From 9cf1abaee6cf9c4c8e1a71957f16737ca562e96d Mon Sep 17 00:00:00 2001
From: zhiboniu <zhiboniu@163.com>
Date: Wed, 18 May 2022 13:17:11 +0000
Subject: [PATCH] support attr infer

---
 deploy/configs/inference_attr.yaml |  33 +++++++++++
 deploy/images/Pedestrain_Attr.jpg  | Bin 0 -> 12531 bytes
 deploy/python/postprocess.py       |  87 +++++++++++++++++++++++++++++
 deploy/python/predict_cls.py       |  21 ++++---
 4 files changed, 134 insertions(+), 7 deletions(-)
 create mode 100644 deploy/configs/inference_attr.yaml
 create mode 100644 deploy/images/Pedestrain_Attr.jpg

diff --git a/deploy/configs/inference_attr.yaml b/deploy/configs/inference_attr.yaml
new file mode 100644
index 00000000..d11164ee
--- /dev/null
+++ b/deploy/configs/inference_attr.yaml
@@ -0,0 +1,33 @@
+Global:
+  infer_imgs: "./images/Pedestrain_Attr.jpg"
+  inference_model_dir: "../inference/"
+  batch_size: 1
+  use_gpu: True
+  enable_mkldnn: False
+  cpu_num_threads: 10
+  enable_benchmark: True
+  use_fp16: False
+  ir_optim: True
+  use_tensorrt: False
+  gpu_mem: 8000
+  enable_profile: False
+
+PreProcess:
+  transform_ops:
+    - ResizeImage:
+        size: [192, 256]
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        order: ''
+        channel_num: 3
+    - ToCHWImage:
+
+PostProcess:
+  main_indicator: Attribute
+  Attribute:
+    threshold: 0.5
+    glasses_threshold: 0.3
+    hold_threshold: 0.6
+  
\ No newline at end of file
diff --git a/deploy/images/Pedestrain_Attr.jpg b/deploy/images/Pedestrain_Attr.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..6a87e856af8c17a3b93617b93ea517b91c508619
GIT binary patch
literal 12531
zcmbW6cQhQp+wWH|QKBV!kG6>3OC)-?SgaDgtP-6Fi4Y=db%Ko&Wp!e)T1d3j7h$a~
zdJPga2)X&a?>+aPbN{;cK67UNnK^UjGjryd=b7){*}p4*yZYLC+5iFq0D$0M0sLJ6
zJOPlCl9G{<kdu*-QBaUmQZZ6f-MK@>LeD_U$id3R$-&Cb&do0_$jvLt$IdP!FDxn{
zDI+7pC8(&XAf+NMEhF`xNeCz?D5&mGF;i1BOYyMtNc}(C-!1?hIl(2Mjfj93KuAYG
zL`U$q55V<rog@VRWq|)_1cXGyB&1~I6qI-V4M5)o5E2j(5fT%TkPs998y)qpA3#h;
zLeC?qLCRp}M8+GyDD^t6h@9^+s+$S)?I*vqb6^Yw<-PmNEUXU%1cih}WMt*!6%>`8
zXliNe=<4a4TUdgvtZg7Ju5Rugo?g(P;E>R;@aGY+Z{p(LCcJ~CXJlr*&(6t36ql4D
z%gQS%s~Q@c(9JEaZS6h1eVG1%!J*;FDcm%EW_E6Vb!~lPb8CBNckkEn$?4ho#qZ0j
z|8Nlii2fVvU;E!+{|6V{KQ2OIVj^O)|8NlyhW)EVbi^b)lBDz+W@JtQ47^gW$r&G~
z6`{H*_@qHUnVbW^QQqU1S$**9KWP6&_J0Q!^Z!Nmzrg-C*F1oVh~VGh5zztE0VnJ(
zNESyOnNv2DiEswO$3G&yav0{FN4RA2u>?)Wu>EnHr4rIOoq?A2(9OTaqBHAgInR(=
ztkv|%<kcd@?er>Mom%v%vmBCFe*t-xzTksG<M8hOr+ymy_*!DnA_7?rhs4h_QV|@%
zPp&32P?A<2N_>jt35itPqDx(jHDSINe6qV=$bN3+1vj^P+^0v4kG3ux*fmt7%@`L-
