RandomApply

class torchvision.transforms.v2.RandomApply(transforms: Union[Sequence[Callable], ModuleList], p: float = 0.5)

[BETA] Apply randomly a list of transformations with a given probability.

Warning

The RandomApply transform is in Beta stage, and while we do not expect major breaking changes, some APIs may still change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753, and you can also check out https://github.com/pytorch/vision/issues/7319 to learn more about the APIs that we suspect might involve future changes.

Note

In order to script the transformation, please use torch.nn.ModuleList as input instead of list/tuple of transforms as shown below:

>>> transforms = transforms.RandomApply(torch.nn.ModuleList([
>>>     transforms.ColorJitter(),
>>> ]), p=0.3)
>>> scripted_transforms = torch.jit.script(transforms)

Make sure to use only scriptable transformations, i.e. that work with torch.Tensor, does not require lambda functions or PIL.Image.

Parameters:

• transforms (sequence or torch.nn.Module) – list of transformations

• p (float) – probability of applying the list of transforms

extra_repr() → str

Set the extra representation of the module

To print customized extra information, you should re-implement this method in your own modules. Both single-line and multi-line strings are acceptable.

forward(*inputs: Any) → Any

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.