chainer.functions.as_strided¶

chainer.functions.as_strided(x, shape, strides, storage_offset=None)[source]¶

Create a new view of array with the given shape, strides, and offset.

Parameters

x (tuple of Variable or numpy.ndarray or cupy.ndarray) – The array pointing a memory buffer. Its view is totally ignored.
shape (tuple of int) – The shape of output.
strides (tuple of int) – The strides of output, given in the unit of steps.
storage_offset (int) – The offset between the head of allocated memory and the pointer of first element, given in the unit of steps.

Returns

The strided variable.

Return type

Variable

Warning

Users should be aware that this function potentially causes unintended side effects. See numpy.lib.stride_tricks.as_strided for the detail.

Note

The backward algorithm is borrowed from torch.Tensor.as_strided. Therefore, the returned gradient of backward is layout-agnostic when x contains memory overlap. See notes in pytorch’s source code (as_strided Backward and layout-aware/agnostic autograd) too.

Note

In this function strides and storage_offset are given in the unit of steps instead of bytes. This specification differs from numpy.lib.stride_tricks.as_strided().

Example

>>> from chainer import functions as F, Variable
>>> x = Variable(np.arange(4, dtype=np.float32))
>>> x
variable([0., 1., 2., 3.])
>>> y = F.as_strided(x, (3, 2), (1, 1), 0)
>>> y
variable([[0., 1.],
          [1., 2.],
          [2., 3.]])
>>> y.grad = np.ones((3, 2), dtype=np.float32)
>>> y.backward()
>>> x.grad
array([1., 2., 2., 1.], dtype=float32)