current_condition_name
].forward()
loss_value = self._vectorial_loss(
torch.zeros_like(residual, requires_grad=True), residual
)
return (
self._vect_to_scalar(weights * loss_value),
self._vect_to_scalar(loss_value)
self._vect_to_scalar(loss_value),
)
def _vect_to_scalar(self, loss_value):
@@ -426,11 +424,12 @@ class SAPINN(PINNInterface):
elif self.loss.reduction == "sum":
ret = torch.sum(loss_value)
else:
raise RuntimeError(f"Invalid reduction, got {self.loss.reduction} "
"but expected mean or sum.")
raise RuntimeError(
f"Invalid reduction, got {self.loss.reduction} "
"but expected mean or sum."
)
return ret
@property
def neural_net(self):
"""

49
pina/solvers/rom.py
View File

@@ -4,6 +4,7 @@ import torch
from pina.solvers import SupervisedSolver
class ReducedOrderModelSolver(SupervisedSolver):
r"""
ReducedOrderModelSolver solver class. This class implements a
@@ -114,9 +115,12 @@ class ReducedOrderModelSolver(SupervisedSolver):
rate scheduler.
:param dict scheduler_kwargs: LR scheduler constructor keyword args.
"""
model = torch.nn.ModuleDict({
'reduction_network' : reduction_network,
'interpolation_network' : interpolation_network})
model = torch.nn.ModuleDict(
{
"reduction_network": reduction_network,
"interpolation_network": interpolation_network,
}
)
super().__init__(
model=model,
@@ -125,18 +129,22 @@ class ReducedOrderModelSolver(SupervisedSolver):
optimizer=optimizer,
optimizer_kwargs=optimizer_kwargs,
scheduler=scheduler,
scheduler_kwargs=scheduler_kwargs
scheduler_kwargs=scheduler_kwargs,
)
# assert reduction object contains encode/ decode
if not hasattr(self.neural_net['reduction_network'], 'encode'):
raise SyntaxError('reduction_network must have encode method. '
'The encode method should return a lower '
'dimensional representation of the input.')
if not hasattr(self.neural_net['reduction_network'], 'decode'):
raise SyntaxError('reduction_network must have decode method. '
'The decode method should return a high '
'dimensional representation of the encoding.')
if not hasattr(self.neural_net["reduction_network"], "encode"):
raise SyntaxError(
"reduction_network must have encode method. "
"The encode method should return a lower "
"dimensional representation of the input."
)
if not hasattr(self.neural_net["reduction_network"], "decode"):
raise SyntaxError(
"reduction_network must have decode method. "
"The decode method should return a high "
"dimensional representation of the encoding."
)
def forward(self, x):
"""
@@ -149,8 +157,8 @@ class ReducedOrderModelSolver(SupervisedSolver):
:return: Solver solution.
:rtype: torch.Tensor
"""
reduction_network = self.neural_net['reduction_network']
interpolation_network = self.neural_net['interpolation_network']
reduction_network = self.neural_net["reduction_network"]
interpolation_network = self.neural_net["interpolation_network"]
return reduction_network.decode(interpolation_network(x))
def loss_data(self, input_pts, output_pts):
@@ -167,17 +175,18 @@ class ReducedOrderModelSolver(SupervisedSolver):
:rtype: torch.Tensor
"""
# extract networks
reduction_network = self.neural_net['reduction_network']
interpolation_network = self.neural_net['interpolation_network']
reduction_network = self.neural_net["reduction_network"]
interpolation_network = self.neural_net["interpolation_network"]
# encoded representations loss
encode_repr_inter_net = interpolation_network(input_pts)
encode_repr_reduction_network = reduction_network.encode(output_pts)
loss_encode = self.loss(encode_repr_inter_net,
encode_repr_reduction_network)
loss_encode = self.loss(
encode_repr_inter_net, encode_repr_reduction_network
)
# reconstruction loss
loss_reconstruction = self.loss(
reduction_network.decode(encode_repr_reduction_network),
output_pts)
reduction_network.decode(encode_repr_reduction_network), output_pts
)
return loss_encode + loss_reconstruction

6
pina/trainer.py
View File

@@ -67,9 +67,9 @@ class Trainer(pytorch_lightning.Trainer):
pb = self._model.problem
if hasattr(pb, "unknown_parameters"):
for key in pb.unknown_parameters:
pb.unknown_parameters[key] = torch.nn.Parameter(pb.unknown_parameters[key].data.to(device))
pb.unknown_parameters[key] = torch.nn.Parameter(
pb.unknown_parameters[key].data.to(device)
)
def train(self, **kwargs):
"""