RandomErasing

class torchvision.transforms.RandomErasing(p=0.5, scale=(0.02, 0.33), ratio=(0.3, 3.3), value=0, inplace=False)

Randomly selects a rectangle region in a torch.Tensor image and erases its pixels. This transform does not support PIL Image. ‘Random Erasing Data Augmentation’ by Zhong et al. See https://arxiv.org/abs/1708.04896

Parameters:

• p – probability that the random erasing operation will be performed.

• scale – range of proportion of erased area against input image.

• ratio – range of aspect ratio of erased area.

• value – erasing value. Default is 0. If a single int, it is used to erase all pixels. If a tuple of length 3, it is used to erase R, G, B channels respectively. If a str of ‘random’, erasing each pixel with random values.

• inplace – boolean to make this transform inplace. Default set to False.

Returns:

Erased Image.

Example

>>> transform = transforms.Compose([
>>>   transforms.RandomHorizontalFlip(),
>>>   transforms.PILToTensor(),
>>>   transforms.ConvertImageDtype(torch.float),
>>>   transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
>>>   transforms.RandomErasing(),
>>> ])

forward(img)

Parameters:

img (Tensor) – Tensor image to be erased.

Returns:

Erased Tensor image.

Return type:

img (Tensor)

static get_params(img: Tensor, scale: Tuple[float, float], ratio: Tuple[float, float], value: Optional[List[float]] = None) → Tuple[int, int, int, int, Tensor]

Get parameters for erase for a random erasing.

Parameters:

• img (Tensor) – Tensor image to be erased.

• scale (sequence) – range of proportion of erased area against input image.

• ratio (sequence) – range of aspect ratio of erased area.

• value (list, optional) – erasing value. If None, it is interpreted as “random” (erasing each pixel with random values). If len(value) is 1, it is interpreted as a number, i.e. value[0].

Returns:

params (i, j, h, w, v) to be passed to erase for random erasing.

Return type:

tuple