128 lines
3.9 KiB
Python
128 lines
3.9 KiB
Python
import torch
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import pytest
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from pina import LabelTensor, Condition, Span, PINN
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from pina.problem import SpatialProblem
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from pina.model import FeedForward
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from pina.operators import nabla
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class Poisson(SpatialProblem):
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output_variables = ['u']
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spatial_domain = Span({'x': [0, 1], 'y': [0, 1]})
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def laplace_equation(input_, output_):
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force_term = (torch.sin(input_.extract(['x'])*torch.pi) *
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torch.sin(input_.extract(['y'])*torch.pi))
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nabla_u = nabla(output_, input_, components=['u'], d=['x', 'y'])
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return nabla_u - force_term
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def nil_dirichlet(input_, output_):
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value = 0.0
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return output_.extract(['u']) - value
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conditions = {
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'gamma1': Condition(Span({'x': [0, 1], 'y': 1}), nil_dirichlet),
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'gamma2': Condition(Span({'x': [0, 1], 'y': 0}), nil_dirichlet),
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'gamma3': Condition(Span({'x': 1, 'y': [0, 1]}), nil_dirichlet),
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'gamma4': Condition(Span({'x': 0, 'y': [0, 1]}), nil_dirichlet),
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'D': Condition(Span({'x': [0, 1], 'y': [0, 1]}), laplace_equation),
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}
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def poisson_sol(self, pts):
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return -(
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torch.sin(pts.extract(['x'])*torch.pi) *
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torch.sin(pts.extract(['y'])*torch.pi)
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)/(2*torch.pi**2)
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truth_solution = poisson_sol
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problem = Poisson()
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model = FeedForward(problem.input_variables, problem.output_variables)
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def test_constructor():
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PINN(problem, model)
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def test_span_pts():
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pinn = PINN(problem, model)
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n = 10
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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pinn.span_pts(n, 'grid', boundaries)
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for b in boundaries:
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assert pinn.input_pts[b].shape[0] == n
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pinn.span_pts(n, 'random', boundaries)
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for b in boundaries:
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assert pinn.input_pts[b].shape[0] == n
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pinn.span_pts(n, 'grid', locations=['D'])
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assert pinn.input_pts['D'].shape[0] == n**2
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pinn.span_pts(n, 'random', locations=['D'])
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assert pinn.input_pts['D'].shape[0] == n
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def test_train():
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pinn = PINN(problem, model)
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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pinn.span_pts(n, 'grid', boundaries)
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pinn.span_pts(n, 'grid', locations=['D'])
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pinn.train(5)
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def test_train():
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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expected_keys = [[], list(range(0, 50, 3))]
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param = [0, 3]
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for i, truth_key in zip(param, expected_keys):
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pinn = PINN(problem, model)
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pinn.span_pts(n, 'grid', boundaries)
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pinn.span_pts(n, 'grid', locations=['D'])
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pinn.train(50, save_loss=i)
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assert list(pinn.history_loss.keys()) == truth_key
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def test_train_batch():
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pinn = PINN(problem, model, batch_size=6)
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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pinn.span_pts(n, 'grid', boundaries)
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pinn.span_pts(n, 'grid', locations=['D'])
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pinn.train(5)
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def test_train_batch():
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 10
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expected_keys = [[], list(range(0, 50, 3))]
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param = [0, 3]
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for i, truth_key in zip(param, expected_keys):
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pinn = PINN(problem, model, batch_size=6)
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pinn.span_pts(n, 'grid', boundaries)
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pinn.span_pts(n, 'grid', locations=['D'])
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pinn.train(50, save_loss=i)
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assert list(pinn.history_loss.keys()) == truth_key
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if torch.cuda.is_available():
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def test_gpu_train():
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pinn = PINN(problem, model, batch_size=20, device='cuda')
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 100
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pinn.span_pts(n, 'grid', boundaries)
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pinn.span_pts(n, 'grid', locations=['D'])
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pinn.train(5)
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def test_gpu_train_nobatch():
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pinn = PINN(problem, model, batch_size=None, device='cuda')
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boundaries = ['gamma1', 'gamma2', 'gamma3', 'gamma4']
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n = 100
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pinn.span_pts(n, 'grid', boundaries)
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pinn.span_pts(n, 'grid', locations=['D'])
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pinn.train(5)
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