Unfold

Unfold(kernel_size, dilation=None, padding=None, stride=None, mode="constant", value=None)

Extracts sliding windows from a channels-last input ``[N, D1..Dn, C]`` into
``[N, L, prod(kernel_size), C]``, one row per window placement.

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

Args:
    kernel_size: the window extent per spatial dimension.
    dilation: the window dilation per spatial dimension; empty is 1; None
        takes the function's own default.
    padding: the ``(low, high)`` pad pairs per spatial axis; empty is no
        padding; None takes the function's own default.
    stride: the window step per spatial dimension; empty is 1; None takes
        the function's own default.
    mode: how the padded region is filled.
    value: the constant fill; unset is 0; None takes the function's own
        default.

__init__

__init__(self, kernel_size: 'int | Sequence[int]', dilation: 'int | Sequence[int] | None' = None, padding: 'int | Sequence[int] | None' = None, stride: 'int | Sequence[int] | None' = None, mode: '_PadMode' = 'constant', value: 'float | None' = None) -> '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.unfold on input with the stored settings; returns a new tensor.