59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import torch
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import pytest
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from pina import LabelTensor
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from pina.model import FeedForward
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class myFeature(torch.nn.Module):
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"""
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Feature: sin(pi*x)
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"""
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def __init__(self):
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super(myFeature, self).__init__()
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def forward(self, x):
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return LabelTensor(torch.sin(torch.pi * x.extract('a')), 'sin(a)')
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data = torch.rand((20, 3))
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input_vars = ['a', 'b', 'c']
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output_vars = ['d', 'e']
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input_ = LabelTensor(data, input_vars)
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def test_constructor():
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FeedForward(input_vars, output_vars)
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FeedForward(3, 4)
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FeedForward(input_vars, output_vars, extra_features=[myFeature()])
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FeedForward(input_vars, output_vars, inner_size=10, n_layers=20)
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FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2])
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FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2],
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func=torch.nn.ReLU)
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FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2],
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func=[torch.nn.ReLU, torch.nn.ReLU, None, torch.nn.Tanh])
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def test_constructor_wrong():
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with pytest.raises(RuntimeError):
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FeedForward(input_vars, output_vars, layers=[10, 20, 5, 2],
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func=[torch.nn.ReLU, torch.nn.ReLU])
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def test_forward():
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fnn = FeedForward(input_vars, output_vars)
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output_ = fnn(input_)
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assert output_.labels == output_vars
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def test_forward2():
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dim_in, dim_out = 3, 2
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fnn = FeedForward(dim_in, dim_out)
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output_ = fnn(input_)
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assert output_.shape == (input_.shape[0], dim_out)
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def test_forward_features():
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fnn = FeedForward(input_vars, output_vars, extra_features=[myFeature()])
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output_ = fnn(input_)
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assert output_.labels == output_vars
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