be11110bb2
* fnn update, remove labeltensors * allow custom torch models * updating tests --------- Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local> Co-authored-by: Dario Coscia <dariocoscia@dhcp-031.eduroam.sissa.it>
32 lines
1.1 KiB
Python
32 lines
1.1 KiB
Python
import pytest
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import torch
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from pina import LabelTensor
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from pina.model import DeepONet
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from pina.model import FeedForward as FFN
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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']
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input_ = LabelTensor(data, input_vars)
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# TODO
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# def test_constructor():
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# branch = FFN(input_variables=['a', 'c'], output_variables=20)
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# trunk = FFN(input_variables=['b'], output_variables=20)
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# onet = DeepONet(nets=[trunk, branch], output_variables=output_vars)
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# def test_constructor_fails_when_invalid_inner_layer_size():
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# branch = FFN(input_variables=['a', 'c'], output_variables=20)
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# trunk = FFN(input_variables=['b'], output_variables=19)
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# with pytest.raises(ValueError):
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# DeepONet(nets=[trunk, branch], output_variables=output_vars)
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# def test_forward():
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# branch = FFN(input_variables=['a', 'c'], output_variables=10)
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# trunk = FFN(input_variables=['b'], output_variables=10)
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# onet = DeepONet(nets=[trunk, branch], output_variables=output_vars)
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# output_ = onet(input_)
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# assert output_.labels == output_vars
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