holocron.transforms

holocron.transforms provides PIL and PyTorch tensor transformations.

Resize

Resize(size: tuple[int, int], mode: ResizeMethod = SQUISH, pad_mode: str = 'constant', **kwargs: Any)

Bases: Resize

Implements a more flexible resizing scheme.

from holocron.transforms import Resize pil_img = ... tf = Resize((224, 224), mode="pad") resized_img = tf(pil_img)

PARAMETER	DESCRIPTION
size	the desired height and width of the image in pixels TYPE: tuple[int, int]
mode	the resizing scheme ("squish" is similar to PyTorch, "pad" will preserve the aspect ratio and pad) TYPE: ResizeMethod DEFAULT: SQUISH
pad_mode	padding mode when mode is "pad" TYPE: str DEFAULT: 'constant'
kwargs	the keyword arguments of torchvision.transforms.v2.Resize TYPE: Any DEFAULT: {}

Source code in holocron/transforms/interpolation.py

def __init__(
    self,
    size: tuple[int, int],
    mode: ResizeMethod = ResizeMethod.SQUISH,
    pad_mode: str = "constant",
    **kwargs: Any,
) -> None:
    if not isinstance(mode, ResizeMethod):
        raise TypeError("mode is expected to be a ResizeMethod")
    if not isinstance(size, (tuple, list)) or len(size) != 2 or any(s <= 0 for s in size):
        raise ValueError("size is expected to be a sequence of 2 positive integers")
    super().__init__(size, **kwargs)
    self.mode: ResizeMethod = mode
    self.pad_mode: str = pad_mode
    self.size: tuple[int, int]

RandomZoomOut

RandomZoomOut(size: tuple[int, int], scale: tuple[float, float] = (0.5, 1.0), **kwargs: Any)

Bases: Module

Implements a size reduction of the orignal image to provide a zoom out effect.

from holocron.transforms import RandomZoomOut pil_img = ... tf = RandomZoomOut((224, 224), scale=(0.3, 1.)) resized_img = tf(pil_img)

PARAMETER	DESCRIPTION
size	the desired height and width of the image in pixels TYPE: tuple[int, int]
scale	the range of relative area of the projected image to the desired size TYPE: tuple[float, float] DEFAULT: (0.5, 1.0)
kwargs	the keyword arguments of torchvision.transforms.functional.resize TYPE: Any DEFAULT: {}

Source code in holocron/transforms/interpolation.py

def __init__(self, size: tuple[int, int], scale: tuple[float, float] = (0.5, 1.0), **kwargs: Any) -> None:
    if not isinstance(size, (tuple, list)) or len(size) != 2 or any(s <= 0 for s in size):
        raise ValueError("size is expected to be a sequence of 2 positive integers")
    if len(scale) != 2 or scale[0] > scale[1]:
        raise ValueError("scale is expected to be a couple of floats, the first one being small than the second")
    super().__init__()
    self.size: tuple[int, int] = size
    self.scale: tuple[float, float] = scale
    self._kwargs: dict[str, Any] = kwargs