- Tensor.resize_(*sizes, memory_format=torch.contiguous_format) Tensor ¶
selftensor to the specified size. If the number of elements is larger than the current storage size, then the underlying storage is resized to fit the new number of elements. If the number of elements is smaller, the underlying storage is not changed. Existing elements are preserved but any new memory is uninitialized.
This is a low-level method. The storage is reinterpreted as C-contiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use
view(), which checks for contiguity, or
reshape(), which copies data if needed. To change the size in-place with custom strides, see
>>> x = torch.tensor([[1, 2], [3, 4], [5, 6]]) >>> x.resize_(2, 2) tensor([[ 1, 2], [ 3, 4]])