RandomApply

class torchvision.transforms.RandomApply(transforms, p=0.5)

Apply randomly a list of transformations with a given probability.

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

Examples using RandomApply:

Illustration of transforms

Illustration of transforms

forward(img)

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.