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gpu input data support (#73)

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This commit is contained in:
Nicola Demo
2023-03-10 13:57:31 +01:00
committed by GitHub
parent c5b2596910
commit 465718aead
2 changed files with 37 additions and 34 deletions
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2
pina/pinn.py
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@@ -241,7 +241,7 @@ class PINN(object):
pts = condition.input_points.to( pts = condition.input_points.to(
dtype=self.dtype, device=self.device) dtype=self.dtype, device=self.device)
predicted = self.model(pts) predicted = self.model(pts)
residuals = predicted - condition.output_points residuals = predicted - condition.output_points.to(device=self.device, dtype=self.dtype) # TODO fix
local_loss = ( local_loss = (
condition.data_weight*self._compute_norm(residuals)) condition.data_weight*self._compute_norm(residuals))
single_loss.append(local_loss) single_loss.append(local_loss)

69
tests/test_pinn.py
View File

@@ -6,6 +6,8 @@ from pina.problem import SpatialProblem
from pina.model import FeedForward from pina.model import FeedForward
from pina.operators import nabla from pina.operators import nabla
in_ = LabelTensor(torch.tensor([[0., 1.]]), ['x', 'y'])
out_ = LabelTensor(torch.tensor([[0.]]), ['u'])
class Poisson(SpatialProblem): class Poisson(SpatialProblem):
output_variables = ['u'] output_variables = ['u']
@@ -27,6 +29,7 @@ class Poisson(SpatialProblem):
'gamma3': Condition(Span({'x': 1, 'y': [0, 1]}), nil_dirichlet), 'gamma3': Condition(Span({'x': 1, 'y': [0, 1]}), nil_dirichlet),
'gamma4': Condition(Span({'x': 0, 'y': [0, 1]}), nil_dirichlet), 'gamma4': Condition(Span({'x': 0, 'y': [0, 1]}), nil_dirichlet),
'D': Condition(Span({'x': [0, 1], 'y': [0, 1]}), laplace_equation), 'D': Condition(Span({'x': [0, 1], 'y': [0, 1]}), laplace_equation),
'data': Condition(in_, out_)
} }
def poisson_sol(self, pts): def poisson_sol(self, pts):
@@ -51,10 +54,10 @@ def test_span_pts():
pinn = PINN(problem, model) pinn = PINN(problem, model)
n = 10 n = 10
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
pinn.span_pts(n, 'grid', boundaries) pinn.span_pts(n, 'grid', locations=boundaries)
for b in boundaries: for b in boundaries:
assert pinn.input_pts[b].shape[0] == n assert pinn.input_pts[b].shape[0] == n
pinn.span_pts(n, 'random', boundaries) pinn.span_pts(n, 'random', locations=boundaries)
for b in boundaries: for b in boundaries:
assert pinn.input_pts[b].shape[0] == n assert pinn.input_pts[b].shape[0] == n
@@ -100,19 +103,19 @@ def test_train():
pinn = PINN(problem, model) pinn = PINN(problem, model)
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10 n = 10
pinn.span_pts(n, 'grid', boundaries) pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(5) pinn.train(5)
def test_train(): def test_train_2():
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10 n = 10
expected_keys = [[], list(range(0, 50, 3))] expected_keys = [[], list(range(0, 50, 3))]
param = [0, 3] param = [0, 3]
for i, truth_key in zip(param, expected_keys): for i, truth_key in zip(param, expected_keys):
pinn = PINN(problem, model) pinn = PINN(problem, model)
pinn.span_pts(n, 'grid', boundaries) pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(50, save_loss=i) pinn.train(50, save_loss=i)
assert list(pinn.history_loss.keys()) == truth_key assert list(pinn.history_loss.keys()) == truth_key
@@ -125,7 +128,7 @@ def test_train_with_optimizer_kwargs():
param = [0, 3] param = [0, 3]
for i, truth_key in zip(param, expected_keys): for i, truth_key in zip(param, expected_keys):
pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3}) pinn = PINN(problem, model, optimizer_kwargs={'lr' : 0.3})
pinn.span_pts(n, 'grid', boundaries) pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(50, save_loss=i) pinn.train(50, save_loss=i)
assert list(pinn.history_loss.keys()) == truth_key assert list(pinn.history_loss.keys()) == truth_key
@@ -143,48 +146,48 @@ def test_train_with_lr_scheduler():
lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR, lr_scheduler_type=torch.optim.lr_scheduler.CyclicLR,
lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False} lr_scheduler_kwargs={'base_lr' : 0.1, 'max_lr' : 0.3, 'cycle_momentum': False}
) )
pinn.span_pts(n, 'grid', boundaries) pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(50, save_loss=i) pinn.train(50, save_loss=i)
assert list(pinn.history_loss.keys()) == truth_key assert list(pinn.history_loss.keys()) == truth_key
def test_train_batch(): # def test_train_batch():
pinn = PINN(problem, model, batch_size=6) # pinn = PINN(problem, model, batch_size=6)
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10 # n = 10
pinn.span_pts(n, 'grid', boundaries) # pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) # pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(5) # pinn.train(5)
def test_train_batch(): # def test_train_batch_2():
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 10 # n = 10
expected_keys = [[], list(range(0, 50, 3))] # expected_keys = [[], list(range(0, 50, 3))]
param = [0, 3] # param = [0, 3]
for i, truth_key in zip(param, expected_keys): # for i, truth_key in zip(param, expected_keys):
pinn = PINN(problem, model, batch_size=6) # pinn = PINN(problem, model, batch_size=6)
pinn.span_pts(n, 'grid', boundaries) # pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) # pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(50, save_loss=i) # pinn.train(50, save_loss=i)
assert list(pinn.history_loss.keys()) == truth_key # assert list(pinn.history_loss.keys()) == truth_key
if torch.cuda.is_available(): if torch.cuda.is_available():
def test_gpu_train(): # def test_gpu_train():
pinn = PINN(problem, model, batch_size=20, device='cuda') # pinn = PINN(problem, model, batch_size=20, device='cuda')
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] # boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 100 # n = 100
pinn.span_pts(n, 'grid', boundaries) # pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) # pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(5) # pinn.train(5)
def test_gpu_train_nobatch(): def test_gpu_train_nobatch():
pinn = PINN(problem, model, batch_size=None, device='cuda') pinn = PINN(problem, model, batch_size=None, device='cuda')
boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4'] boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
n = 100 n = 100
pinn.span_pts(n, 'grid', boundaries) pinn.span_pts(n, 'grid', locations=boundaries)
pinn.span_pts(n, 'grid', locations=['D']) pinn.span_pts(n, 'grid', locations=['D'])
pinn.train(5) pinn.train(5)