drop_block2d

torchvision.ops.drop_block2d(input: torch.Tensor, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06, training: bool = True) → torch.Tensor

Implements DropBlock2d from “DropBlock: A regularization method for convolutional networks” <https://arxiv.org/abs/1810.12890>.

Parameters

• input (Tensor[N, C, H, W]) – The input tensor or 4-dimensions with the first one being its batch i.e. a batch with N rows.

• p (float) – Probability of an element to be dropped.

• block_size (int) – Size of the block to drop.

• inplace (bool) – If set to True, will do this operation in-place. Default: False.

• eps (float) – A value added to the denominator for numerical stability. Default: 1e-6.

• training (bool) – apply dropblock if is True. Default: True.

Returns

The randomly zeroed tensor after dropblock.

Return type

Tensor[N, C, H, W]