Pytorch doesn't support one-hot vector?

Question

I am very confused by how Pytorch deals with one-hot vectors. In this tutorial, the neural network will generate a one-hot vector as its output. As far as I understand, the schematic structure of the neural network in the tutorial should be like:

However, the labels are not in one-hot vector format. I get the following size

print(labels.size()) print(outputs.size())  output>>> torch.Size([4])  output>>> torch.Size([4, 10]) 

Miraculously, I they pass the outputs and labels to criterion=CrossEntropyLoss(), there's no error at all.

loss = criterion(outputs, labels) # How come it has no error? 

My hypothesis:

Maybe pytorch automatically convert the labels to one-hot vector form. So, I try to convert labels to one-hot vector before passing it to the loss function.

def to_one_hot_vector(num_class, label):     b = np.zeros((label.shape[0], num_class))     b[np.arange(label.shape[0]), label] = 1      return b  labels_one_hot = to_one_hot_vector(10,labels) labels_one_hot = torch.Tensor(labels_one_hot) labels_one_hot = labels_one_hot.type(torch.LongTensor)  loss = criterion(outputs, labels_one_hot) # Now it gives me error 

However, I got the following error

RuntimeError: multi-target not supported at /opt/pytorch/pytorch/aten/src/THCUNN/generic/ClassNLLCriterion.cu:15

So, one-hot vectors are not supported in Pytorch? How does Pytorch calculates the cross entropy for the two tensor outputs = [1,0,0],[0,0,1] and labels = [0,2] ? It doesn't make sense to me at all at the moment.

Answer 1

PyTorch states in its documentation for CrossEntropyLoss that

This criterion expects a class index (0 to C-1) as the target for each value of a 1D tensor of size minibatch

In other words, it has your to_one_hot_vector function conceptually built in CEL and does not expose the one-hot API. Notice that one-hot vectors are memory inefficient compared to storing class labels.

If you are given one-hot vectors and need to go to class labels format (for instance to be compatible with CEL), you can use argmax like below:

import torch   labels = torch.tensor([1, 2, 3, 5]) one_hot = torch.zeros(4, 6) one_hot[torch.arange(4), labels] = 1   reverted = torch.argmax(one_hot, dim=1) assert (labels == reverted).all().item()