zZY3buyoJH1ytQ4ip`(9hu@U*>y*VMkzkntkkpYGfv-!5#XI&Sp4prc94!XKMC?-za
zw+V&aF7qSH8RE0Lyx%~}R<N7hwd3@|-&#jSkxHq(iiUy0RS9pkO70oD%$GF74g487
zzhp%m8MmR~GHLaX!}%?fXm#if{S4$7;ehZXd&Zmf0_f-vj$7`6ggHHG5B?U)@Ebgy
zU^ZnQ)qCfU+8KPm^=?E+RXU0dk+CV07&6ZGs@ARY!SiZqyQX^Ww@D>T*7c8|kEVbA
z@i>suRpNS&4yhPLctt)O?BzehC_hHu_3BC+&BJH(CBu{DfmC#RKm$)Q;vWR$B|)ez
zqDonhYmvWz+vh1`?G>jV$OTTES}1SJ^DI9JM+xcDh|^?Ac2x~7yqL<p#)HK3vd#ps
zdOk6>sc&D;@K)u<IZTn|@??;`)(lhLvq%xx8s#J6h%+uLb|6t;2k)HlAF9S{Y;&IB
z?)b{v{Zx9yt9!-<dKvYRMQ5fnP}&d~de|Rc*YYU&yY)}(Quc78O{~ap2VFtiS<Swn
zi=C1&q~O&#l_6s#Q7CXaQp~Rz5vi}XOIl^^*?eCPH6#ErW>c*$Zf>G0PPpCq0mOb#
zg_`OvOnK;j-`a(QF+35cA*iu-;--Q%@%sD>ule9vIh7bDZ~{_qkETVs*<pMxsO2-M
z;1bqX;KcsW6Pu~Jo+uG>$Ko3QTg7B1ltdh0P<ukzMWFGIth0I}o%%dB7;;u+zo5sT
zb2GbsQs-haQ#5sVK;$0FxSeySD8mcs(SClkj{G{aq1A6y4cXDqy088665sWx`y_vW
z$D;OA6|lj$#f{kZyNIa#MhAO-M|x?j<^~D>DHA?CV*JNBFPKXu4~Kz*WNO!ri!5%B
z^bzo_yw)QK5u29&W0zWKihbk0JVHdMl*rxZ)y<!c8t?7fFmX`oJV**wa`k+#+gmA6
zKQGnwhQm9wm%{z^YtlTkz^pX~(hL<370O3CwH9q{+&(GHs@}!-B<@++Ag$LGRk5C)
z$94-23G|PvCFg%D4Av|usZ0I57Wcxwc+gx<^(M0F&kay%cncTof!r1OrqXVj_Hwdh
zG&-#}xwSfU9&<`Bg>G+3c<I@*0vm5v_lg-YMJpgA^FFyi3<OHT2%(Y-?TZgbIs`0#
z?K@~`Rql1~KoT+aJ$lok+h~(j|I%1nIS1~kPsw6i)csNWIUmgMXUq<hTZ5mty7Hk9
zX}yNuMXoKaw_0}bA@yG5b8N$!TInwI-a7y|ut8P@4|B&4#U$FUM`>lQiJMSX@^X*8
zDEN=a{AH;#o<gl?0S&$%H!VqxDC~08EIhoGaFwNcqQ8oBB?xIN>iF$+{8uU*O#G=s
zI8~a{r%WufY;40sdJ!u%OIfeSnRcAkCROH-4RJI6c)I*5xq2=kOpu{s?RA;-e7&<j
z8Gs7Us_i<|&AHW#@Q{&VtxS#53S|xAu7}6kjour@!UJs2+jN=8SIn4-v{Q0nrk52&
zuIAaWBKji%IKmNGMn*~loo;>LFA^;n;$;KJS-ZX{efPx-B6s2AWa-Ajv76-Qp3euK
zx288KlLU2^r}1$|Mu*Gl5xEqsex`h<At->fzH-lR5<0Ke%}<N~{qR~mqXbl0eeV#9
z9fST*ge`+PbgQ@zSyXq&lxQ-u#y)qqZ_hhlvmMynb@5tPJKIIrkDhx%=o#xbLYP+c
zT^rkMTbbXQPs#2%u>GP6yLxO#_~ha6UX(Ys+IVc|Ot@(}ccJ-nwVnj*!P~+$-n!WV
zZX0AU<&T$6`*uBh&G)N7f;@u*MOgFOIlPPzCwVRBm4NblyY*+N8m`=in3nb=&o|$o
z($dT@lnPx2Y!rvT)~0_tw@24Q`<>YFHl(o3`-6jR;yIzav%iv19c_PsXa+dr2i{!r
z4OdQVucdjS&sfLgJ9FD#Oj~VX3T>G~oR2`Zs?vY{0+4P4$IX&@$l2jP_4^qRDO;UR
zPJU%|`1MTV<HSvsh%A?Yw9mq9;gyS8N^9XWp=!TPCABdQv4q)*AZ5;JV>x<P$tU#;
z{nFqPm#zad7s9+G*ii8ygz15HQl7<XRFw~r7EAe$t^CbNqOql9r2xe2RF?!{1YQ$n
zx2l9cLx0Wi<qAsngtcjYbV<vb>Jo!*y_aA`F2KHd8Zh6>b_!$+kria#HJ>kjM&0Un
zinX)c@KCg&;MW;)<IKvbZaVOrp19OGa*5rwxr(cbVF_2=FRr;SJg)7lTO5!a5hnZW
zNo%1NPJo1MFg5l7Q6yTdpf9x{?*N?mL+9eMo<zEB)Pd=|sic0!OGV$RQV2N)3I@$0
zm4OX?RubrspL1~!?Yb1X%uK(O`+buZyXhTBwrmHh-saYZKmH5wTY_ywcllWj`~}o~
z{5<1VM^4SIYhov~F~g=jHTC!lx&kBCk=A2l3a2N%6XpG(sSy+=Z#+G@ymSBi9Fb|d
zRK0XG#i5b&S89}rv9x{o3%=y4cHB&y&igOc{iF)j8pUyx`d@k2*V}5Y*-D-<03YVi
