RandomAffine

class torchvision.transforms.v2.RandomAffine(degrees: Union[Number, Sequence], translate: Optional[Sequence[float]] = None, scale: Optional[Sequence[float]] = None, shear: Optional[Union[int, float, Sequence[float]]] = None, interpolation: Union[InterpolationMode, int] = InterpolationMode.NEAREST, fill: Union[int, float, Sequence[int], Sequence[float], None, Dict[Union[Type, str], Optional[Union[int, float, Sequence[int], Sequence[float]]]]] = 0, center: Optional[List[float]] = None)

Random affine transformation the input keeping center invariant.

If the input is a torch.Tensor or a TVTensor (e.g. Image, Video, BoundingBoxes etc.) it can have arbitrary number of leading batch dimensions. For example, the image can have [..., C, H, W] shape. A bounding box can have [..., 4] shape.

Parameters:

• degrees (sequence or number) – Range of degrees to select from. If degrees is a number instead of sequence like (min, max), the range of degrees will be (-degrees, +degrees). Set to 0 to deactivate rotations.

• translate (tuple, optional) – tuple of maximum absolute fraction for horizontal and vertical translations. For example translate=(a, b), then horizontal shift is randomly sampled in the range -img_width * a < dx < img_width * a and vertical shift is randomly sampled in the range -img_height * b < dy < img_height * b. Will not translate by default.

• scale (tuple, optional) – scaling factor interval, e.g (a, b), then scale is randomly sampled from the range a <= scale <= b. Will keep original scale by default.

• shear (sequence or number, optional) – Range of degrees to select from. If shear is a number, a shear parallel to the x-axis in the range (-shear, +shear) will be applied. Else if shear is a sequence of 2 values a shear parallel to the x-axis in the range (shear[0], shear[1]) will be applied. Else if shear is a sequence of 4 values, an x-axis shear in (shear[0], shear[1]) and y-axis shear in (shear[2], shear[3]) will be applied. Will not apply shear by default.

• interpolation (InterpolationMode, optional) – 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 (number or tuple or dict, optional) – Pixel fill value used when the padding_mode is constant. Default is 0. If a tuple of length 3, it is used to fill R, G, B channels respectively. Fill value can be also a dictionary mapping data type to the fill value, e.g. fill={tv_tensors.Image: 127, tv_tensors.Mask: 0} where Image will be filled with 127 and Mask will be filled with 0.

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

Examples using RandomAffine:

Illustration of transforms

Illustration of transforms

static get_params(degrees: List[float], translate: Optional[List[float]], scale_ranges: Optional[List[float]], shears: Optional[List[float]], img_size: List[int]) → Tuple[float, Tuple[int, int], float, Tuple[float, float]]

Get parameters for affine transformation

Returns:

params to be passed to the affine transformation

make_params(flat_inputs: List[Any]) → Dict[str, Any]

Method to override for custom transforms.

See How to write your own v2 transforms

transform(inpt: Any, params: Dict[str, Any]) → Any

Method to override for custom transforms.

See How to write your own v2 transforms