RandomApply¶
- class torchvision.transforms.v2.RandomApply(transforms: Union[Sequence[Callable], ModuleList], p: float = 0.5)[source]¶
[BETA] Apply randomly a list of transformations with a given probability.
Note
The RandomApply transform is in Beta stage, and while we do not expect disruptive breaking changes, some APIs may slightly change according to user feedback. Please submit any feedback you may have in this issue: https://github.com/pytorch/vision/issues/6753.
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 orPIL.Image
.- Parameters:
transforms (sequence or torch.nn.Module) – list of transformations
p (float) – probability of applying the list of transforms
Examples using
RandomApply
:Illustration of transforms- extra_repr() str [source]¶
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 [source]¶
Define 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.