I have my label tensor of shape (1,1,128,128,128) in which the values might range from 0,24. I want to convert this to one hot encoded tensor, using the nn.fucntional.one_hot
function
n = 24
one_hot = torch.nn.functional.one_hot(indices, n)
but this expects a tensor of indices, honestly, I am not sure how to get those. The only tensor I have is the label tensor of the shape described above and it contains values ranging from 1-24, not the indices
How can I get a tensor of indices from my tensor? Thanks in advance.
One hot tensor is a Tensor in which all the values at indices where i =j and i!= j is same. Method Used: one_hot: This method accepts a Tensor of indices, a scalar defining depth of the one hot dimension and returns a one hot Tensor with default on value 1 and off value 0. These on and off values can be modified.
Creating PyTorch one-hot encoding In the above example, we try to implement the one hot() encoding function as shown here first; we import all required packages, such as a torch. After that, we created a tenor with different values, and finally, we applied the one hot() function as shown.
The tf. one_hot operation takes a list of category indices and a depth (for our purposes, essentially a number of unique categories), and outputs a One Hot Encoded Tensor. You'll notice a few key differences though between OneHotEncoder and tf.
Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)
If the error you are getting is this one:
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
RuntimeError: one_hot is only applicable to index tensor.
Maybe you just need to convert to int64
:
import torch
# random Tensor with the shape you said
indices = torch.Tensor(1, 1, 128, 128, 128).random_(1, 24)
# indices.shape => torch.Size([1, 1, 128, 128, 128])
# indices.dtype => torch.float32
n = 24
one_hot = torch.nn.functional.one_hot(indices.to(torch.int64), n)
# one_hot.shape => torch.Size([1, 1, 128, 128, 128, 24])
# one_hot.dtype => torch.int64
You can use indices.long()
too.
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