From 4a3f19cd499b9852fdc31a4b868ef96482ceedc5 Mon Sep 17 00:00:00 2001 From: gexy5 Date: Tue, 14 Jun 2022 15:50:45 +0800 Subject: [PATCH] add omnivore Signed-off-by: gexy5 --- README.md | 108 +++++++++++++++++++++++++++++++++++++++++++++++++++- archery.mp4 | Bin 549197 -> 0 bytes omnivore.py | 2 +- result1.png | Bin 0 -> 32379 bytes result2.png | Bin 0 -> 116830 bytes 5 files changed, 108 insertions(+), 2 deletions(-) delete mode 100644 archery.mp4 create mode 100644 result1.png create mode 100644 result2.png diff --git a/README.md b/README.md index 88d0b48..675b016 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,108 @@ -# omnivore +# Video Classification with Omnivore +*Author: [Xinyu Ge](https://github.com/gexy185)* + +
+ +## Description + +A video classification operator generates labels (and corresponding scores) and extracts features for the input video. +It transforms the video into frames and loads pre-trained models by model names. +This operator has implemented pre-trained models from [Omnivore](https://arxiv.org/abs/2201.08377) +and maps vectors with labels provided by datasets used for pre-training. + +
+ +## Code Example + +Use the pretrained Omnivore model to classify and generate a vector for the given video path './archery.mp4' +([download](https://dl.fbaipublicfiles.com/pytorchvideo/projects/archery.mp4)). + + *Write the pipeline in simplified style*: + +- Predict labels (default): +```python +import towhee + +( + towhee.glob('./archery.mp4') + .video_decode.ffmpeg() + .action_classification.omnivore( + model_name='omnivore_swinT', topk=5) + .show() +) +``` + + +*Write a same pipeline with explicit inputs/outputs name specifications*: + +```python +import towhee + +( + towhee.glob['path']('./archery.mp4') + .video_decode.ffmpeg['path', 'frames']() + .action_classification.omnivore['frames', ('labels', 'scores', 'features')]( + model_name='omnivore_swinT') + .select['path', 'labels', 'scores', 'features']() + .show(formatter={'path': 'video_path'}) +) +``` + + + +
+ +## Factory Constructor + +Create the operator via the following factory method + +***video_classification.omnivore( +model_name='tsm_k400_r50_seg8', skip_preprocess=False, classmap=None, topk=5)*** + +**Parameters:** + +​ ***model_name***: *str* + +​ The name of pre-trained tsm model. + +​ Supported model names: +- omnivore_swinT +- omnivore_swinS +- omnivore_swinB +- omnivore_swinB_in21k +- omnivore_swinL_in21k +- omnivore_swinB_epic + +​ ***skip_preprocess***: *bool* + +​ Flag to control whether to skip video transforms, defaults to False. +If set to True, the step