jitfields.resize

resize

resize(x, factor=None, shape=None, ndim=None, anchor='edge', order=2, bound='dct2', prefilter=True, out=None)

Resize a tensor using spline interpolation

Parameters:

Name	Type	Description	Default
`x`	`(..., *inshape) tensor`	Input tensor, with shape `(..., *inshape)`.	required
`factor`	`[sequence of] float`	Factor by which to resize the tensor (> 1 == bigger) One of factor or shape must be provided.	`None`
`shape`	`[sequence of] float`	Shape of output tensor. One of factor or shape must be provided.	`None`
`ndim`	`int`	Number if spatial dimensions. If not provided, try to guess from factor or shape. If guess fails, assume `ndim = x.dim()`.	`None`
`anchor`	`{'edge', 'center'} or None`	What feature should be aligned across the input and output tensors. If 'edge' or 'center', the effective scaling factor may slightly differ from the requested scaling factor. If None, the center of the (0, 0) voxel is aligned, and the requested factor is exactly applied.	`'edge'`
`order`	`[sequence of] {0..7}`	Interpolation order.	`2`
`bound`	`[sequence of] BoundType`	How to deal with out-of-bound values.	`'dct2'`
`prefilter`	`bool`	Whether to first compute interpolating coefficients. Must be true for proper interpolation, otherwise this function merely performs a non-interpolating "prolongation".	`True`
`out`	`Optional[Tensor]`	Output placeholder.	`None`

Returns:

Name	Type	Description
`x`	`(..., *shape) tensor`	Resized tensor, with shape `(..., *shape)`.

restrict(x, factor=None, shape=None, ndim=None, anchor='edge', order=1, bound='dct2', make_adjoint=False, out=None)

Restrict (adjoint of resize) a tensor using spline interpolation

Parameters:

Name	Type	Description	Default
`x`	`(..., *inshape) tensor`	Input tensor, with shape `(..., *inshape)`.	required
`factor`	`[sequence of] float`	Factor by which to resize the tensor (> 1 == smaller) One of factor or shape must be provided.	`None`
`shape`	`[sequence of] float`	Shape of output tensor. One of factor or shape must be provided.	`None`
`ndim`	`int`	Number if spatial dimensions. If not provided, try to guess from factor or shape. If guess fails, assume ndim = x.dim().	`None`
`anchor`	`{'edge', 'center'} or None`	What feature should be aligned across the input and output tensors. If 'edge' or 'center', the effective scaling factor may slightly differ from the requested scaling factor. If None, the center of the (0, 0) voxel is aligned, and the requested factor is exactly applied.	`'edge'`
`order`	`[sequence of] {0..7}`	Interpolation order.	`2`
`bound`	`[sequence of] BoundType`	How to deal with out-of-bound values.	`'dct2'`
`make_adjoint`	`bool`	Make `restrict` the numerical adjoint of `resize` by accumulating values into the output tensor. Otherwise, normalize by `count` to compute an average.	`False`
`out`	`Optional[Tensor]`	Output placeholder.	`None`

Returns:

Name	Type	Description
`x`	`(..., *shape) tensor`	Restricted tensor, with shape `(..., *shape)`.