z8lIOKDUWUDz-eMSTk){ht7DmYgWaOYlt3?PmUUPPmV2FPL^wzq38b$e{lLw=5yGh1
z;2|m@q9;Kh1Ji6y+Dau8TeMus2)?WM2Po2iP$Q22A=2Kjeafet-5C)Hf-H7(U>bn7
z!=F<xYTRqg`Vz$T{sK(@0<s>?9lqL*y|r8x4fqT2H}7cu+KSGWO;n*7ekf%ni1yju
z<jkpRo=8Z%t9AitYE=PG%*I%Qb^PnA8aex>=S1tA>^f+F2bp=+vM#kfchTkwdlh7_
zwjH|2F8LSWI<0^bNVaU~P-?i*l7xlvwhyHE-)!)_ZLJ!2a}#*7AWZsZUSz1G(55tn
zR54dA(<Df>>#3a4?^EVl<t6d{<q0fXlV%i743Elf>r&)+#rh@8nceafIL_?D4jy<D
zY2ds1^sepq;RljJ!o0$J<zG~*B~H()X6}8nqs_NW*dZURv*~V(2x^93Wz`0MziW+A
z)M{<XmGAG{a7<p#8rS$aqXj#{2FOHvP+~F9$>RUKSbIp-C0e^AE@nZ;9z=Ed{8TmI
z0220!Dbg%Z?o$SVQ&KnM`U%sxkT<Hs<3Vu*$E&{T{TIp#O}{(P@jQdf{B#pmX+pHp
zN+&FejOo~*FyKgt{0F{fo64Qg3bP3-Ed^^>MmtLU@#z{O>cymMh6&}PA@+zF3&*+P
zd0QB)kI<{3jAljKGiAIzs=2<Sb<g6oI`m5MdD!Amt4F5(G9#NJ)R~XIc#kAnuawzg
z)8wSQjb7YzGs?RtZXInRu8{4>JZ~kdW0kyYYtmGs#Si9CAZyW|w06%LErP@I;cljE
z--+GZUy(m;_^wvSpS00VM4!=<hNIQrru2z)&u3cjx=3B-W-IpXRlTW(1t@2<?TuuW
z`!P`kCMNLazx+|b?y_8n4KxVy{1Lumrkm7kXx@c=Qs3Frui!E<W5+aF`lHUarA4of
z$zBiioI~2y%jFph=>yllfY%I`(blF~(e^K3op+w6RbnE7D+SOhaINQpAAMWRbNtS^
zbXZ8Mm!R#KGCI*(zs7H=C*WBN&AVOk1Cx36zkCv$w_2piv#q8RY-%U(D)DgKKr`RZ
zyf@aGhJV)igHAYv?BwRPh@0RGKJnh*6;2QIR?WQG_=6}^g4hYkP-`(C3$qd5Ki4<m
z$gT_xT7!exbZc@?4fdTO_^0S@_zyA?b*1eB_PuhrNg(0tc22LFuL87C=b^Dl7WNP;
zW|?2v8xdAa`o^M%NtXO{RN^8UGi63Xl<(35w9p;d$CsWgrf1JIAd_Vq9^k5&Rus^#
zO;^pGBdY4=3)~L4q#Lj-yM9*t!n33?aVUv^dE+%6Nu)p)9W)L-t^B6PX=!JkK2bxj
zuuj4H=8*#7dr=|`6>XVng+tavy6$S`;u{YUW05-<TRl8uL9wvZyjxk#LwTc(yy$=!
zFB7M!h{ek1EG?}I$^JA@^!PZ1!`7!g8qO7u-`W&ae&7z+@aRQ_0YWfpW&b4Vqvo4P
z{Pu|KH=px5velcJ>`MDNhU2jhsjr%(?Jsiv0+v3hd7IZMbILUh3^z2~tn^+^ZhMtW
zU_+k-vHbAX?r}SjkY~W!Hbsh0;aCS??r*(r=|+OAR<fuE4mYgGGGZq?BS1!+s^R-V
z8dv{raG5MaaILXqYbAr~6-babkCc|_G3lePPfQ5oHv@E~NQITD<>dCpRP~ou(`BXB
zFP<f9T%FZacPbk-z?N;5=N@D`-Ivr=)E<9kW~(^Y6dQYB@nxL6{Y5P6kxL$8yIRk9
zS;wATv!w0ucf<GlKIw$-3fq^wy?CtGd34!BRGf2a?B0ipE~8}(i?zJ?P8d0@@O6wh
z!@Zfz+?G`%&hz76ern^y=lcGm<Ks`pfMFKjN;;F)8qygN;*p=)fi$WS{dmc94vB$>
zfp)JiDZ#(o#%!=nY92qE6`3-#bbuwgV})Y5Z&d=dq;*7?LKJw5RVdpk_8ImXYO*Pq
z-Iz)4vs_IJ`g~&Qc<5{FHGf5i>XDbt_rC6ORk=4*E!tO23&q+n=k$3{c82d8y`hWP
z!<IJP2l%Ujw@`+;&Oq%bZTG(boumXXJNya^>KoQm$(#9*04eywlznR3?NSRFv#y|#
z(m}r9S`hsU_<Cg(Yv(HdgHv%0{c2U{=~3ZW>jMts(?PokQ)9VnM38gp>3C6XNgMs_
zd(YQ^b?(rzSrUVZVF}yV(@KlWG}h`o<C1cfnR25!zR`+LLFL+uoswpQp=Y~B=)hmJ
zAN<)VLvU`Zj^%0CU?lFuwy_@QT!yuXG2NlqQ<~1&aL=K^$`p8YO>}w45c2x|1@!2w
zo2ekxE?LeiU0JFE=WA*Ug3c<Nk;NOXOk+WX`k59IkL$IWlqggGrGgs<c~m&#4MeE%
zd{QO3FzphOiTxAWM4@y<lPa|2t0S%K2k2c}7N(Suf)d8|&V}Cc91Fl?K<jmo^|_T)
z6ZMd5K$G9WX`P4QXz1g@&q2nE&(jZYpavD1pcjs;@B3x%(kPbg3l3W+2DF7=)a>{F
