o )j.@sddZddlZddlmZGdddejZGdddejZGdddejZGd d d ejZdS) zM This implementation is adapted from https://github.com/wenet-e2e/wespeaker. Nc(eZdZdZfddZddZZS)TAPzG Temporal average pooling, only first-order mean is considered c tt|dSN)superr__init__selfkwargs __class__j/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/modelscope/models/audio/sv/pooling_layers.pyr z TAP.__init__cCs|jdd}|jdd}|S)NdimZ start_dim)meanflatten)r x pooling_meanr r rforwards  z TAP.forward__name__ __module__ __qualname____doc__rr __classcell__r r r rr rcr)TSDPzR Temporal standard deviation pooling, only second-order std is considered c rr)rr!rrr r rrrz TSDP.__init__cCs(ttj|ddd}|jdd}|SNrrg:0yE>rr)torchsqrtvarr)r r pooling_stdr r rrs z TSDP.forwardrr r r rr!r r!cr)TSTPz Temporal statistics pooling, concatenate mean and std, which is used in x-vector Comment: simple concatenation can not make full use of both statistics c rr)rr'rrr r rr-rz TSTP.__init__cCsP|jdd}ttj|ddd}|jdd}|jdd}t||fd}|Sr")rr#r$r%rcat)r rrr&statsr r rr0s   z TSTP.forwardrr r r rr'&s r'cs*eZdZdZdfdd ZddZZS) ASTPzx Attentive statistics pooling: Channel- and context-dependent statistics pooling, first used in ECAPA_TDNN. FcsXtt|||_|rtj|d|dd|_n tj||dd|_tj||dd|_dS)Nr)Z kernel_size)rr*rglobal_context_attnnZConv1dlinear1linear2)r Zin_dimZbottleneck_dimr-r r rr@s  z ASTP.__init__c Cst|jdkr||jd|jd|jd|jd}t|jdks%J|jrNtj|ddd|}ttj|dddd |}tj |||fdd }n|}t | |}tj | |dd }tj||dd }tj||ddd |d}t|jd d }tj ||gdd S) z x: a 3-dimensional tensor in tdnn-based architecture (B,F,T) or a 4-dimensional tensor in resnet architecture (B,C,F,T) 0-dim: batch-dimension, last-dim: time-dimension (frame-dimension) rrr,rT)rZkeepdimg|=r)min)lenshapeZreshaper-r#rZ expand_asr$r%r(tanhr/Zsoftmaxr0sumclamp) r rZ context_meanZ context_stdZx_inalpharr%Zstdr r rrRs(*z ASTP.forward)r+Frr r r rr*;sr*) rr#Ztorch.nnr.Modulerr!r'r*r r r rs