---
title: "Upsample"
sidebar_label: "Upsample"
description: "The clika_runtime.nn Upsample class."
---

<!-- Generated by tools/api_reference/generate_api_docs.py. Do not edit. -->

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__`

```python
__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`

```python
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`

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

forward(input) -> Tensor

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