zYC}^%LZv-`n}rbrv#MW3zSxdBcC36m=h<~|-!2a_g*jCq4pj;wENDX&`DCVWEY<Gv
zy~00gVdXr><%_+^G4JvFZW-eL+%yzeJvbXp8evt$0wE)w-gvfa($3qa5`=XSvr)2W
z<3GnhJrwHZL0j*(tddGCv(oQm5j$DGMEiJ*0B*A&SF>ZUvyVPKF`HYidQE&3qvYNs
zrL_`(Q8^Z7q2LfM3N2_t5U}QEnS(gJK1XTs)`_3-Mh6DylOcuJw{oe#D;C<NL5O#i
zF$87?BP54WP_65=M)Zf2#;#GNzW`c%L!~lbgxOZQw#Bb66)g;XT{?dL!x06ZQ@#MW
z&`^rv_+J2|ksGl#`~GpDv300Gd7?Sa_QHAH`B3aaXLGV+(`s2&5!7Uy(9RnNQm7gE
z6%anW8ph4+097bW-T3T!HJz~81!x&owt529ACTc20cK9lTWs{rapvKEzq;J|7jt+%
z2}N<BbcYXSInnBcg4N)c-N8y=o?4o4TR#<v@lHjVUF3BY^9$CUS~0Ov5eHepFG@J9
zhcCl@E%Jd+STicIp#qVyeu}9>-ozZ{XhrU6+J^MLFRSArm}mCOwhIJXiv&COGi<UW
z+U*ZC!SkJCK>bv+q?pXMrIB_6k`F_wHi78Y;x^q)SI<pQ7?8-`(Q!Drli35d(_^sI
zs2GsiRHtI8T&rNm@hwZ|3}c5}e1r+^9Ag~5*SI)eD84Gsz0>mvSQ85z^GbeRM!m8D
za-+}UOBdSs1xtx6#27vwqw-X9>5meo%E|${DE#qUiajvJ_-^Je<mOx<a)_35MeQ~$
zd_lft1x<Z9)4PTG+h;6lbL?+UseqxYf2u=$uEC5bx-z4*A?{l=Heq(Z_m7sku+3-6
zCw_Dd8l~AH7266{-2<z(S6S)vOEx3-|GXHdY&N<03_EeomMcdBXNS=(-f#Ou>y*+U
zX`ap4(MH|b+ubf?jJ4)ym4>Wm+u7jhC*wTJ6t%!G9$Pl%r=wcF>ur(WzHmYG<Q2HZ
zG&a<cwkY8SpaBNVoGQT?U*6f&jx&|*_{Mv&7-0E9uilPWN+(&WTt*A&uwcwNx%vRS
z?hOaoQqQ5-dgX3yq?WUtW;_JXYL;~L6OF$fyW|YMC3X!|Lw~A(zH%lMoIP>bA`iU3
zZymE)SLXl*zE>p)vP#ZY^6WIYJJ5OldZDcr%j^4-**bq{HaRQJHQoTD0TF8-d(|{B
z7us+I%L6*Wt>nDCN^-KU?@}w*Mo<)(h{e}q0d)GFW#nFVKfU;p?!H%#D1>=oc?UvD
z3F8>oBqwu4wO1^{7iY05Q|t$z<(R}6E9qyOQj4{%J4(1uJ_2!TyIB?7X8irer3L7q
z(D^aw&Q95-vEQT6$VHEea>JtlH>T2T8ae%%H>lMYDWjC=O3dbv%Ey<x<b1oZYH!aF
zg+p7fIWjPZj)T_o5G$Z6^cjK&N=$u`SojpuxwWVta8Ki+3Pt1>BikbV`diBZZxeT#
z9aWF%b+h-E%uft4O+J0eY-Q>lsbU7_R(}Bn11Du)2)fGFmutp^LfQ}yfd0g>jh`4B
z?4n991IE!Ws{BpXE&Oaj`OWo@I^#vG)7JzoCGU0)@#b0US)-`4nr!;KGiJinYr)g}
z@p3RB7fWXfXEI3RmJqR2y2)da<RY$Yv1(;PH}h!KYS#TF+1`<5z9|aUgaOl1{FL~b
ziHkM3#$}p;tFQD`;czp^5FW;qeBa_3Y&JAsGHH3zJo-7j;H<bjS<BUqCynuAZ{;vz
z61KCX&5_AR!|YO2cltaXa_@eI?npeAGQilyu+LYX1&QVMVbxAB!zdgJ=Nsw4#mwk=
z{Y09Jq%f{?>eBusHe}+AH_KZM57)g8DQV0iSPlIbLkm`qB`=^`6MEPuzJ5H!DQo-Y
zE&AHExmP^|<<*s!?|LcGE1zA2r)%8_vT85-B`2mH_T<a0^YIg;j5S3vBG2yZMyyGE
zeap1v!j!wavh)o*n4I3w@KgDpBOE-g9RFA;>vXPae7rRHxQ<OQFLtuDiTr0JAY&gf
zLn}Khs>w0zV1N21XVsb3-}73n@-IMT^9E?HxXxTUcj#TU{UG6PF8R>ScCF#J!M9Y;
zPiuz64hu}Alq*t`F-F$3Rc}l$?ga`vti+Ve?Aw6y*K(22$Og~6c%m(kNYdonG`-DK
zb`P|n-$Gn6smsmJKx*TvUvrCkyp_Xu$&gIhGI}M4gS|_eChT+DvMBcGq`5sHy$`z8
zk(&R$6!*e>oAAioXO1(c1KwwsB??&EmPmLkzO6z~I6PkalKF0sJqaQ++wX-3(|6;@
zfe)ZU;YcDxs26<pYO+ih;}eLQD@)5T!7zUm=S^xl-!KOZRgweDc{McyOjV#tmKl>D
zUup>`G;l9fY6yIE7r{+QGRdQ=aK59lBW};yBk<+DyKDzanQvb1ng?+et+g%8wesOx
zZrPNh%0VqY&;9aJApZia?dn*tHd(^P?bJ-2AJ?Xpy7-INlZ|wSf>b{kBP44d(pAK-
