df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
96 lines
3.7 KiB
Python
96 lines
3.7 KiB
Python
import torch
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import pytest
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from pina.model.block import PeriodicBoundaryEmbedding, FourierFeatureEmbedding
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# test tolerance
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tol = 1e-6
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def check_same_columns(tensor):
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# Get the first column and compute residual
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residual = tensor - tensor[0]
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zeros = torch.zeros_like(residual)
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# Compare each column with the first column
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all_same = torch.allclose(input=residual,other=zeros,atol=tol)
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return all_same
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def grad(u, x):
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"""
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Compute the first derivative of u with respect to x.
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"""
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return torch.autograd.grad(u, x, grad_outputs=torch.ones_like(u),
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create_graph=True, allow_unused=True,
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retain_graph=True)[0]
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def test_constructor_PeriodicBoundaryEmbedding():
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PeriodicBoundaryEmbedding(input_dimension=1, periods=2)
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PeriodicBoundaryEmbedding(input_dimension=1, periods={'x': 3, 'y' : 4})
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PeriodicBoundaryEmbedding(input_dimension=1, periods={0: 3, 1 : 4})
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PeriodicBoundaryEmbedding(input_dimension=1, periods=2, output_dimension=10)
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with pytest.raises(TypeError):
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PeriodicBoundaryEmbedding()
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with pytest.raises(ValueError):
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PeriodicBoundaryEmbedding(input_dimension=1., periods=1)
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PeriodicBoundaryEmbedding(input_dimension=1, periods=1,
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output_dimension=1.)
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PeriodicBoundaryEmbedding(input_dimension=1, periods={'x':'x'})
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PeriodicBoundaryEmbedding(input_dimension=1, periods={0:'x'})
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@pytest.mark.parametrize("period", [1, 4, 10])
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@pytest.mark.parametrize("input_dimension", [1, 2, 3])
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def test_forward_backward_same_period_PeriodicBoundaryEmbedding(input_dimension,
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period):
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func = torch.nn.Sequential(
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PeriodicBoundaryEmbedding(input_dimension=input_dimension,
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output_dimension=60, periods=period),
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torch.nn.Tanh(),
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torch.nn.Linear(60, 60),
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torch.nn.Tanh(),
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torch.nn.Linear(60, 1)
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)
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# coordinates
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x = period * torch.tensor([[0.],[1.]])
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if input_dimension == 2:
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x = torch.cartesian_prod(x.flatten(),x.flatten())
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elif input_dimension == 3:
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x = torch.cartesian_prod(x.flatten(),x.flatten(),x.flatten())
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x.requires_grad = True
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# output
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f = func(x)
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assert check_same_columns(f)
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# compute backward
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loss = f.mean()
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loss.backward()
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def test_constructor_FourierFeatureEmbedding():
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FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
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sigma=1)
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with pytest.raises(TypeError):
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FourierFeatureEmbedding()
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with pytest.raises(RuntimeError):
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FourierFeatureEmbedding(input_dimension=1, output_dimension=3, sigma=1)
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with pytest.raises(ValueError):
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FourierFeatureEmbedding(input_dimension='x', output_dimension=20,
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sigma=1)
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FourierFeatureEmbedding(input_dimension=1, output_dimension='x',
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sigma=1)
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FourierFeatureEmbedding(input_dimension=1, output_dimension=20,
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sigma='x')
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@pytest.mark.parametrize("output_dimension", [2, 4, 6])
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@pytest.mark.parametrize("input_dimension", [1, 2, 3])
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@pytest.mark.parametrize("sigma", [10, 1, 0.1])
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def test_forward_backward_FourierFeatureEmbedding(input_dimension,
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output_dimension,
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sigma):
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func = FourierFeatureEmbedding(input_dimension, output_dimension,
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sigma)
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# coordinates
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x = torch.rand((10, input_dimension), requires_grad=True)
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# output
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f = func(x)
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assert f.shape[-1] == output_dimension
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# compute backward
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loss = f.mean()
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loss.backward() |