to transform videos will be skipped. +In this case, the user should guarantee that all the input video frames are already reprocessed properly, +and thus can be fed to model directly. + +​ ***classmap***: *Dict[str: int]*: + +​ Dictionary that maps class names to one hot vectors. +If not given, the operator will load the default class map dictionary. + +​ ***topk***: *int* + +​ The topk labels & scores to present in result. The default value is 5. + +## Interface + +A video classification operator generates a list of class labels +and a corresponding vector in numpy.ndarray given a video input data. + +**Parameters:** + +​ ***video***: *Union[str, numpy.ndarray]* + +​ Input video data using local path in string or video frames in ndarray. + + +**Returns**: *(list, list, torch.Tensor)* + +​ A tuple of (labels, scores, features), +which contains lists of predicted class names and corresponding scores. diff --git a/archery.mp4 b/archery.mp4 deleted file mode 100644 index 4a724d6f1c804338b442c9a9890e80502d710a08..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 549197 zcmYIvQRjclyOtgMt41ly8Zgcb}|_ zx2909;ja_j+`2KcGULQ)$X@B)}GV&r~G|cz{ zszSd&BV)r~g^;bCyOptt6Fwt7JrfNhJ;Q%!GbblIPC7bQS65mWb0cG0D}5VUTL)9R z{|=!wbF#Mj#n{?8ncLbpa^f568|WMIFyK2FoA5B<8yOo|*&15%FmTdy(&Ovf=v%ov z8uQS*F>%tnF)%RUTO0G38N1;-Ive~_Z1{GL?!TtLtDb`q4+AazuhH*0ktZZ#9_04`6 zz5g$Bv@$pRjphFe=<#hF{^txsb8CI4|Kc#WaWZzW(*Fhh$_=cX9rWGx3~jCL^qqcr z!`~7)Ip~|){JQuBI_Uq0F>%nhHg^0imVus~`!8*7#KZVY>l^9Y{g;J-o`Jc(<9~;k zI~e~@UarRGre;nCzdBnxV;enFTf1NF{}$T)npzsW|N7=(WTF2*q@K08%`c1ZXlQI> zZ0PL7!%Y8Qm=5~?mDIu5(d^gU!BFr2rTd@W!H~z$!35vh;J2>-rS;43Ff!57n~~dpX2}8_1$<_IerC>PR4dT%=qSZznAp;Lj2z0uP=T3-~0dXHUazq0EAwR z!-4>8&%5(KvM#bx!FopO^ik-;)IRmTaMV37XbuL~#|q{p#P&D{u`4&1Hf2eMR0ZdK&yI-8Wo?__^z)IUk3_x$D62q zI5tFP{&|fHG`RzA8=}ZEkBPLPlr|DJzf(i;=)f4Z=48STS#@?p#qKS~Y(xkiR^JL5{v3)#-y=T22^47> z1*q`%V=QItNYAy#o<&45dmv=4CGqY8H4}Wa8KpfLN`l4RiXE#{K_@J45c zY0*Vp3XnZ@3AEO;Bzx@5Ya<#0w=0H34$QhLLE|jgA|XeVuaU??@1VW+u8#HyAuaA6 zUj{}fzaxD0QcNPP4b3#sZ0P0RiBzt6cuHS2_SyqUuRVj-o}}ECSCC_q?NBnkWCUOI zeYurZT6bvw8JiL`?0H%tDXs3Q^dnKMHQvgG&=WK>AO2}Hui)TI2x-j#GhnVU9a%#_ z%4<)j?Jfs~{idcBUTB$Vi~D~m6;W|01oWhVbJs={6tX#hIuZS^WI6*<1B*sAVXF(tgP)g3er~s8ygTRM1S2{wK zGUa@zT8HB8Ct)Q9C#wO>?lU^2H<@&17UoLX`4sNEu(NJ^t{1qnyz?`b>;?{&wNwmb zdxGMM)Va)P?d-gX7|-YNP$BATB6zdTGnox*t_F8i-`dM6vQ%A7`X5Vxxi4b&O%{R> zZFtm)rPuI|#h-TG6krUnWOULba>(3_G3YjlGuu?f>slg5`LNo_3+vw*KVNR{cP_Qn zNVKzkk#V8(YBN7L_?YZeYewG~89$SuhR)b;1C;YbplzAV9xh@9Nd9iP&Y^M9DH_7w z6_xNm1CahjbZs6|7>PqT>x}Ld9?N+M*idT(H;JmBz*cvqAoQF2=nZQ2p62r}msQ(* ze!cIJ0{7_!4ic~1#(`7$A7&z*X3{% z5F57Tj__jCIY4jH;`FRf#bOEsXLNy%Xm~!5qCY0Y@QE%Os|7n$#?TK{G+sFTBCD6G z{KxLq98JxilZsG*T-z-!