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# affine

torchvision.transforms.functional.affine(img: Tensor, angle: float, translate: List[int], scale: float, shear: List[float], interpolation: InterpolationMode = InterpolationMode.NEAREST, fill: Optional[List[float]] = None, center: Optional[List[int]] = None) → Tensor

  Apply affine transformation on the image keeping image center invariant. If the image is torch Tensor, it is expected to have […, H, W] shape, where … means an arbitrary number of leading dimensions.

  Parameters:

    - img (PIL Image or Tensor) – image to transform.

    - angle (number) – rotation angle in degrees between -180 and 180, clockwise direction.

    - translate (sequence of python:integers) – horizontal and vertical translations (post-rotation translation)

    - scale (float) – overall scale

    - shear (float or sequence) – shear angle value in degrees between -180 to 180, clockwise direction. If a sequence is specified, the first value corresponds to a shear parallel to the x-axis, while the second value corresponds to a shear parallel to the y-axis.

    - interpolation (InterpolationMode) – Desired interpolation enum defined by `torchvision.transforms.InterpolationMode`. Default is `InterpolationMode.NEAREST`. If input is Tensor, only `InterpolationMode.NEAREST`, `InterpolationMode.BILINEAR` are supported. The corresponding Pillow integer constants, e.g. `PIL.Image.BILINEAR` are accepted as well.

    - fill (sequence or number, optional) –

      Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively.

      Note

      In torchscript mode single int/float value is not supported, please use a sequence of length 1: `[value, ]`.

    - center (sequence, optional) – Optional center of rotation. Origin is the upper left corner. Default is the center of the image.

  Returns:

    Transformed image.

  Return type:

    PIL Image or Tensor

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