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# drop_block3d

torchvision.ops.drop_block3d(input: Tensor, p: float, block_size: int, inplace: bool = False, eps: float = 1e-06, training: bool = True) → Tensor

  Implements DropBlock3d from “DropBlock: A regularization method for convolutional networks” <https://arxiv.org/abs/1810.12890>.

  Parameters:

    - input (Tensor[N, C, D, H, W]) – The input tensor or 5-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, D, H, W]

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