@Mv_bMXf)2vBF%5d3DF9HHzXIA9c=gdsUOlbzxrT@P68U zqE8DZarR@#Zxg9YBU+TCK5ht0YS8`g1>BtF+tXM|de$o4a^g^HllX6FM3zI&0@1JI zhO3x>d>Gzy&Zx zq@IUmg~jM~+$m?}&Iv21Pn?UhJZ3Brz^*bMv?q~@sFC=PbF@7|Sk`Ks{zh&2+X#Rja z-exe%kcF5TwIS#wbm9U@0yc%?LG zVzIAG_h38wh9HNG;nov2Corogh@9{Spg0C-J0s($0A4X?{w zZV)b1eotorh2;ft7QTu1fv82o{nn~1YXKU$cGnzmK0){dcqMtr1}d&PIbX`z)091; z_QKXhhuAUGaUw}eTnGEKmf!8eGaF9hx<@PUKxk_qS$`)yp%IhTU%~-!CFsHZZXZ?A zj$iZaOseL6|H6hB@;q*HUg(|;FVPuszNE7~8%eG>bmErr7#55@n8$&R7kr+H4t&8Q zklK~>Ws$5|X+gg{0kn#elQ-*#O|@ZG}%Ig9GzHlluyh_ zdMo{&n!sq_5|~=1{wn19WSrHhV_L$xOTaTF`BQi%(<>}s-Fkdv2xOx!xwyQ5O}hx+ zY$+5TgO1bT5iC*les@i{F&DL5`WH`52ah{AZLKVp-jU?^&f;uIdy4h0xCX3H-fPFL z7ZW&bI)9vyz{)Gp}i`9V9odu2GzILyV?3i+#MECd2Glicxo8F&l(kl zX8(ghGUDQpnQ$iI7sDlzO}axNcqo{sdQWk*MrCV4qZ2j?C7q@x=2lUj#jxOwuJWq_ zwH5s6G*W*5zALHKH{AtU%6{uTrj~Ej)ptT#BW~Z~HlkCEf|Ey~7-u%c#hZ^sr@_s< zbkbRlz8bA9pU&s=bZFyz;dIR+-_xYT>=lCO)QLPgydDyZ&2|_kni-xLa zGu_MJjm~!7b5$e!SbG(5iS@ymdG5!wR=pHnQuit(V-9MMca7!6!U!W=(Ov4KwLr1IYgy}ac`kKnCqmne1K^wPw;M45)*oG# ze=CXw5j^toiBb(PY!hKw{LvKs~snbRG=00hWq8vgu~hzkuv3@AJBJ?0?oo{JNA7JonT-Ik38Qukcx zy&p1WwqEvd0@m4yg+tIP9qFY@u6J;yaB3N@LUWn{`q{8-SZwL~LMn$Bg7mG(g>LID z!-B1ww;>sp;83!Y4gg(4IU_<2p7o_x&*?jhSOui96=_oM3XhO$d5A{%isL7y@g}3+;ri@E~$BV|EI;3 zl~j|p^d6Irp5=ttK-4HC!6TYQ^R3iv^W88+(8=ChDr5%Zr8=weP3?AqndH20C}AH? zFRONc@uj;8bRM|AS8I@Z@!L_}pe3Njo0`5_U(21bZrXlr!$yfL>+l`Nr&SLhAgHT( z>#L&pPN9_A(l<$$yl;*ySueA*SRys824f}L)p6#T5*`eefQ}*1=wKZnJ0$S^ig9If zfsDQbzE;;vxbI$hcb0;il<#!5l!y5c7e=g9)EP@8SwDna6#(jsY!sm9&E3oAJr5k& zJv}3rfpsu=T|hTiH<*hV>Cy~L#a(!tr-%{$V5z`iKZBCL1v2_>c`wxgx=lm+;v7wM z&bHYBz5%b2Xfq?;!2gXO$OJ4g4G8`OiwDzrQ?wS^XCpMmCYV%;WQJQ6!r^guK9c$TG+5ew}ARwC7XfO{R6 z;uia7ZHM|z$FX#BJP!YE_)*H^CO-!ifxIgE5XVnAuZY{l+I5XE$YiZyIGjjd)ym39 zJ}_qs1cmq9M>sOzV5F+)0AIkz#f?WVM3>a#s=o_E3IJxGySXWXG8i@2CMR3pBV@Nd za*b48*pJBZQg%;PD%%607IT#c_UDO=^nraucOza0iD@rX3DvsaKz3@qp4NI<*;;7N z8R;`~)v}|?{;ubzK?ii;NA{nLDINs3e`}Q)F(=L@5?FmeW}LVJBfi|`pJ%5aHC2X9 zsZ}A2h0Mz@V!^i}kq9IgctAV82*?iA69?h2@-GGc@B zm89AR#eJC?`0$rH4rX4_WP-Dnyk@V2eb@V!hCQ-cid&E&gGlEHK{v zd6Wch3c?hH<pHGtxe&e{Nyx3DaY`4e@a%Yjrm^7*gb#w;8{L$bwARry} zj1uLEiok`N1=F7e0lIu66qY!EhOebH)C+9I&~)R)J^c=7{dow0_mG;VLv@%`sv+|3 z$?Bu%hpQesY>0_BuZKUga;0o-b~OzOlf9xZ-*{;&s&wk%LQ6wYF^9UL-Tj@ljrxq) zds=VYS%H3OX$bP(Bm_K$8`O!KIf{V4u(4;d8zoR5>n0f^hcqQ=1Z0A*kh$WvGyxET zQoJoWG%^SFVQ&i(vShJvwr5NepN1T+jZS%=?v&fxIw3|MUSnm-gYz7H#@8#nDH43s zK07e}V6Y$=_q@29>tp&stx@>?^f7m9I|jC+L_z7)?A{_FI4#M{YY7af@ag&$q} zFOBa$%NquHk5ZK;saO%cGKsiUeUYWs+);N#k;A6fx&dQrok0`P{z#47<@#`}eK{`nA;{e_`#d^s@! 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