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2023-04-18 10:49:57 +02:00
parent da33aeae3a
commit 736c78fd64
17 changed files with 292 additions and 172 deletions
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68
pina/pinn.py
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@@ -88,10 +88,13 @@ class PINN(object):
@property
def problem(self):
""" The problem formulation."""
return self._problem
@problem.setter
def problem(self, problem):
"""
Set the problem formulation."""
if not isinstance(problem, AbstractProblem):
raise TypeError
self._problem = problem
@@ -99,11 +102,11 @@ class PINN(object):
def _compute_norm(self, vec):
"""
Compute the norm of the `vec` one-dimensional tensor based on the
`self.error_norm` attribute.
`self.error_norm` attribute.
.. todo: complete
:param vec torch.tensor: the tensor
:param torch.Tensor vec: the tensor
"""
if isinstance(self.error_norm, int):
return torch.linalg.vector_norm(vec, ord=self.error_norm, dtype=self.dytpe)
@@ -115,7 +118,11 @@ class PINN(object):
raise RuntimeError
def save_state(self, filename):
"""
Save the state of the model.
:param str filename: the filename to save the state to.
"""
checkpoint = {
'epoch': self.trained_epoch,
'model_state': self.model.state_dict(),
@@ -133,6 +140,11 @@ class PINN(object):
torch.save(checkpoint, filename)
def load_state(self, filename):
"""
Load the state of the model.
:param str filename: the filename to load the state from.
"""
checkpoint = torch.load(filename)
self.model.load_state_dict(checkpoint['model_state'])
@@ -298,32 +310,32 @@ class PINN(object):
return sum(losses).item()
def error(self, dtype='l2', res=100):
# def error(self, dtype='l2', res=100):
import numpy as np
if hasattr(self.problem, 'truth_solution') and self.problem.truth_solution is not None:
pts_container = []
for mn, mx in self.problem.domain_bound:
pts_container.append(np.linspace(mn, mx, res))
grids_container = np.meshgrid(*pts_container)
Z_true = self.problem.truth_solution(*grids_container)
# import numpy as np
# if hasattr(self.problem, 'truth_solution') and self.problem.truth_solution is not None:
# pts_container = []
# for mn, mx in self.problem.domain_bound:
# pts_container.append(np.linspace(mn, mx, res))
# grids_container = np.meshgrid(*pts_container)
# Z_true = self.problem.truth_solution(*grids_container)
elif hasattr(self.problem, 'data_solution') and self.problem.data_solution is not None:
grids_container = self.problem.data_solution['grid']
Z_true = self.problem.data_solution['grid_solution']
try:
unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T.to(
dtype=self.dtype, device=self.device)
Z_pred = self.model(unrolled_pts)
Z_pred = Z_pred.detach().numpy().reshape(grids_container[0].shape)
# elif hasattr(self.problem, 'data_solution') and self.problem.data_solution is not None:
# grids_container = self.problem.data_solution['grid']
# Z_true = self.problem.data_solution['grid_solution']
# try:
# unrolled_pts = torch.tensor([t.flatten() for t in grids_container]).T.to(
# dtype=self.dtype, device=self.device)
# Z_pred = self.model(unrolled_pts)
# Z_pred = Z_pred.detach().numpy().reshape(grids_container[0].shape)
if dtype == 'l2':
return np.linalg.norm(Z_pred - Z_true)/np.linalg.norm(Z_true)
else:
# TODO H1
pass
except:
print("")
print("Something went wrong...")
print(
"Not able to compute the error. Please pass a data solution or a true solution")
# if dtype == 'l2':
# return np.linalg.norm(Z_pred - Z_true)/np.linalg.norm(Z_true)
# else:
# # TODO H1
# pass
# except:
# print("")
# print("Something went wrong...")
# print(
# "Not able to compute the error. Please pass a data solution or a true solution")