zZypGnoo+Sq1j$sz9&t+AWtL7&9Hbkuwg;KyvRpK2Qh)gTG9>I3zBIohPv@e!0`QW_
zHnQ+!tP|nYT41D|87HdqNcw2ks;N_H$|57`Y$g7iv2C+X88p>`Z%d(K>m6A5-m(SB
zzhvZCXVXr>OEq~>lOat0@JJ?SI<Az0UAU(q9TOK6CZ7@})FY)$%fdWZ%w}W{u-<C4
zo{j9z0K7`l7yUBaZ(EJ>5AV^;2eu#L6>N2jUZAbDbFVvM_bvNgg#H?G1U~jGYt1y0
zfQLRRPgSMu)Dd+u)OvaT3$c#4NZv76VhJnLV;<&7my%Ay*Lca(5NcxcI4M+afFSv-
z4^W+iuIqtL_dU7&aucRW*S?KIt|hgsCGz7^e~WBs&!t(LJuRa$1O;U|j?$k$4?jTM
z!rqe}I~lhXO_Lc?E>V^&^w`B7l`0}7Rh}FGW@1P;5h!lUa!xp4znbz1&~_~+F5I`|
zZ*_RYevZt{0$~~t%&axeKOj$NW>1FdyoLu4k#{oZM(cuX3urzk_uBJn^u>Cry}^5H
ze5kajv{Pg=DTlUr`))33>(D<lON|=;J;OMoxNE)%<68SIx8K<FX!FU*1Xw$36BDql
z!1ngeS>30BfHwxpDMA!BR3_ZFDiJfP-p{C-_vHF0R9Phk&T1rn*`2%L-XreMX7da_
zSSX%Se}2<@Y0lq(V7lr1mUe@O#JYVi<+{K|{2AhO{kR|)NjLeC64_v-4rxq*7=5%#
z(YGtFgs=|=QxPQ%Y*r%fwNj;xp#(#L={jf&@=1>9_2pWnOYmsEE{FFN99I%Fl*iSC
zvUTgYZ`RG0{iu9vz}$RyKDe6C9?L8l&rNk=)3XK)`r@!CuC!GyZ{_x7g5Qjd8dY(q
zx}U2&c#yvNdV-;q<X}gn<9sB9I8|NSj@HG@X6drN?8h^|kttcXr>Q-iLf#BRAG|V~
zKEJp0z7u=>JA&cQBYgV$ubPo-oizM0>70t)Mi^(7-`J|3?|U~zKe#HjG96IWfXWk+
zlx(CD{4s6R>&=P2kz}h~auYcRYPoW;tzTqBbF7VuLN(4~pCJmtJVBfP88?LJW-%WN
zOZ9%DZT0}i^A~_S0z2&yREYU>^fc&)(FRyu)QCiWl&66KWocZHmYXsv`sYFWjdl>7
zO^gtU74rZ9Y!Ck&!logyx<QmuD?j}H=`u>q{pD)0wV{^#*aL0mbJ-+)sH9w+jxfje
zJKx$P71dBUSq{z4+?B9--mNMS>q2l(a40EZv2!2yknetT&%8xf7Ng|j=PQ=K)mVN9
zzVL*n+m#kQcg^SwpR68IrFGAu@hmVuPaJA-_ngy-ie@c2Z&Tkcv8vePqBwy2_{zQ&
zCH3F8JNg3z7GB;q|G|2qjIz%7XkdPx71H%bQbMLfJYAf)%7Cznv3hIUgS7sozMG?;
zWsFvK>QD9|_g?L!$)Lpbo@T;!%FQZ{bM-TAw;nYA9LxQu-dsWsSL6AcpT3t>*)AxB
z4Y$R!Da;clpBwv=JywfvssY>XAG?t_?_pA)AwPIQPDGLnU8fhdyz^la6-PyB9E~<5
zcAnjbzVB`}XpD7QWmQ;fI|uKuIXhjHsDk60TBm&Hnl72kel!WDL%9Tc9pIM>K8I6>
zLvU%<gE;N95hZ=wY_<8SKHY`;T;Z@t9)ru~x;Wb--sEyT>ae(TQa905=SAps!|TGa
z(j>P=xcz*+n~F!CbBm>TV7r`rwCdGVJ7p1PaiDy^t*ZWcb@%k`>fl+mwz%AVSrSb5
z>%i*w<4B|BkLin8@xVvtWskLN{QUYUJ<{?Al`K8QmiIgm`=KxVm}`NF>W#cV(jS$i
zoA8z-EBcX!kvMF&n}?zz(9R_t(Fa;SFDw&$?W0rSbofFse4)Es<7LJxD$kehSCk_;
z%X|EnN$1v#c_jSGQQj0_GYALcPD@*f>H)GGE3;RpB*fzK8yqrLPL1i4s5_W!kK=xU
zqxSZ7Bi&g~Uvu~U+M&2IW3pOE&KA~!JEP$H5bT!DzwH;{v1=#$i|VLEg99E{e7A&D
z6a_E}K9~28N|w?YPezYvs`>sfpqCs+su+LsguwUX(wTFL1ivU?!qMh_+imgb5b0W*
zzQp4o%Z{1b?OnU<XtZ+fDX_Zwoxu>}g6q<(3*Qxf`wxHYb=qIRcT39cpmY^a(3HlC
znSFTzkjHK1qFQ=kbe!E^nXHSFU?mFLjv_2c-*b(usViq*NgF`gg$Za+yf^TSYmjhO
z$4l;*pk=T?Ctb5cpd9cmrBu+_R+*Q<x<I=NYD5eR=yHr5M_aYkqrN(wW3orNLM;M6
zLuGt|Ri+BU`X}3}L5)0Vd{9>CW#DB*a$YBq&|=9KlUKKB%XmDgz`72OPvyOw@y6}H
z(|Qh;2&)(<+O^o2tbOKAP5j+CCpdh7Y!7Iu%rH^}X8S4tYMAc;ogEJj?Cyd=m$4kE
z^<7hJNY(Dh{e^>r(syc>x$B!>SsOVksei=v1@H>7CF}q_EhVzpQVbfbw_}e$1C<z-
z`QAcEd4NxH*^B3vnP=U*7Lk=@0qo?=1*HWfwpHoUNgPIV=xX;wZ_RD?w}Ss(VUT?1
