:j>ddlZddlZddlmcmZddlmZmZddlmZ ddede de de d e d e d efd Z ddede de de d e d e d efd Z ejjdGddej ZejjdGddeZy)N)nnTensor)_log_api_usage_onceinputp block_sizeinplaceepstrainingreturncrtjjs-tjjst t |dks|dkDrt d|d|jdk7rt d|jd|r|dk(r|S|j\}}}} t|| |}||z| z|dz||z d z| |z d zzzz } tj||||z d z| |z d zf|j|j } | j| tj| |dzgdzd } tj | d ||f|dz} d | z } | j#|| j%zz } |r"|j'| j'| |S|| z| z}|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_scales o/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchvision/ops/drop_block.pyr#r# s& 99 ! ! #EII,@,@,BL)3w!c'OPQsRSTUU zzQ>uzzl,WXX qCx JAq!QZA&J UQYJMq:~/Aa*nWXFX.YZ [E KKAq:~11z>A3EFekkbgbnbn oE U EE%*/*Q.a 8E LLvJ ;S]gkl]l mE IEkkmsUYY['89O 5/ L / Lctjjs-tjjst t |dks|dkDrt d|d|jdk7rt d|jd|r|dk(r|S|j\}}}} } t||| | }||z| z| z|dz||z d z| |z d zz| |z d zzzz } tj||||z d z| |z d z| |z d zf|j|j } | j| tj| |d zgd zd } tj | d|||f|d z} d | z } | j#|| j%zz } |r"|j'| j'| |S|| z| z}|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)rr r!r"r drop_block3dr$r%r&r'r(rrr)r*r+ max_pool3dr-r.r/)rrr r r r r0r1Dr2r3r4r5r6s r7r=r=7s& 99 ! ! #EII,@,@,BL)3w!c'OPQsRSTUU zzQ>uzzl,WXX qCx JJLMAq!QZAq)J UQY] A 1z>A3E!j.[\J\2]abeoaorsas2tu vE KK Aq:~!1z>A#5q:~7IJRWR]R]fkfrfr E U EE%*/*Q.a 8E LL ij*j-Q[eij[j E IEkkmsUYY['89O 5/ L / Lr8r#c VeZdZdZd dededededdf fd Zd edefd Z de fd Z xZ S) DropBlock2dz# See :func:`drop_block2d`. rr r r r NcZt|||_||_||_||_yN)super__init__rr r r selfrr r r __class__s r7rEzDropBlock2d.__init__qs* $ r8rct||j|j|j|j|j Sz 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 rGrs r7forwardzDropBlock2d.forwardy0E4664??DLL$((TXTaTabbr8c|jjd|jd|jd|jd}|S)Nz(p=z , block_size=z , inplace=))rH__name__rr r )rGss r7__repr__zDropBlock2d.__repr__sC~~&&'s466(-?PPZ[_[g[gZhhi jr8Fư>) rP __module__ __qualname____doc__floatintboolrErrLstrrR __classcell__rHs@r7rArAlsS%S4e`dcVcc#r8rAr=c JeZdZdZd dededededdf fd Zd edefd Z xZ S) DropBlock3dz# See :func:`drop_block3d`. rr r r r Nc*t|||||yrC)rDrErFs r7rEzDropBlock3d.__init__s J5r8rct||j|j|j|j|j SrJ)r=rr r r r rKs r7rLzDropBlock3d.forwardrMr8rS) rPrUrVrWrXrYrZrErrLr\r]s@r7r_r_sG6%6S646e6`d6cVccr8r_)FrTT)rtorch.fxtorch.nn.functionalr functionalr*rutilsrrXrYrZr#r=fxwrapModulerAr_r8r7rjs 'ko+ ++),+7;+JO+cg+ +^ko/ //),/7;/JO/cg/ /d n"))8 nc+cr8