Low MIG values bug found & solution

[yanndubs]

Low MIG values bug found & solution

[DianeBouchacourt]

After a bit more digging, I think the problem comes from the fact that indexes are selected but the view() operation

disentangling-vae/disvae/evaluate.py

Line 272 in 7b8285b

samples_zCx = samples_zCx.expand(len_dataset, latent_dim, n_samples)

is reordering in a wrong manner, permute is the correct way to do it. Indeed, I've checked by taking the first 10000 samples, which are also the first 10000 samples in mean. With the view() operation it does not return the same values as in mean (which it should since the samples are mean values). I replaced with permute and it works as expected.

Thus I think it should be:

    samples_zCx = samples_zCx.index_select(0, samples_x).permute(1,0)
    samples_zCx = samples_zCx.unsqueeze(0).expand(len_dataset, latent_dim, n_samples)

in place of

disentangling-vae/disvae/evaluate.py

Line 269 in 7b8285b

samples_zCx = samples_zCx.index_select(0, samples_x).view(latent_dim, n_samples)

and

disentangling-vae/disvae/evaluate.py

Line 272 in 7b8285b

samples_zCx = samples_zCx.expand(len_dataset, latent_dim, n_samples)

[YannDubs]

Awesome thanks Diane, a few others had open issues for that but there was no solutions (I don't actively work on that anymore). Do you think you could send a PR ?

ideally, it would use samples instead of the mean , but I think it's more urgent to have the correct permutations !

[DianeBouchacourt]

Let me check my code again, and I will ! I also need to do a PR just for these changes (I edited quite some stuff in the fork I created).