Fix tests #62
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Fix tests #62
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mariogeiger
commented
Jan 13, 2025
| @@ -48,8 +45,8 @@ def make_descriptors(): | |||
| ) | |||
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| # These ETPs will trigger the symmetricContraction kernel | |||
| yield descriptors.spherical_harmonics(cue.SO3(1), [0, 1, 2, 3, 4, 5]) | |||
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Here I put back a lower degree polynomial.
The degree 5 was giving a bit too high errors compared to all the other ETP tested.
mariogeiger
commented
Jan 13, 2025
Comment on lines
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| @pytest.mark.parametrize("batch_size", [0, 5]) | ||
| @pytest.mark.parametrize("use_fallback", [True, False]) | ||
| def test_high_degrees(use_fallback: bool, batch_size: int): | ||
| if not use_fallback and not torch.cuda.is_available(): | ||
| pytest.skip("CUDA is not available") | ||
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| e = cue.descriptors.spherical_harmonics(cue.SO3(1), [0, 1, 2, 3, 4, 5]) | ||
| m = cuet.EquivariantTensorProduct( | ||
| e, | ||
| layout=cue.mul_ir, | ||
| device=device, | ||
| math_dtype=torch.float32, | ||
| use_fallback=use_fallback, | ||
| ) | ||
| inputs = [ | ||
| torch.randn((batch_size, rep.dim), device=device, dtype=torch.float32) | ||
| for rep in e.inputs | ||
| ] | ||
| output = m(*inputs) | ||
| assert output.shape == (batch_size, e.output.dim) |
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Here I separately test if a high degree polynomial goes through.
borisfom
approved these changes
Jan 13, 2025
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