Upsample

Upsample(size=None, scale_factor=None, mode="nearest", align_corners=None, recompute_scale_factor=False, antialias=False)

Resamples the spatial dimensions to ``size`` or by ``scale_factor``
(exactly one given); the input is channels-last and its rank picks the
spatial variant.

The module form of ``clika_runtime.nn.functional.interpolate``: construct
it once with the settings, then call it like a function.

Args:
    size: the target spatial extents; each entry an integer or a 0-D
        tensor; None takes the function's own default.
    scale_factor: the multiplier per spatial dimension; each entry a number
        or a 0-D tensor; None takes the function's own default.
    mode: the interpolation mode.
    align_corners: aligns the corner samples of input and output for the
        linear modes; None takes the function's own default.
    recompute_scale_factor: derives the effective scale from the computed
        output size.
    antialias: low-pass filters when downsampling.

__init__

__init__(self, size: 'Sequence[Tensor | float | int] | None' = None, scale_factor: 'Sequence[Tensor | float | int] | None' = None, mode: '_InterpMode' = 'nearest', align_corners: 'bool | None' = None, recompute_scale_factor: 'bool' = False, antialias: 'bool' = False) -> 'None'

Initialize self. See help(type(self)) for accurate signature.

extra_repr

extra_repr(self) -> 'str'

extra_repr() -> str

One line of per-class detail for :meth:__repr__; a layer prints its geometry here (in_features=64, out_features=256).

forward

forward(self, input: 'Tensor') -> 'Tensor'

forward(input) -> Tensor

Runs clika_runtime.nn.functional.interpolate on input with the stored settings; returns a new tensor.