I have an image, and I create four rotated versions of it using.
def rotate4dtensor(x):
x90 = x.transpose(2, 3)
x180 = x.flip(2)
x270 = x.transpose(2, 3).flip(3)
return [x,x90,x180,x270]
and now when during training I call the function in this manner
target = target.cuda()
input = input.cuda()
input = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
output = model(input)
loss = criterion(output, target_var)
#####vectorized version
losses_rot = []
#rotation loss for supervised
sup_rot = rotate4dtensor(input); tgts = []
sup_rot4 = torch.stack(sup_rot)
out = model(sup_rot4,is_rotation=True)
It throws the following error when no of gpus 3,5,6 but works fine with 1,2,and 4 why?
