85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
from pina.model.layers import SpectralConvBlock1D, SpectralConvBlock2D, SpectralConvBlock3D
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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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SpectralConvBlock1D(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 = SpectralConvBlock1D(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 = SpectralConvBlock1D(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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SpectralConvBlock2D(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 = SpectralConvBlock2D(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 = SpectralConvBlock2D(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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SpectralConvBlock3D(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 = SpectralConvBlock3D(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 = SpectralConvBlock3D(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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