- 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.
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.
In order to script the transformation, please use
torch.nn.ModuleListas 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
transforms (sequence or torch.nn.Module) – list of transformations
p (float) – probability of applying the list 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]¶
Defines the computation performed at every call.
Should be overridden by all subclasses.
Although the recipe for forward pass needs to be defined within this function, one should call the
Moduleinstance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.