df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
85 lines
2.6 KiB
Python
85 lines
2.6 KiB
Python
from pina.model.block import FourierBlock1D, FourierBlock2D, FourierBlock3D
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import torch
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input_numb_fields = 3
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output_numb_fields = 4
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batch = 5
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def test_constructor_1d():
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FourierBlock1D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=5)
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def test_forward_1d():
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sconv = FourierBlock1D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=4)
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x = torch.rand(batch, input_numb_fields, 10)
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sconv(x)
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def test_backward_1d():
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sconv = FourierBlock1D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=4)
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x = torch.rand(batch, input_numb_fields, 10)
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x.requires_grad = True
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sconv(x)
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l = torch.mean(sconv(x))
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l.backward()
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assert x._grad.shape == torch.Size([5, 3, 10])
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def test_constructor_2d():
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FourierBlock2D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=[5, 4])
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def test_forward_2d():
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sconv = FourierBlock2D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=[5, 4])
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x = torch.rand(batch, input_numb_fields, 10, 10)
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sconv(x)
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def test_backward_2d():
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sconv = FourierBlock2D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=[5, 4])
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x = torch.rand(batch, input_numb_fields, 10, 10)
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x.requires_grad = True
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sconv(x)
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l = torch.mean(sconv(x))
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l.backward()
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assert x._grad.shape == torch.Size([5, 3, 10, 10])
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def test_constructor_3d():
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FourierBlock3D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=[5, 4, 4])
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def test_forward_3d():
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sconv = FourierBlock3D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=[5, 4, 4])
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x = torch.rand(batch, input_numb_fields, 10, 10, 10)
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sconv(x)
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def test_backward_3d():
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sconv = FourierBlock3D(input_numb_fields=input_numb_fields,
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output_numb_fields=output_numb_fields,
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n_modes=[5, 4, 4])
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x = torch.rand(batch, input_numb_fields, 10, 10, 10)
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x.requires_grad = True
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sconv(x)
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l = torch.mean(sconv(x))
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l.backward()
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assert x._grad.shape == torch.Size([5, 3, 10, 10, 10])
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