Run into the issue myself and did some searching, torch.sparse.torch.eye(num_labels).index_select(dim=0, index=labels) also seems to work pretty well in addition to the scatter_ solution in the 0.3 release.

# Convert int into one-hot format

**Sajid_Iqbal**(Sajid Iqbal) #23

def get_one_hot(preds,gt):

encoded_target = preds.data.clone().zero_()

target = gt.unsqueeze(1) # now target is in shape [BCHW]=[20,1,240,240]

unseq = target.long()

unseq = unseq.data

```
# encoded_target.scatter_(dim,index,val)
# unseq dim 'dim' must be 1
encoded_target.scatter_(1, unseq, 1)
encoded_target=encoded_target.view(-1,5)
#b=encoded_target.view(-1,5,240,240)
#show_my_one_hot(b,target)
return encoded_target
```

It returns the one hot encoding of the target. In my case the target was of shape [1,1,240,240] and preds of shape [1,5,240,240]

**justheuristic**(Justheuristic) #24

Here’s a tensorflow-like solution based on previous code in this thread

```
def to_one_hot(y, n_dims=None):
""" Take integer y (tensor or variable) with n dims and convert it to 1-hot representation with n+1 dims. """
y_tensor = y.data if isinstance(y, Variable) else y
y_tensor = y_tensor.type(torch.LongTensor).view(-1, 1)
n_dims = n_dims if n_dims is not None else int(torch.max(y_tensor)) + 1
y_one_hot = torch.zeros(y_tensor.size()[0], n_dims).scatter_(1, y_tensor, 1)
y_one_hot = y_one_hot.view(*y.shape, -1)
return Variable(y_one_hot) if isinstance(y, Variable) else y_one_hot
```

**willyd**(Guillaume Dumont) #25

It is also possible to abuse broadcasting and do:

```
# some labels
labels = torch.arange(3)
labels = labels.reshape(3, 1)
num_classes = 4
one_hot_target = (labels == torch.arange(num_classes).reshape(1, num_classes)).float()
```

gives

```
1 0 0 0
0 1 0 0
0 0 1 0
[torch.FloatTensor of size (3,4)]
```