o V h@sddlZddlZddlmmZddlmZmZddlmZ dded e d e d e d e d e defddZ dded e d e d e d e d e defddZ ejdGdddejZejdGdddeZdS)N)nnTensor)_log_api_usage_onceFư>Tinputp block_sizeinplaceepstrainingreturnc Cs^tjstjstt|dks|dkrtd|d|jdkr,td|jd|r2|dkr4|S|\}}}} t || |}||| |d||d | |d } tj ||||d | |d f|j |j d } | | tj| |dgdd d } tj| d ||f|dd} d | } | || } |r|| | |S|| | }|S)a Implements DropBlock2d from `"DropBlock: A regularization method for convolutional networks" `. 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)rrstride kernel_sizepadding)torchjit is_scripting is_tracingr drop_block2d ValueErrorndimsizeminemptyrr bernoulli_Fpad max_pool2dnumelsummul_) rrr r r r NCHWgammanoisenormalize_scaler6N/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/ops/drop_block.pyr" s,   ,,  r"cCs~tjstjstt|dks|dkrtd|d|jdkr,td|jd|r2|dkr4|S|\}}}} } t ||| | }||| | |d||d | |d | |d } tj ||||d | |d | |d f|j |j d } | | tj| |d gd d d} tj| d|||f|d d} d | } | || } |r|| | |S|| | }|S)a  Implements DropBlock3d from `"DropBlock: A regularization method for convolutional networks" `. 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)rrr r!r drop_block3dr#r$r%r&r'rrr(r)r* max_pool3dr,r-r.)rrr r r r r/r0Dr1r2r3r4r5r6r6r7r;7s4  <,  r;c sXeZdZdZddededededd f fd d Zd edefd dZ de fddZ Z S) DropBlock2dz# See :func:`drop_block2d`. Frrr r r r Ncs&t||_||_||_||_dSN)super__init__rr r r selfrr r r  __class__r6r7rAqs  zDropBlock2d.__init__rcCt||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 rCrr6r6r7forwardyzDropBlock2d.forwardcCs*|jjd|jd|jd|jd}|S)Nz(p=z , block_size=z , inplace=))rE__name__rr r )rCsr6r6r7__repr__s&zDropBlock2d.__repr__Fr) rL __module__ __qualname____doc__floatintboolrArrIstrrN __classcell__r6r6rDr7r>ls $ r>c sJeZdZdZddededededd f fd d Zd edefd dZ Z S) DropBlock3dz# See :func:`drop_block3d`. Frrr r r r Ncst||||dSr?)r@rArBrDr6r7rAszDropBlock3d.__init__rcCrFrG)r;rr r r r rHr6r6r7rIrJzDropBlock3d.forwardrO) rLrPrQrRrSrTrUrArrIrWr6r6rDr7rXs$rX)FrT)rtorch.fxtorch.nn.functionalr functionalr)rutilsrrSrTrUr"r;fxwrapModuler>rXr6r6r6r7sR  / 2