df673cad4e
* solvers -> solver * adaptive_functions -> adaptive_function * callbacks -> callback * operators -> operator * pinns -> physics_informed_solver * layers -> block
69 lines
2.0 KiB
Python
69 lines
2.0 KiB
Python
import torch
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import pytest
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from pina.model.block import OrthogonalBlock
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torch.manual_seed(111)
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list_matrices = [
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torch.randn(10, 3),
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torch.rand(100, 5),
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torch.randn(5, 5),
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]
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list_prohibited_matrices_dim0 = list_matrices[:-1]
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@pytest.mark.parametrize("dim", [-1, 0, 1, None])
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@pytest.mark.parametrize("requires_grad", [True, False, None])
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def test_constructor(dim, requires_grad):
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if dim is None and requires_grad is None:
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block = OrthogonalBlock()
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elif dim is None:
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block = OrthogonalBlock(requires_grad=requires_grad)
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elif requires_grad is None:
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block = OrthogonalBlock(dim=dim)
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else:
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block = OrthogonalBlock(dim=dim, requires_grad=requires_grad)
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if dim is not None:
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assert block.dim == dim
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if requires_grad is not None:
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assert block.requires_grad == requires_grad
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def test_wrong_constructor():
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with pytest.raises(IndexError):
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OrthogonalBlock(2)
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with pytest.raises(ValueError):
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OrthogonalBlock('a')
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@pytest.mark.parametrize("V", list_matrices)
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def test_forward(V):
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orth = OrthogonalBlock()
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orth_row = OrthogonalBlock(0)
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V_orth = orth(V)
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V_orth_row = orth_row(V.T)
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assert torch.allclose(V_orth.T @ V_orth, torch.eye(V.shape[1]), atol=1e-6)
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assert torch.allclose(V_orth_row @ V_orth_row.T, torch.eye(V.shape[1]), atol=1e-6)
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@pytest.mark.parametrize("V", list_matrices)
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def test_backward(V):
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orth = OrthogonalBlock(requires_grad=True)
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V_orth = orth(V)
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loss = V_orth.mean()
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loss.backward()
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@pytest.mark.parametrize("V", list_matrices)
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def test_wrong_backward(V):
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orth = OrthogonalBlock(requires_grad=False)
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V_orth = orth(V)
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loss = V_orth.mean()
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with pytest.raises(RuntimeError):
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loss.backward()
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@pytest.mark.parametrize("V", list_prohibited_matrices_dim0)
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def test_forward_prohibited(V):
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orth = OrthogonalBlock(0)
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with pytest.raises(Warning):
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V_orth = orth(V)
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assert V.shape[0] > V.shape[1]
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