Support arbitrary number of feature dimensions in latent to discrete transformation #26
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thisac
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Sep 3, 2025
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Thanks @VolodyaCO. Don't forget to add a an entry to the release-log.
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@thisac I don't understand very well the reno process for release notes. Is every PR intended to create a new release? |
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@thisac nvm, I just read "All of the list items in this section are combined when the release notes are rendered" |
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anahitamansouri
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Sep 22, 2025
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kevinchern
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| @parameterized.expand( | ||
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| (1, torch.tensor([[[1.0, 1.0]], [[1.0, -1.0]], [[-1.0, -1.0]], [[-1.0, 1.0]]])), | ||
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| 5, | ||
| torch.tensor( | ||
| [[[1.0, 1.0]] * 5, [[1.0, -1.0]] * 5, [[-1.0, -1.0]] * 5, [[-1.0, 1.0]] * 5] | ||
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Suggested change
| @parameterized.expand( | |
| [ | |
| (1, torch.tensor([[[1.0, 1.0]], [[1.0, -1.0]], [[-1.0, -1.0]], [[-1.0, 1.0]]])), | |
| ( | |
| 5, | |
| torch.tensor( | |
| [[[1.0, 1.0]] * 5, [[1.0, -1.0]] * 5, [[-1.0, -1.0]] * 5, [[-1.0, 1.0]] * 5] | |
| ), | |
| ), | |
| ] | |
| ) | |
| @parameterized.expand([ | |
| (1, torch.tensor([[[1, 1]], [[1, -1]], [[-1, -1]], [[-1, 1]]]).float()), | |
| (5, torch.tensor([[[1, 1]]*5, [[1, -1]]*5, [[-1, -1]]*5, [[-1, 1]]*5]).float()) | |
| ]) |
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This is the same. I'll leave it like it is, or you can commit this suggestion later.
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fix number of feature dimensions [docs] consistent notation release ignore files in aim testing different number of latent dimensions extend tests to more feature dimensions Update tests/test_dvae_winci2020.py Co-authored-by: Anahita Mansouri Bigvand <[email protected]> Update tests/test_dvae_winci2020.py Co-authored-by: Anahita Mansouri Bigvand <[email protected]> Update tests/test_dvae_winci2020.py Co-authored-by: Anahita Mansouri Bigvand <[email protected]>
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LGTM @thisac @VolodyaCO |
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The encoder might not encode a datapoint x to a 1D spin string. In the case of autoencoders where vector quantisation layers are added, encoders might encode a datapoint x to a HxWxC array. In such a case, the default latent to discrete transformation would be wrong, as it expected only a single feature dimension, whereas in this case there are three such feature dimensions.
This PR fixes that by automatically finding the number of feature dimensions in the encoded data representation, and performs the gumbel softmax transformation accordingly, keeping the same number of feature dimensions.