I would like to flatten a tensor into column-major order. What is the optimal way for achieving this in PyTorch? Row-major order seems to be the default in PyTorch’s flatten function, and I don’t think there is an order option like in Numpy’s flatten function. Thanks!
Use tranpose method to reshape your tensor then flatten i.e.
x = torch.tensor([[1,2,3],[4,5,6],[7,8,9]]) x.flatten() tensor([1, 2, 3, 4, 5, 6, 7, 8, 9]) x.transpose(1, 0).flatten() tensor([1, 4, 7, 2, 5, 8, 3, 6, 9])
Thank you for your reply!
That works well for matrices (where the transpose can be written as simply
However, I am dealing with ndarrays, so that a matrix transpose is not applicable.
I just found that the solution had already been posted on this forum, here:
The answer was:
t = torch.rand(3, 3, 3) # convert to column-major order t.set_(t.storage(), t.storage_offset(), t.size(), tuple(reversed(t.stride()))) t.flatten() # 1D array in column-major order
Note that if you just want a tensor’s 1D representation in column-major order, the above operation will change the ordering of the elements in tensor
t. This function will pull out just the flattened array in column-major order:
def flatten_fortran_w_clone(t): nt = t.clone() nt = nt.set_(nt.storage(), nt.storage_offset(), nt.size(), tuple(reversed(nt.stride()))) return nt.flatten()
But there might be a better way of doing it without having to create a copy…