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Generate one hot labels from integer labels in PyTorch
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def make_one_hot(labels, C=2): | |
''' | |
Converts an integer label torch.autograd.Variable to a one-hot Variable. | |
Parameters | |
---------- | |
labels : torch.autograd.Variable of torch.cuda.LongTensor | |
N x 1 x H x W, where N is batch size. | |
Each value is an integer representing correct classification. | |
C : integer. | |
number of classes in labels. | |
Returns | |
------- | |
target : torch.autograd.Variable of torch.cuda.FloatTensor | |
N x C x H x W, where C is class number. One-hot encoded. | |
''' | |
one_hot = torch.cuda.FloatTensor(labels.size(0), C, labels.size(2), labels.size(3)).zero_() | |
target = one_hot.scatter_(1, labels.data, 1) | |
target = Variable(target) | |
return target |
Thanks @rodrigoberriel, I think the official function was added as part of the big torch==0.4.0
bump that came out a year or so after this gist was published. Folks should use that instead.
Note that PyTorch's one_hot
expands the last dimension, so the resulting tensor is NHWC
rather than PyTorch standard NCHW
which your prediction is likely to come in. To turn it into NCHW, one would need to add .permute(0,3,1,2)
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It looks like this gist has some visits, so I would like to let you know that PyTorch has
one_hot
in thenn.functional
package: torch.nn.functional.one_hot(tensor, num_classes=0)