zbWW4YU`mmXfA0bwiTYNQc#*#5MSC#=n@`!$g8L^<gfhXa<!ZcH>5MlSr<_PgyFyRh
zS@?F09?q`6yYl0VZ<00)*oEWe<mQ-LOn)rW%d(KM2iL9ZInG-)Gk8)0GYbR)1H=1)
zwkCLIbW`&ETLOGrb^qSB5os{x6Ir!Y?%O_`&E*}UyX6J`&{8XWW5qF&xW!obqLiBV
z%;YJ5?4?y9u;2|x+II6YU;9*t{Bt)l(u0z2)%Fco-|bx6zkqw(2sF-msCZ{;zUVl6
zvgwTsW3{wX1n8TVO|%6_+!*R)CdnUwM?|D!9I~WPL@?)Q3)YBV_7YO5tI|cQj5pd)
z7e#-0kvdh9M;fXX-1v&IzFG-B<df|<KXrX=ZW=8sca{~?-ptmk*Pbx_O5RtYT8ru;
zMf~v-dO_Yz_n199zGhYS#{B!qF~on3KUI`13ZF8r;N#X}4S3JfzP$_Dbj~z>#vrPv
zRuqcg6_o4+i0EEme7#0d{lzkuwA=+{jo>M;L9ToHqOOZ`vB1uwT?n3m4bvUnvtCud
zds_XbPP3%4Dm}hGys{UjE7nECC+n0dv|?$`BEx&2ijlJ(0$E0vW%1)cenffSMrG;m
z)3gndR_eF$Q#Op!65|+{$YKk;l?o})szWso5W*m>g*a7ddlyvu<R__@E;QkF!q!E^
zoR_r+%8+O>nDZl7KBk^Y50EcA)ss!W3B*ehzAfe4HK&*cc9+Qv1YVhZ5~v5uW^`^~
z;S{D!8>|+}EthfeDvPdU#dS*?TF!4z$vRR!WNKo8_g8RG>pBy$9GSJ#cZ#5%m-u|z
zx2KZ`<C0n_)Gz(R+ALn2dGM00?LZ%2(aY1IZ0-UQa%+&Zofjy^OB+)Nj%s`_+AhR5
zt~HLVcqBKilE;JR=g9Dl1^sDZ?oHV*66GtgW+$&A?ZX9%ywey&zW6VCuv6ysVTYv-
zs^WVJ;1ltivjhduwYGqX?OmJD^Z~|^9D$&JPR-luBs&3iqeb8Lek<U_JoQ6ooc7`5
zCoGHkj<<tuGkXEGS$+cznal)>)+0gQYD0+uPISP@7__Ei5LeYn8NWe1nJ}HhFkd=P
z@v)8^6TsoQ&0-if1*Es;)H?$CRfcF<hum}eqfY~E{sqB!N<R(N;)XiPxHk~`|E|1e
zv}hTT&U0c;kd(%Ah1&}+zT)E7yeY_~Td~CCj%I&~NY-L5FYtl`Ek=Dn;bx6#u*4UT
zJb1&E<yr|7&BOq407L*tWiUYS;czRTxXg-pD?QxI!uQC)@3X&%z^OP1`i+4|?8OV*
z+JlzVhWua2RmxE!B%<ZAqOLI}pCH0gWl5c?E|F?4%`Ys@Yp>mQ$n>{-2>6+f>{S!}
z0Gyrs`tWXEn<A=mKEzM~KpOK*S3x!-Sld_$?DZ~3x`G%s7D$Bn_E}vKa8}bMWs3;>
ztsjF6-J=*+%i@%r%;1MRx6$F<f4@n^WB15!Et9rngO$*2*(T4`-!apj9^C*7hNp$C
z-Os(WWI(uta9QX#l_5Y?Gy^0eV!hy0HBof0o^*<6J(G`Y|1V&#24|P>YstuOEZ&yF
zG?{Yy7ZbKu{;Gq0h3OCU<0w?jz|nN%7-9Wgh=Q>+=HHgVjAyaZ3R}o54J=p?{Zymq
zm}3N=e}5!K7)BO>Vzx);CBoP#WVzKJXp=MXO85z5=H4{Roc8la1M3>!5dTx2>vVMZ
zu-$d@qmk7Ry~TlwOEPx6>^kiS4oTZaaSQx|V@HV!LnFIwzwt3>8Fs7IE>D?gb~-qf
z=pxQyDa{lD_iUabWjrz0U46}i(Q4u}aBoXa$C+VxX(dRteM>F4ex}&G8h>yD)iOn`
za9&obO}y&xqUy$oz~F;X!rfSILCv8x^Z}np32dx^RF7P?ll8z<$j4>PFSPzr7ZXTJ
z(A@c+UJCRCKI1U*nWH|)x_}#QYmc0#dZ!C!-U%?K0zK9a@@8G}V;MpYFj42Nej^I;
zS(z?=BP`IYEyh@OT~_H{JM2#(Yl!&rCRq$)z`$-H;<e{3U}vC+)w@S9v<s_Zo=&jk
zuB=ML1<vBW;ckjtxw;Efy_*}OewLgHDspCFcEoaLBdpx~2>V*A)80Y#B!HZ^k1Y_>
z{Jx4cNt$Z{yhVOw4+B+(*u;{dZLutB&bJ}_f7-_Ni!YbDtEy2*8O0*CuW9At76dm?
z)lp$%Yxg$n4^PdyVF_uYV^-u;{O^lXUgU`BJ;kcPGsbT7uDI{X#i@K&Cb2AjXH`lT
z_lBCeC9x)xjXu>hOr+J6BgJ;j6Ij+Gvm?+}JgIIO{AP-QLDQkc`$-ZyA1>7U$sr%;
zK6w(bAmy=0gHH0`iPv)LcG%PQb{YFVMYO;)&4AD~K*JvC{{#vc6@4*>gb{77oYmy2
zfoqlS#*ra1DPP+P)ClSiYp1b`SPybRlJiO^jf4&@66d2v7VOE)G6lDomLW#$X+lX2
zUv9j|eK;b2vY;|Bq4Pcll$$0H9HzwVi&M)-)qOH6n^i+@(eh3|q5_fTkMcI{>jk*?
