pyiqa.matlab_utils.resize
=========================

.. py:module:: pyiqa.matlab_utils.resize

.. autoapi-nested-parse::

   A standalone PyTorch implementation for fast and efficient bicubic resampling.
   The resulting values are the same to MATLAB function imresize('bicubic').
   ## Author:      Sanghyun Son
   ## Email:       sonsang35@gmail.com (primary), thstkdgus35@snu.ac.kr (secondary)
   ## Version:     1.2.0
   ## Last update: July 9th, 2020 (KST)
   Dependency: torch
   Example::
   >>> import torch
   >>> import core
   >>> x = torch.arange(16).float().view(1, 1, 4, 4)
   >>> y = core.imresize(x, sizes=(3, 3))
   >>> print(y)
   tensor([[[[ 0.7506,  2.1004,  3.4503],
             [ 6.1505,  7.5000,  8.8499],
             [11.5497, 12.8996, 14.2494]]]])





Module Contents
---------------

.. py:function:: imresize(x: torch.Tensor, scale: Optional[float] = None, sizes: Optional[Tuple[int, int]] = None, kernel: Union[str, torch.Tensor] = 'cubic', sigma: float = 2, rotation_degree: float = 0, padding_type: str = 'reflect', antialiasing: bool = True) -> torch.Tensor

   :param x:
   :type x: torch.Tensor
   :param scale:
   :type scale: float
   :param sizes:
   :type sizes: tuple(int, int)
   :param kernel:
   :type kernel: str, default='cubic'
   :param sigma:
   :type sigma: float, default=2
   :param rotation_degree:
   :type rotation_degree: float, default=0
   :param padding_type:
   :type padding_type: str, default='reflect'
   :param antialiasing:
   :type antialiasing: bool, default=True

   :rtype: torch.Tensor