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Refactoring code

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2022-01-27 14:55:42 +01:00
parent fb16fc7f3a
commit fa8ffd5042
32 changed files with 417 additions and 442 deletions
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69
examples/run_burgers.py
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@@ -9,34 +9,20 @@ from torch.nn import ReLU, Tanh, Softplus
from problems.burgers import Burgers1D
from pina.deep_feed_forward import DeepFeedForward
from pina.adaptive_functions import AdaptiveSin, AdaptiveCos, AdaptiveTanh
from pina import Plotter
class myFeature(torch.nn.Module):
"""
Feature: sin(x)
Feature: sin(pi*x)
"""
def __init__(self, idx):
super(myFeature, self).__init__()
self.idx = idx
def forward(self, x):
return torch.sin(torch.pi * x[:, self.idx])
class myExp(torch.nn.Module):
def __init__(self, idx):
super().__init__()
self.idx = idx
def forward(self, x):
return torch.exp(x[:, self.idx])
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Run PINA")
@@ -51,13 +37,13 @@ if __name__ == "__main__":
burgers_problem = Burgers1D()
model = DeepFeedForward(
layers=[20, 10, 5],
#layers=[8, 4, 2],
layers=[30, 20, 10, 5],
#layers=[8, 8, 8],
#layers=[16, 8, 4, 4],
#layers=[20, 4, 4, 4],
output_variables=burgers_problem.output_variables,
input_variables=burgers_problem.input_variables,
func=Tanh,
func=Softplus,
extra_features=feat
)
@@ -70,46 +56,11 @@ if __name__ == "__main__":
lr_accelerate=None)
if args.s:
pinn.span_pts(8000, 'latin', ['D'])
pinn.span_pts(50, 'random', ['gamma1', 'gamma2', 'initia'])
#pinn.plot_pts()
pinn.train(10000, 1000)
#with open('burgers_history_{}_{}.txt'.format(args.id_run, args.features), 'w') as file_:
# for i, losses in enumerate(pinn.history):
# file_.write('{} {}\n'.format(i, sum(losses).item()))
pinn.span_pts(2000, 'latin', ['D'])
pinn.span_pts(150, 'random', ['gamma1', 'gamma2', 'initia'])
pinn.train(5000, 100)
pinn.save_state('pina.burger.{}.{}'.format(args.id_run, args.features))
else:
pinn.load_state('pina.burger.{}.{}'.format(args.id_run, args.features))
#pinn.plot(256,filename='pina.burger.{}.{}.jpg'.format(args.id_run, args.features))
print(pinn.history)
with open('burgers_history_{}_{}.txt'.format(args.id_run, args.features), 'w') as file_:
for i, losses in enumerate(pinn.history):
print(losses)
file_.write('{} {}\n'.format(i, sum(losses)))
import scipy
data = scipy.io.loadmat('Data/burgers_shock.mat')
data_solution = {'grid': np.meshgrid(data['x'], data['t']), 'grid_solution': data['usol'].T}
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt
t =75
for t in [25, 50, 75]:
input = torch.cat([
torch.linspace(-1, 1, 256).reshape(-1, 1),
torch.ones(size=(256, 1)) * t /100],
axis=1).double()
output = pinn.model(input)
fout = 'pina.burgers.{}.{}.t{}.dat'.format(args.id_run, args.features, t)
with open(fout, 'w') as f_:
f_.write('x utruth upinn\n')
for x, utruth, upinn in zip(data['x'], data['usol'][:, t], output.tensor.detach()):
f_.write('{} {} {}\n'.format(x[0], utruth, upinn.item()))
plt.plot(data['usol'][:, t], label='truth')
plt.plot(output.tensor.detach(), 'x', label='pinn')
plt.legend()
plt.show()
plotter = Plotter()
plotter.plot(pinn)