zEB-A$)^}-WBdUxKvt$+;bo@<Z?E@8%rjtZ%s-7w|5oQ|Zzazi`W$)7s?r*Hn5LDmV
zYZcGy(WTp%maWE_|Ji@ycv|V>K?8KrN7F3i1lLqpMMhhhyRc$tRgGvJwUTmn6iS^;
z#6<+s8-yuL-VjL;yE<+}{4ykyNw=#-cC?6Ef|wTdZY?IHlQa_F-KA~cs(UBr=v+Z<
zXi#CG+)rqXIqoF!8f-p{(Mo%CAv?`b;NsvrgmoYOE?Lc>6#cmVS%AgdI|Ymlg8i+i
z$Xen1DlMEPa@S^Xo^J+W_fHuQrB>!+InpDdv##k%p2m48rTr7;d^XNxM|xxa%seT-
z<4Evf3bs&`s4S%4;S@`RNIv0w5+zcbg>BGEk$D;1V*c!1bAd4ZnB2x|K6r>KG>y6T
zPJV0VT`A~mW~}BMTK1!5zM>}LjZ+t&DtT2o5;CPQ&7<0AEo~9vtvw$Om|UMv{o#M_
zxCTUMC0o_LsN3q9w?2ttIyk{hrW9vrAeOARHsAdPxJw&#5+{ux?5J<Q$?Tuu72J3|
zGm99wf3I^h=q!b?ASxqjw~p-kUE#K=@&~Sk1oh1i7FLjU6Au}+r!=jXaqc(eM#fY#
z8`qD~>@9MlZ-?c7EGN;w{<E+9uAx{+g$f`NzW#_{-@@>-Eqvu_y495!JmC(~Ja(j_
z$+G)GZFo?SW%Ev;Zo@j4jo-NjJ}u+hs!;i5+5Fb-;>H!DnfOj#-wwz7TaI>4{rKTO
z2_UO(G>34`{Chx!mXsDrAyI8Gs}D+Tkl$4I2)1o@Uhjl&ZfZ!E3t!r|9dKdcpr7rk
zp>h;HfW?|-{1~n}W|t@Os=lKDClN%dZa1fkeQIIZ_?JBh=OsHIit>bSESAsInw`i8
zEH{bEYr4|;I#bLuyRydk>hJ2^aY9y{-Yxu9Q{@?KHe1~~-6<9ZHKl)PYV03XMKb-#
zT-jIV+jzL%EqXd!YVZ7f>#|@$kJH)+S^KVg8yVCbT<WQfdNbryXo$;wd)?DQxJ<h?
zMW(nm^L5+cHg_NS7qG(p7r>gBJP`k5o3Y4Wp^}y{rpa^g5pCt49qxjrUj{HRO{uL(
zr$Fy~8HUlCYQJfAEnSeW)1)HVoHNT<DhSdQ??7b|p<R)BJ#~ecV?phM;)sLGDwudf
zkvx4`8YXkFR00DyUUwp{l}Q#krNRA7|K0TzZ?~U!85^@6M_f(M9`)tblsF@a!oH21
z)k|$gPHD*GU`-oYMYWW>SH{I#L4F#M55kJFP)8zqWgPW5A*vSDDWJ_aRF~Zo<5|?2
z%77XKyhuJ9r-JQuC;+CK)1UY$5(n35hwyoi1x+V$y1WX%4W4`x#+W1ffD__Hq6EVY
z0gB&ef}C&~215#rO(k_+uk}wIgKUP4y^Sx@euJIQs@{n<yhZ9#Kh6@8ZE!bY;MoN;
zAZqff`dmR7?0Ss<L;$!eYtnOBB>*E{_e-3j+8MX0BuH0LrhC4f50&_U9ZrKr`aZl;
z+D-?`YAaULHA?_BpNJ~^$<lGjDsy-8MePk&mdcA-FQ73!l_`;qu>!Q*R(@i>zGlv^
zX-56Pf`Uv#J1+9Rr<mZM8I6J{On~lwa^J`8U4t~Uc#N{_1H0oZ!wD}EA7?Qk8A^W)
zPoka{&zL9SowQVM)eNs%?e2YxO*={ETHYT|c=Yq3k|uz)TIJNCbwVTuw4Ibm$j>vi
z*wz^B8Z}?dGA1%{jZXXY_;U&ZL8AJ0)RVA%9Tf`@-Z#Q1fb+>8Ji#iqL80dR1^<@(
zaOcfmfG6F8mq2@~_Rsa3bgjU70j-UJ=HXrE)GA@JJS+AKrS<x%Ab2ubK&O*nfc@dk
zv51NE&G+jKi_GUv#rY`^37|PolY9PaZ4v;t%$~+;4H-Z${sX^Lyw~Z9wqzbS)!UiH
zPz$Sz!qt2%XhZe`LtF#{Ra?s(539|}*0H1#)Q0gb9L!xzk>XComOS%K7S58VHPrfk
ztuirXZbM~1FrmFJ1-3~T04U9vu;MkiP*+Hhj48=C$?NdxB*C>|DMJ_2L`h_E+YNh4
zlfK>_*;(!2qD!s+;NCkgY6Ht}HyFLIH$@<`t(xV`Mcf*X!KN5=kee3vL;yM%X96)J
zezjEG(`GsZdzL41k;ZaM@TleOb>gL&qx9_rLFq{tr}QN_JIznw*y^}q^KReV9e6OZ
zzCE+!yXflc%JpXwgcN_OI^s$*{MTxFQ%SY#ylo%nE2mS?(JGSd8f6W##jCRxa!+Mh
z#(NvPPLJAg44V!G@xuspTGY<k`P?*GKN(Mh$=^9y34+ox4s5c$nFhCVa342=bf=f7
zlb=(61i>JU5{yGqCJ_Pq#(c2;y8fmSghGN${4;6xs4?_cmdS+f``_)?>QBe+tf8NC
zQ=V+L;gW=Aw1~1g)oq}@S6qL7w*OPk;~O6J3!gn$T$^3{fH23Ww`lwLLruM6@{~;b
zlx5<6X-T$E5-c1;(IfFbVU=%}nZVC}|Nc|OZxIIg98d`PW=%>^Wpbg;I%RU^eOeQd
zZw6s1Hxa&Le!%B-SAvB!>B+4s)_ziby+`SDqarU3ycd1<`DrEpqi=eJhX^x#-mB@B
zYji19sS6DwB{kU*D|Oh^Z)3$n!uL7CTAuE2X_~fIy<%%sZ+B{{et1)3sU=<>j9*(;
epmWP!Rld5wdWrcP1l9WJl0}4YBP&?`&ixP059~1j

literal 0
HcmV?d00001

diff --git a/deploy/python/postprocess.py b/deploy/python/postprocess.py
index 4f4d005f..f58434ad 100644
--- a/deploy/python/postprocess.py
+++ b/deploy/python/postprocess.py
@@ -179,3 +179,90 @@ class Binarize(object):
             byte[:, i:i + 1] = np.dot(x[:, i * 8:(i + 1) * 8], self.unit)
 
         return byte
+
+
+class Attribute(object):
+    def __init__(self,
+                 threshold=0.5,
+                 glasses_threshold=0.3,
+                 hold_threshold=0.6):
+        self.threshold = threshold
+        self.glasses_threshold = glasses_threshold
+        self.hold_threshold = hold_threshold
+
+    def __call__(self, batch_preds, file_names=None):
+        # postprocess output of predictor
+        age_list = ['AgeLess18', 'Age18-60', 'AgeOver60']
+        direct_list = ['Front', 'Side', 'Back']
+        bag_list = ['HandBag', 'ShoulderBag', 'Backpack']
+        upper_list = ['UpperStride', 'UpperLogo', 'UpperPlaid', 'UpperSplice']
+        lower_list = [
+            'LowerStripe', 'LowerPattern', 'LongCoat', 'Trousers', 'Shorts',
+            'Skirt&Dress'
+        ]
+        batch_res = []
+        for res in batch_preds:
+            res = res.tolist()
+            label_res = []
+            # gender 
+            gender = 'Female' if res[22] > self.threshold else 'Male'
