- torch.as_strided(input, size, stride, storage_offset=None) Tensor ¶
Create a view of an existing torch.Tensor
Prefer using other view functions, like
torch.Tensor.expand(), to setting a view’s strides manually with as_strided, as this function’s behavior depends on the implementation of a tensor’s storage. The constructed view of the storage must only refer to elements within the storage or a runtime error will be thrown, and if the view is “overlapped” (with multiple indices referring to the same element in memory) its behavior is undefined.
input (Tensor) – the input tensor.
size (tuple or ints) – the shape of the output tensor
stride (tuple or ints) – the stride of the output tensor
storage_offset (int, optional) – the offset in the underlying storage of the output tensor. If
None, the storage_offset of the output tensor will match the input tensor.
>>> x = torch.randn(3, 3) >>> x tensor([[ 0.9039, 0.6291, 1.0795], [ 0.1586, 2.1939, -0.4900], [-0.1909, -0.7503, 1.9355]]) >>> t = torch.as_strided(x, (2, 2), (1, 2)) >>> t tensor([[0.9039, 1.0795], [0.6291, 0.1586]]) >>> t = torch.as_strided(x, (2, 2), (1, 2), 1) tensor([[0.6291, 0.1586], [1.0795, 2.1939]])