0e3625de80
* equation class * difference domain * dummy dataloader * writer class * refactoring and minor fix
247 lines
7.6 KiB
Python
247 lines
7.6 KiB
Python
import torch
|
|
import pytest
|
|
|
|
from pina import LabelTensor, Condition, CartesianDomain, PINN
|
|
from pina.problem import SpatialProblem
|
|
from pina.model import FeedForward
|
|
from pina.operators import nabla
|
|
from pina.equation.equation import Equation
|
|
from pina.equation.equation_factory import FixedValue
|
|
|
|
|
|
in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
|
|
out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
|
|
|
|
def laplace_equation(input_, output_):
|
|
force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
|
|
torch.sin(input_.extract(['y'])*torch.pi))
|
|
nabla_u = nabla(output_.extract(['u']), input_)
|
|
return nabla_u - force_term
|
|
|
|
my_laplace = Equation(laplace_equation)
|
|
|
|
class Poisson(SpatialProblem):
|
|
output_variables = ['u']
|
|
spatial_domain = CartesianDomain({'x': [0, 1], 'y': [0, 1]})
|
|
|
|
conditions = {
|
|
'gamma1': Condition(
|
|
location=CartesianDomain({'x': [0, 1], 'y': 1}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma2': Condition(
|
|
location=CartesianDomain({'x': [0, 1], 'y': 0}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma3': Condition(
|
|
location=CartesianDomain({'x': 1, 'y': [0, 1]}),
|
|
equation=FixedValue(0.0)),
|
|
'gamma4': Condition(
|
|
location=CartesianDomain({'x': 0, 'y': [0, 1]}),
|
|
equation=FixedValue(0.0)),
|
|
'D': Condition(
|
|
location=CartesianDomain({'x': [0, 1], 'y': [0, 1]}),
|
|
equation=my_laplace),
|
|
'data': Condition(
|
|
input_points=in_,
|
|
output_points=out_)
|
|
}
|
|
|
|
def poisson_sol(self, pts):
|
|
return -(
|
|
torch.sin(pts.extract(['x'])*torch.pi) *
|
|
torch.sin(pts.extract(['y'])*torch.pi)
|
|
)/(2*torch.pi**2)
|
|
|
|
truth_solution = poisson_sol
|
|
|
|
|
|
class myFeature(torch.nn.Module):
|
|
"""
|
|
Feature: sin(x)
|
|
"""
|
|
|
|
def __init__(self):
|
|
super(myFeature, self).__init__()
|
|
|
|
def forward(self, x):
|
|
t = (torch.sin(x.extract(['x'])*torch.pi) *
|
|
torch.sin(x.extract(['y'])*torch.pi))
|
|
return LabelTensor(t, ['sin(x)sin(y)'])
|
|
|
|
|
|
problem = Poisson()
|
|
model = FeedForward(len(problem.input_variables),len(problem.output_variables))
|
|
model_extra_feat = FeedForward(len(problem.input_variables) + 1,len(problem.output_variables))
|
|
|
|
|
|
def test_constructor():
|
|
PINN(problem, model)
|
|
|
|
|
|
def test_constructor_extra_feats():
|
|
PINN(problem, model_extra_feat, [myFeature()])
|
|
|
|
|
|
def test_span_pts():
|
|
pinn = PINN(problem, model)
|
|
n = 10
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
for b in boundaries:
|
|
assert pinn.input_pts[b].shape[0] == n
|
|
pinn.span_pts(n, 'random', locations=boundaries)
|
|
for b in boundaries:
|
|
assert pinn.input_pts[b].shape[0] == n
|
|
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
assert pinn.input_pts['D'].shape[0] == n**2
|
|
pinn.span_pts(n, 'random', locations=['D'])
|
|
assert pinn.input_pts['D'].shape[0] == n
|
|
|
|
pinn.span_pts(n, 'latin', locations=['D'])
|
|
assert pinn.input_pts['D'].shape[0] == n
|
|
|
|
pinn.span_pts(n, 'lh', locations=['D'])
|
|
assert pinn.input_pts['D'].shape[0] == n
|
|
|
|
|
|
def test_sampling_all_args():
|
|
pinn = PINN(problem, model)
|
|
n = 10
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
|
|
|
|
def test_sampling_all_kwargs():
|
|
pinn = PINN(problem, model)
|
|
n = 10
|
|
pinn.span_pts(n=n, mode='latin', locations=['D'])
|
|
|
|
|
|
def test_sampling_dict():
|
|
pinn = PINN(problem, model)