+            label_res.append(gender)
+            # age
+            age = age_list[np.argmax(res[19:22])]
+            label_res.append(age)
+            # direction 
+            direction = direct_list[np.argmax(res[23:])]
+            label_res.append(direction)
+            # glasses
+            glasses = 'Glasses: '
+            if res[1] > self.glasses_threshold:
+                glasses += 'True'
+            else:
+                glasses += 'False'
+            label_res.append(glasses)
+            # hat
+            hat = 'Hat: '
+            if res[0] > self.threshold:
+                hat += 'True'
+            else:
+                hat += 'False'
+            label_res.append(hat)
+            # hold obj
+            hold_obj = 'HoldObjectsInFront: '
+            if res[18] > self.hold_threshold:
+                hold_obj += 'True'
+            else:
+                hold_obj += 'False'
+            label_res.append(hold_obj)
+            # bag
+            bag = bag_list[np.argmax(res[15:18])]
+            bag_score = res[15 + np.argmax(res[15:18])]
+            bag_label = bag if bag_score > self.threshold else 'No bag'
+            label_res.append(bag_label)
+            # upper
+            upper_res = res[4:8]
+            upper_label = 'Upper:'
+            sleeve = 'LongSleeve' if res[3] > res[2] else 'ShortSleeve'
+            upper_label += ' {}'.format(sleeve)
+            for i, r in enumerate(upper_res):
+                if r > self.threshold:
+                    upper_label += ' {}'.format(upper_list[i])
+            label_res.append(upper_label)
+            # lower
+            lower_res = res[8:14]
+            lower_label = 'Lower: '
+            has_lower = False
+            for i, l in enumerate(lower_res):
+                if l > self.threshold:
+                    lower_label += ' {}'.format(lower_list[i])
+                    has_lower = True
+            if not has_lower:
+                lower_label += ' {}'.format(lower_list[np.argmax(lower_res)])
+
+            label_res.append(lower_label)
+            # shoe
+            shoe = 'Boots' if res[14] > self.threshold else 'No boots'
+            label_res.append(shoe)
+
+            batch_res.append(label_res)
+        return batch_res
diff --git a/deploy/python/predict_cls.py b/deploy/python/predict_cls.py
index 64c07ea8..d7da15ea 100644
--- a/deploy/python/predict_cls.py
+++ b/deploy/python/predict_cls.py
@@ -138,13 +138,20 @@ def main(config):
                 continue
             batch_results = cls_predictor.predict(batch_imgs)
             for number, result_dict in enumerate(batch_results):
-                filename = batch_names[number]
-                clas_ids = result_dict["class_ids"]
-                scores_str = "[{}]".format(", ".join("{:.2f}".format(
-                    r) for r in result_dict["scores"]))
-                label_names = result_dict["label_names"]
-                print("{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
-                      format(filename, clas_ids, scores_str, label_names))
+                if "Attribute" in config["PostProcess"]:
+                    filename = batch_names[number]
+                    attr_message = result_dict
+                    print("{}:\tclass id(s): {}".format(filename,
+                                                        attr_message))
+                else:
+                    filename = batch_names[number]
+                    clas_ids = result_dict["class_ids"]
+                    scores_str = "[{}]".format(", ".join("{:.2f}".format(
+                        r) for r in result_dict["scores"]))
+                    label_names = result_dict["label_names"]
+                    print(
+                        "{}:\tclass id(s): {}, score(s): {}, label_name(s): {}".
+                        format(filename, clas_ids, scores_str, label_names))
             batch_imgs = []
             batch_names = []
     if cls_predictor.benchmark:
-- 
GitLab