%`i>ddlZddlZddlmcmZddlmZmZddlmZ dded e d e d e d e d e defdZ dded e d e d e d e d e defdZ ejdGddejZejdGddeZdS)N)nnTensor)_log_api_usage_onceFư>Tinputp block_sizeinplaceepstrainingreturnctjs2tjst t |dks|dkrt d|d|jdkrt d|jd|r|dkr|S|\}}}} t|| |}|dzd krt d |d ||z| z|dz||z d z| |z d zzzz } tj ||||z d z| |z d zf|j |j } | | tj| |dzgdzd } tj| d||f|dz} d | z } | || zz } |r)|| | n|| 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.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_) rr r r r r NCHWgammanoisenormalize_scales n/home/jaya/work/projects/VOICE-AGENT/VIET/agent-env/lib/python3.11/site-packages/torchvision/ops/drop_block.pyr&r& s & 9 ! ! # #*EI,@,@,B,B*L)))3ww!c''TPQTTTUUU zQWuzWWWXXX qCxx JAq!QZA&&JA~U*UUUVVVUQYJMq:~/Aa*nWXFX.YZ [E KAq:~11z>A3EFekbgbn o o oE U E%*/*Q.a 8 8 8E LvJ ;S]gkl]l m m mE IEkkmmsUYY[['89O0 5//// / Lctjs2tjst t |dks|dkrt d|d|jdkrt d|jd|r|dkr|S|\}}}} } t||| | }|dzd krt d |d ||z| z| z|d z||z d z| |z d zz| |z d zzzz } tj ||||z d z| |z d z| |z d zf|j |j } | | tj| |dzgdzd } tj| d|||f|dz} d | z } | || zz } |r)|| | n|| 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 rrrrrrrr)rrrr)r"r#r$r%r drop_block3dr'r(r)r*r+rrr,r-r. max_pool3dr0r1r2)rr r r r r r3r4Dr5r6r7r8r9s r:r@r@:s[& 9 ! ! # #*EI,@,@,B,B*L)))3ww!c''TPQTTTUUU zQWuzWWWXXX qCxx JJLLMAq!QZAq))JA~U*UUUVVVUQY] A 1z>A3E!j.[\J\2]abeoaorsas2tu vE K Aq:~!1z>A#5q:~7IJRWR]fkfr   E U E%*/*Q.a 8 8 8E L ij*j-Q[eij[j   E IEkkmmsUYY[['89O0 5//// / Lr;r&c VeZdZdZddededededd f fd Zd edefd Z de fd Z xZ S) DropBlock2dz# See :func:`drop_block2d`. Frr r r r rNct||_||_||_||_dSN)super__init__r r r r selfr r r r __class__s r:rHzDropBlock2d.__init__ws9 $ r;rc\t||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&r r r r r rJrs r:forwardzDropBlock2d.forward(E464?DL$(TXTabbbr;cT|jjd|jd|jd|jd}|S)Nz(p=z , block_size=z , inplace=))rK__name__r r r )rJss r:__repr__zDropBlock2d.__repr__s:~& j j46 j j j j[_[g j j jr;Fr) rS __module__ __qualname____doc__floatintboolrHrrOstrrU __classcell__rKs@r:rDrDrs%S4e`dcVccccc#r;rDr@c JeZdZdZd dededededd f fd Zd edefd Z xZ S) DropBlock3dz# See :func:`drop_block3d`. Frr r r r rNcPt||||dSrF)rGrHrIs r:rHzDropBlock3d.__init__s' J55555r;rc\t||j|j|j|j|jSrM)r@r r r r r rNs r:rOzDropBlock3d.forwardrPr;rV) rSrWrXrYrZr[r\rHrrOr^r_s@r:raras66%6S646e6`d666666cVcccccccccr;ra)FrT)r"torch.fxtorch.nn.functionalr functionalr-rutilsrrZr[r\r&r@fxwrapModulerDrar;r:rls ''''''ko.. ..),.7;.JO.cg. ....dko22 22),27;2JO2cg2 2222j n")8 nccccc+cccccr;