torch.as_strided(input, size, stride, storage_offset=0)Tensor

Create a view of an existing torch.Tensor input with specified size, stride and storage_offset.


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


>>> 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]])


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