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Codacy Small Bug Fixes:

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- cleaned up imports
- cleaned up some code
- added docstrings
This commit is contained in:
SpartaKushK
2023-07-25 16:43:45 +02:00
committed by Nicola Demo
parent bd88e24174
commit 625a77c0d5
13 changed files with 132 additions and 118 deletions
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135
examples/run_parametric_elliptic_optimal_control_alpha.py
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@@ -1,84 +1,87 @@
# import argparse
# import numpy as np
# import torch
# from torch.nn import Softplus
import argparse
import numpy as np
import torch
from torch.nn import Softplus
# from pina import PINN, LabelTensor, Plotter
# from pina.model import MultiFeedForward
# from problems.parametric_elliptic_optimal_control_alpha_variable import (
# ParametricEllipticOptimalControl)
from pina import PINN, LabelTensor, Plotter
from pina.model import MultiFeedForward
from problems.parametric_elliptic_optimal_control_alpha_variable import (
ParametricEllipticOptimalControl)
# class myFeature(torch.nn.Module):
# """
# Feature: sin(x)
# """
class myFeature(torch.nn.Module):
"""
Feature: sin(x)
"""
# def __init__(self):
# super(myFeature, self).__init__()
def __init__(self):
super(myFeature, self).__init__()
# def forward(self, x):
# t = (-x.extract(['x1'])**2+1) * (-x.extract(['x2'])**2+1)
# return LabelTensor(t, ['k0'])
def forward(self, x):
t = (-x.extract(['x1'])**2+1) * (-x.extract(['x2'])**2+1)
return LabelTensor(t, ['k0'])
# class CustomMultiDFF(MultiFeedForward):
class CustomMultiDFF(MultiFeedForward):
# def __init__(self, dff_dict):
# super().__init__(dff_dict)
def __init__(self, dff_dict):
super().__init__(dff_dict)
# def forward(self, x):
# out = self.uu(x)
# p = LabelTensor((out.extract(['u_param']) * x.extract(['alpha'])), ['p'])
# return out.append(p)
def forward(self, x):
out = self.uu(x)
p = LabelTensor(
(out.extract(['u_param']) * x.extract(['alpha'])), ['p'])
return out.append(p)
# if __name__ == "__main__":
if __name__ == "__main__":
# parser = argparse.ArgumentParser(description="Run PINA")
# group = parser.add_mutually_exclusive_group(required=True)
# group.add_argument("-s", "-save", action="store_true")
# group.add_argument("-l", "-load", action="store_true")
# args = parser.parse_args()
parser = argparse.ArgumentParser(description="Run PINA")
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument("-s", "-save", action="store_true")
group.add_argument("-l", "-load", action="store_true")
args = parser.parse_args()
# opc = ParametricEllipticOptimalControl()
# model = CustomMultiDFF(
# {
# 'uu': {
# 'input_variables': ['x1', 'x2', 'mu', 'alpha'],
# 'output_variables': ['u_param', 'y'],
# 'layers': [40, 40, 20],
# 'func': Softplus,
# 'extra_features': [myFeature()],
# },
# }
# )
opc = ParametricEllipticOptimalControl()
model = CustomMultiDFF(
{
'uu': {
'input_variables': ['x1', 'x2', 'mu', 'alpha'],
'output_variables': ['u_param', 'y'],
'layers': [40, 40, 20],
'func': Softplus,
'extra_features': [myFeature()],
},
}
)
# pinn = PINN(
# opc,
# model,
# lr=0.002,
# error_norm='mse',
# regularizer=1e-8)
pinn = PINN(
opc,
model,
lr=0.002,
error_norm='mse',
regularizer=1e-8)
# if args.s:
if args.s:
# pinn.span_pts(
# {'variables': ['x1', 'x2'], 'mode': 'random', 'n': 100},
# {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
# locations=['D'])
# pinn.span_pts(
# {'variables': ['x1', 'x2'], 'mode': 'grid', 'n': 20},
# {'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
# locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
pinn.span_pts(
{'variables': ['x1', 'x2'], 'mode': 'random', 'n': 100},
{'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
locations=['D'])
pinn.span_pts(
{'variables': ['x1', 'x2'], 'mode': 'grid', 'n': 20},
{'variables': ['mu', 'alpha'], 'mode': 'grid', 'n': 5},
locations=['gamma1', 'gamma2', 'gamma3', 'gamma4'])
# pinn.train(1000, 20)
# pinn.save_state('pina.ocp')
pinn.train(1000, 20)
pinn.save_state('pina.ocp')
# else:
# pinn.load_state('pina.ocp')
# plotter = Plotter()
# plotter.plot(pinn, components='y', fixed_variables={'alpha': 0.01, 'mu': 1.0})
# plotter.plot(pinn, components='u_param', fixed_variables={'alpha': 0.01, 'mu': 1.0})
# plotter.plot(pinn, components='p', fixed_variables={'alpha': 0.01, 'mu': 1.0})
raise NotImplementedError('not available problem at the moment...')
else:
pinn.load_state('pina.ocp')
plotter = Plotter()
plotter.plot(pinn, components='y',
fixed_variables={'alpha': 0.01, 'mu': 1.0})
plotter.plot(pinn, components='u_param',
fixed_variables={'alpha': 0.01, 'mu': 1.0})
plotter.plot(pinn, components='p', fixed_variables={
'alpha': 0.01, 'mu': 1.0})