expand(*sizes) → Tensor¶
Returns a new view of the
selftensor with singleton dimensions expanded to a larger size.
Passing -1 as the size for a dimension means not changing the size of that dimension.
Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.
Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the
strideto 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.
*sizes (torch.Size or int...) – the desired expanded size
More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.
>>> x = torch.tensor([, , ]) >>> x.size() torch.Size([3, 1]) >>> x.expand(3, 4) tensor([[ 1, 1, 1, 1], [ 2, 2, 2, 2], [ 3, 3, 3, 3]]) >>> x.expand(-1, 4) # -1 means not changing the size of that dimension tensor([[ 1, 1, 1, 1], [ 2, 2, 2, 2], [ 3, 3, 3, 3]])