model.py dimension misalignment

[shenkev]

There's a dimension misalignment error in the l2norm function when trying to expand the "norm" variable.

Log:

    features = l2norm(features)
  File "/media/shenkev/data/Ubuntu/vsepp/model.py", line 17, in l2norm
    X = torch.div(X, norm.expand_as(X))
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/variable.py", line 725, in expand_as
    return Expand.apply(self, (tensor.size(),))
  File "/usr/local/lib/python2.7/dist-packages/torch/autograd/_functions/tensor.py", line 111, in forward
    result = i.expand(*new_size)
RuntimeError: The expanded size of the tensor (1024) must match the existing size (128) at non-singleton dimension 1. at /pytorch/torch/lib/THC/generic/THCTensor.c:323

I think you can fix this by using unsqueeze instead of expand_as, shown below

def l2norm(X):
    """L2-normalize columns of X
    """
    norm = torch.pow(X, 2).sum(dim=1).sqrt()
    X = torch.div(X, norm.unsqueeze(1))
    return X

[fartashf]

Thanks for reporting. Fixed in 62057bc. This was caused by backward incompatible Broadcasting introduced in PyTorch 0.2.