I got a runtime error at evaluation phase of my simple siamese network as RuntimeError: cuda runtime error (2) : out of memory at the line of executing forward pass.
Some comments on stackoverflow suggests to define x and y with volatile=True as
[code lang='python']
for x, y in dataloader:
x = torch.autograd.Variable(x.cuda(), volatile=True)
y = torch.autograd.Variable(y.cuda(), volatile=True)
y_pred = net(x)
loss = criterion(y, y_pred)
...
[/code]
The above code further outputs user warning as UserWarning: volatile was removed and now has no effect. Use with torch.no_grad(): instead, so I uses torch.no_grad as
[code lang='python'] with torch.no_grad(): for x, y in dataloader: y_pred = net(x) loss = criterion(y, y_pred) ... [/code]
Finally, with torch.no_grad(), runtime error doesn't occur!