|
|
n = 10
|
|
pinn.span_pts(
|
|
{'variables': ['x', 'y'], 'mode': 'grid', 'n': n}, locations=['D'])
|
|
|
|
|
|
def test_sampling_mixed_args_kwargs():
|
|
pinn = PINN(problem, model)
|
|
n = 10
|
|
with pytest.raises(ValueError):
|
|
pinn.span_pts(n, mode='latin', locations=['D'])
|
|
|
|
|
|
def test_train():
|
|
pinn = PINN(problem, model)
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(5)
|
|
|
|
"""
|
|
def test_train_2():
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
expected_keys = [[], list(range(0, 50, 3))]
|
|
param = [0, 3]
|
|
for i, truth_key in zip(param, expected_keys):
|
|
pinn = PINN(problem, model)
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(50, save_loss=i)
|
|
assert list(pinn.history_loss.keys()) == truth_key
|
|
|
|
|
|
def test_train_extra_feats():
|
|
pinn = PINN(problem, model_extra_feat, [myFeature()])
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(5)
|
|
|
|
|
|
def test_train_2_extra_feats():
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
expected_keys = [[], list(range(0, 50, 3))]
|
|
param = [0, 3]
|
|
for i, truth_key in zip(param, expected_keys):
|
|
pinn = PINN(problem, model_extra_feat, [myFeature()])
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(50, save_loss=i)
|
|
assert list(pinn.history_loss.keys()) == truth_key
|
|
|
|
|
|
def test_train_with_optimizer_kwargs():
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
expected_keys = [[], list(range(0, 50, 3))]
|
|
param = [0, 3]
|
|
for i, truth_key in zip(param, expected_keys):
|
|
pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3})
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(50, save_loss=i)
|
|
assert list(pinn.history_loss.keys()) == truth_key
|
|
|
|
|
|
def test_train_with_lr_scheduler():
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 10
|
|
expected_keys = [[], list(range(0, 50, 3))]
|
|
param = [0, 3]
|
|
for i, truth_key in zip(param, expected_keys):
|
|
pinn = PINN(
|
|
problem,
|
|
model,
|
|
lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR,
|
|
lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False}
|
|
)
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(50, save_loss=i)
|
|
assert list(pinn.history_loss.keys()) == truth_key
|
|
|
|
|
|
# def test_train_batch():
|
|
# pinn = PINN(problem, model, batch_size=6)
|
|
# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
# n = 10
|
|
# pinn.span_pts(n, 'grid', locations=boundaries)
|
|
# pinn.span_pts(n, 'grid', locations=['D'])
|
|
# pinn.train(5)
|
|
|
|
|
|
# def test_train_batch_2():
|
|
# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
# n = 10
|
|
# expected_keys = [[], list(range(0, 50, 3))]
|
|
# param = [0, 3]
|
|
# for i, truth_key in zip(param, expected_keys):
|
|
# pinn = PINN(problem, model, batch_size=6)
|
|
# pinn.span_pts(n, 'grid', locations=boundaries)
|
|
# pinn.span_pts(n, 'grid', locations=['D'])
|
|
# pinn.train(50, save_loss=i)
|
|
# assert list(pinn.history_loss.keys()) == truth_key
|
|
|
|
|
|
if torch.cuda.is_available():
|
|
|
|
# def test_gpu_train():
|
|
# pinn = PINN(problem, model, batch_size=20, device='cuda')
|
|
# boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
# n = 100
|
|
# pinn.span_pts(n, 'grid', locations=boundaries)
|
|
# pinn.span_pts(n, 'grid', locations=['D'])
|
|
# pinn.train(5)
|
|
|
|
def test_gpu_train_nobatch():
|
|
pinn = PINN(problem, model, batch_size=None, device='cuda')
|
|
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
|
|
n = 100
|
|
pinn.span_pts(n, 'grid', locations=boundaries)
|
|
pinn.span_pts(n, 'grid', locations=['D'])
|
|
pinn.train(5)
|
|
""" |