How to transform vectors of labels to one-hot encoding and back in Pytorch?
The solution to the question was copied to here after having to go through the entire forum discussion, instead of just finding an easy one from googling.
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1I don't really see the point of creating a thread to copy-paste a solution from another forum. – Ivan Dec 23 '20 at 16:55
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1@Ivan https://meta.stackoverflow.com/a/347922/913098 – Gulzar Apr 12 '21 at 10:56
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The point is to allow Google searchability. Searching through forums is a waste of time for everyone. – Gulzar Feb 10 '22 at 10:04
2 Answers
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From the Pytorch forums
import torch
import numpy as np
labels = torch.randint(0, 10, (10,))
# labels --> one-hot
one_hot = torch.nn.functional.one_hot(labels)
# one-hot --> labels
labels_again = torch.argmax(one_hot, dim=1)
np.testing.assert_equals(labels.numpy(), labels_again.numpy())
Gulzar
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2Notice [this answer](https://stackoverflow.com/a/74784150/913098) for cases where you have to specify the number of classes [which is most of the time] – Gulzar Dec 14 '22 at 12:45
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Since I can't comment on the accepted answer, I just wanted to add that if your target does not include all classes (e.g. because you train in batches), you can specify the number of classes as argument:
# labels --> one-hot
one_hot = torch.nn.functional.one_hot(target, num_classes=7)
swageta
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You now have sufficient reputation to comment. I suggest you do this. – DiMithras Dec 16 '22 at 17:13