58 lines
1.7 KiB
Python
58 lines
1.7 KiB
Python
import torch
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import pytest
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from pina.model.block import LowRankBlock
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from pina import LabelTensor
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input_dimensions=2
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embedding_dimenion=1
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rank=4
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inner_size=20
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n_layers=2
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func=torch.nn.Tanh
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bias=True
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def test_constructor():
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LowRankBlock(input_dimensions=input_dimensions,
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embedding_dimenion=embedding_dimenion,
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rank=rank,
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inner_size=inner_size,
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n_layers=n_layers,
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func=func,
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bias=bias)
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def test_constructor_wrong():
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with pytest.raises(ValueError):
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LowRankBlock(input_dimensions=input_dimensions,
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embedding_dimenion=embedding_dimenion,
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rank=0.5,
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inner_size=inner_size,
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n_layers=n_layers,
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func=func,
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bias=bias)
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def test_forward():
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block = LowRankBlock(input_dimensions=input_dimensions,
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embedding_dimenion=embedding_dimenion,
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rank=rank,
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inner_size=inner_size,
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n_layers=n_layers,
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func=func,
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bias=bias)
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data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u'])
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block(data.extract('u'), data.extract(['x', 'y']))
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def test_backward():
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block = LowRankBlock(input_dimensions=input_dimensions,
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embedding_dimenion=embedding_dimenion,
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rank=rank,
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inner_size=inner_size,
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n_layers=n_layers,
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func=func,
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bias=bias)
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data = LabelTensor(torch.rand(10, 30, 3), labels=['x', 'y', 'u'])
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data.requires_grad_(True)
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out = block(data.extract('u'), data.extract(['x', 'y']))
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loss = out.mean()
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loss.backward() |