U ʹj @sddlZddlZddlmmZddlmZmZddlmZdee e e e e edd d Z dee e e e e edd d Z ejd Gd ddejZejd GdddeZdS)N)nnTensor)_log_api_usage_onceFư>T)inputp block_sizeinplaceepstrainingreturnc Cs^tjstjstt|dks,|dkr`. Args: 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: Tensor[N, C, H, W]: The randomly zeroed tensor after dropblock. ?4drop probability has to be between 0 and 1, but got .z#input should be 4 dimensional. Got dimensions.rdtypedevicervalue)rrZstrideZ kernel_sizepadding)torchjit is_scripting is_tracingr drop_block2d ValueErrorndimsizeminemptyrr bernoulli_FpadZ max_pool2dnumelsummul_) rrr r r r NCHWgammanoisenormalize_scaler3Y/var/www/html/bwave/python/venv/lib/python3.8/site-packages/torchvision/ops/drop_block.pyr s*   ,,  r cCs~tjstjstt|dks,|dkr`. Args: 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: Tensor[N, C, D, H, W]: The randomly zeroed tensor after dropblock. rrrrz#input should be 5 dimensional. Got rrrrrr)rrrr)rrrrr drop_block3dr!r"r#r$r%rrr&r'r(Z max_pool3dr)r*r+)rrr r r r r,r-Dr.r/r0r1r2r3r3r4r87s<  <$  r8csNeZdZdZdeeeeddfdd Zeedd d Z e d d d Z Z S) DropBlock2dz# See :func:`drop_block2d`. FrNrr r r r cs&t||_||_||_||_dSN)super__init__rr r r selfrr r r  __class__r3r4r>qs  zDropBlock2d.__init__rr cCst||j|j|j|j|jSz Args: input (Tensor): Input feature map on which some areas will be randomly dropped. Returns: Tensor: The tensor after DropBlock layer. )r rr r r r r@rr3r3r4forwardyszDropBlock2d.forward)r cCs*|jjd|jd|jd|jd}|S)Nz(p=z , block_size=z , inplace=))rB__name__rr r )r@sr3r3r4__repr__s&zDropBlock2d.__repr__)Fr) rH __module__ __qualname____doc__floatintboolr>rrFstrrJ __classcell__r3r3rAr4r:ls r:cs@eZdZdZd eeeeddfdd Zeedd d Z Z S) DropBlock3dz# See :func:`drop_block3d`. FrNr;cst||||dSr<)r=r>r?rAr3r4r>szDropBlock3d.__init__rCcCst||j|j|j|j|jSrD)r8rr r r r rEr3r3r4rFszDropBlock3d.forward)Fr) rHrKrLrMrNrOrPr>rrFrRr3r3rAr4rSsrS)FrT)FrT)rZtorch.fxZtorch.nn.functionalrZ functionalr'rutilsrrNrOrPr r8ZfxwrapModuler:rSr3r3r3r4s@  / 2