9iFSSKrSSKrSSKJs Jr SSKJrJr SSKJr SS\S\ S\ S\ S \ S \ S \4S jjr SS\S\ S\ S\ S \ S \ S \4S jjr \RRS5 "SS\R 5r\RRS5 "SS\5rg)N)nnTensor)_log_api_usage_onceinputp block_sizeinplaceepstrainingreturnc[RR5(d2[RR5(d[ [ 5 US:dUS:a[ SUS35eURS:wa[ SURS35eU(aUS:XaU$UR5upgp[X)U5nUS-S :Xa[ S US 35eX-U -US-X- S -X- S ---- n [R"XgX- S -X- S -4URURS 9n U RU 5 [R"XS-/S-S S9n [R "U SX"4US-S9n S U - n U R#5XKR%5-- n U(a"UR'U 5R'U 5 U$X -U -nU$)a Implements DropBlock2d from `"DropBlock: A regularization method for convolutional networks" `. Args: input (Tensor[N, C, H, W]): The input tensor or 4-dimensions with the first one being its batch i.e. a batch with ``N`` rows. p (float): Probability of an element to be dropped. block_size (int): Size of the block to drop. inplace (bool): If set to ``True``, will do this operation in-place. Default: ``False``. eps (float): A value added to the denominator for numerical stability. Default: 1e-6. training (bool): apply dropblock if is ``True``. Default: ``True``. Returns: Tensor[N, C, H, W]: The randomly zeroed tensor after dropblock. ?4drop probability has to be between 0 and 1, but got .z#input should be 4 dimensional. Got dimensions.rrblock size should be odd. Got  which is even.dtypedevicevalue)rrstride kernel_sizepadding)torchjit is_scripting is_tracingr drop_block2d ValueErrorndimsizeminemptyrr bernoulli_Fpad max_pool2dnumelsummul_) rrr r r r NCHWgammanoisenormalize_scales Z/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/torchvision/ops/drop_block.pyr%r% s& 99 ! ! # #EII,@,@,B,BL)3w!c'OPQsRSTUU zzQ>uzzl,WXX qCx JA!ZA&JA~9*_UVVUQYJMq~/AanWXFX.YZ [E KKq~11>A3EFekkbgbnbn oE U EE%/*Q.a 8E LLvJ;S]gkl]l mE IEkkmsYY['89O 5/ L / Lc[RR5(d2[RR5(d[ [ 5 US:dUS:a[ SUS35eURS:wa[ SURS35eU(aUS:XaU$UR5upgpn [X(X5nUS-S :Xa[ S US 35eX-U -U -US -X- S -X- S --X- S ---- n [R"XgX- S -X- S -X- S -4URURS9n U RU 5 [R"XS-/S-S S9n [R "U SX"U4US-S9n S U - n U R#5XLR%5-- n U(a"UR'U 5R'U 5 U$X -U -nU$)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 rrrrrrrr)rrrr)r!r"r#r$r drop_block3dr&r'r(r)r*rrr+r,r- max_pool3dr/r0r1)rrr r r r r2r3Dr4r5r6r7r8s r9r?r?:s& 99 ! ! # #EII,@,@,B,BL)3w!c'OPQsRSTUU zzQ>uzzl,WXX qCx JJLMA!ZA)JA~9*_UVVUQY] A 1>A3E!.[\J\2]abaorsas2tu vE KK q~!1>A#5q~7IJRWR]R]fkfrfr E U EE%/*Q.a 8E LL ijj-Q[eij[j E IEkkmsYY['89O 5/ L / Lr:r%c h^\rSrSrSrSS\S\S\S\SS4 U4S jjjrS \ S\ 4S jr S\ 4S jr S r U=r$) DropBlock2drz See :func:`drop_block2d`. rr r r r NcR>[TU]5 XlX lX0lX@lgN)super__init__rr r r selfrr r r __class__s r9rHDropBlock2d.__init__ws" $ r:rc[XRURURURUR 5$z Args: input (Tensor): Input feature map on which some areas will be randomly dropped. Returns: Tensor: The tensor after DropBlock layer. )r%rr r r r rJrs r9forwardDropBlock2d.forward.E664??DLL$((TXTaTabbr:cURRSURSURSURS3nU$)Nz(p=z , block_size=z , inplace=))rK__name__rr r )rJss r9__repr__DropBlock2d.__repr__sC~~&&'s466(-?PPZ[_[g[gZhhi jr:)r r r rFư>)rU __module__ __qualname____firstlineno____doc__floatintboolrHrrPstrrW__static_attributes__ __classcell__rKs@r9rCrCrs]%S4e`dcVcc#r:rCr?c Z^\rSrSrSrS S\S\S\S\SS4 U4S jjjrS \ S\ 4S jr S r U=r $) DropBlock3dz See :func:`drop_block3d`. rr r r r Nc&>[TU]XX45 grF)rGrHrIs r9rHDropBlock3d.__init__s 5r:rc[XRURURURUR 5$rN)r?rr r r r rOs r9rPDropBlock3d.forwardrRr:rY) rUr[r\r]r^r_r`rarHrrPrcrdres@r9rgrgsR6%6S646e6`d66cVcccr:rg)FrZT)r!torch.fxtorch.nn.functionalr functionalr,rutilsrr_r`rar%r?fxwrapModulerCrgrmr:r9rus 'ko. ..),.7;.JO.cg. .dko2 22),27;2JO2cg2 2j n"))8 nc+cr: