import{S as Xi,i as $i,s as Fi,e as n,k as r,w as y,t,M as Si,c as M,d as l,m as c,a as o,x as J,h as p,b as m,N as Ri,G as a,g as i,y as h,q as j,o as U,B as w,v as Ni}from"../../chunks/vendor-hf-doc-builder.js";import{T as Yi}from"../../chunks/Tip-hf-doc-builder.js";import{I as Il}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{C as d}from"../../chunks/CodeBlock-hf-doc-builder.js";import{D as zi}from"../../chunks/DocNotebookDropdown-hf-doc-builder.js";function Qi(Ka){let b,q,C,f,g,B,ds,A,Ts;return{c(){b=n("p"),q=t("\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC5D0\uC11C \uC124\uBA85\uD558\uB294 \uC791\uC5C5\uC740 \uB2E4\uC74C \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uC11C \uC9C0\uC6D0\uB429\uB2C8\uB2E4:"),C=r(),f=n("p"),g=n("a"),B=t("TimeSformer"),ds=t(", "),A=n("a"),Ts=t("VideoMAE"),this.h()},l(I){b=M(I,"P",{});var v=o(b);q=p(v,"\uC774 \uD29C\uD1A0\uB9AC\uC5BC\uC5D0\uC11C \uC124\uBA85\uD558\uB294 \uC791\uC5C5\uC740 \uB2E4\uC74C \uBAA8\uB378 \uC544\uD0A4\uD14D\uCC98\uC5D0\uC11C \uC9C0\uC6D0\uB429\uB2C8\uB2E4:"),v.forEach(l),C=c(I),f=M(I,"P",{});var $=o(f);g=M($,"A",{href:!0});var F=o(g);B=p(F,"TimeSformer"),F.forEach(l),ds=p($,", "),A=M($,"A",{href:!0});var Cl=o(A);Ts=p(Cl,"VideoMAE"),Cl.forEach(l),$.forEach(l),this.h()},h(){m(g,"href","../model_doc/timesformer"),m(A,"href","../model_doc/videomae")},m(I,v){i(I,b,v),a(b,q),i(I,C,v),i(I,f,v),a(f,g),a(g,B),a(f,ds),a(f,A),a(A,Ts)},d(I){I&&l(b),I&&l(C),I&&l(f)}}}function xi(Ka){let b,q,C,f,g,B,ds,A,Ts,I,v,$,F,Cl,Oa,vl,Yt,se,P,L,fs,zt,Qt,us,xt,Ht,Dt,Ll,qt,le,K,ae,_l,Pt,ee,bs,te,k,Lt,Is,Kt,Ot,Kl,sp,lp,pe,Zl,ap,ne,Cs,Me,S,O,Ol,vs,ep,sa,tp,oe,_s,Zs,pp,np,ie,gs,re,gl,Mp,ce,As,me,Al,op,ye,Es,Je,El,ip,he,Gs,je,E,rp,la,cp,mp,aa,yp,Jp,ea,hp,jp,Ue,ss,Up,Ws,wp,dp,we,Gl,Tp,de,ls,Wl,ta,fp,up,bp,Bl,pa,Ip,Cp,Te,Bs,fe,kl,vp,ue,N,as,na,ks,_p,Ma,Zp,be,Vl,gp,Ie,Vs,Ce,Rl,Ap,ve,Rs,_e,V,Ep,oa,Gp,Wp,ia,Bp,kp,Ze,_,ra,Vp,Rp,Xs,Xp,$p,ca,Fp,Sp,$s,Np,Yp,ge,Y,es,ma,Fs,zp,ya,Qp,Ae,ts,xp,Ss,Hp,Dp,Ee,Ns,Ge,ps,qp,Ys,Pp,Lp,We,Xl,Kp,Be,ns,Ja,Op,sn,ha,ln,ke,$l,an,Ve,zs,Re,Fl,en,Xe,Qs,$e,Sl,tn,Fe,xs,Se,T,ja,pn,nn,Hs,Mn,on,Ds,Ua,rn,cn,qs,wa,mn,yn,da,Jn,hn,Ta,jn,Un,Ps,wn,dn,fa,Tn,fn,Ne,Ms,un,ua,bn,In,Ye,Ls,ze,z,os,ba,Ks,Cn,Ia,vn,Qe,Os,xe,sl,Nl,NM,He,Q,is,Ca,ll,_n,va,Zn,De,G,gn,al,_a,An,En,Za,Gn,Wn,el,ga,Bn,kn,qe,R,Vn,Aa,Rn,Xn,Ea,$n,Fn,Pe,tl,Le,Z,Ga,Sn,Nn,Wa,Yn,zn,Ba,Qn,xn,ka,Hn,Dn,Ke,Yl,qn,Oe,pl,st,nl,Va,Pn,Ln,lt,Ml,ol,Kn,On,at,W,sM,Ra,lM,aM,Xa,eM,tM,$a,pM,nM,et,il,tt,rs,MM,Fa,oM,iM,pt,rl,nt,cs,rM,Sa,cM,mM,Mt,cl,Na,yM,JM,ot,ml,it,ms,hM,Ya,jM,UM,rt,yl,ct,x,ys,za,Jl,wM,Qa,dM,mt,zl,TM,yt,Ql,fM,Jt,hl,ht,jl,xl,YM,jt,X,uM,Ul,xa,bM,IM,Ha,CM,vM,Ut,wl,wt,Js,_M,Da,ZM,gM,dt,dl,Tt,hs,AM,qa,EM,GM,ft,Tl,ut,fl,Pa,WM,BM,bt,ul,It;return B=new Il({}),v=new zi({props:{classNames:"absolute z-10 right-0 top-0",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/video_classification.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/video_classification.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/video_classification.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/video_classification.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/video_classification.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/video_classification.ipynb"}]}}),K=new Yi({props:{$$slots:{default:[Qi]},$$scope:{ctx:Ka}}}),bs=new d({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwcHl0b3JjaHZpZGVvJTIwdHJhbnNmb3JtZXJzJTIwZXZhbHVhdGU=",highlighted:"pip install -q pytorchvideo transformers evaluate"}}),Cs=new d({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMG5vdGVib29rX2xvZ2luJTBBJTBBbm90ZWJvb2tfbG9naW4oKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> notebook_login

<span class="hljs-meta">&gt;&gt;&gt; </span>notebook_login()`}}),vs=new Il({}),gs=new d({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMGhmX2h1Yl9kb3dubG9hZCUwQSUwQWhmX2RhdGFzZXRfaWRlbnRpZmllciUyMCUzRCUyMCUyMnNheWFrcGF1bCUyRnVjZjEwMS1zdWJzZXQlMjIlMEFmaWxlbmFtZSUyMCUzRCUyMCUyMlVDRjEwMV9zdWJzZXQudGFyLmd6JTIyJTBBZmlsZV9wYXRoJTIwJTNEJTIwaGZfaHViX2Rvd25sb2FkKHJlcG9faWQlM0RoZl9kYXRhc2V0X2lkZW50aWZpZXIlMkMlMjBmaWxlbmFtZSUzRGZpbGVuYW1lJTJDJTIwcmVwb190eXBlJTNEJTIyZGF0YXNldCUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> hf_hub_download

<span class="hljs-meta">&gt;&gt;&gt; </span>hf_dataset_identifier = <span class="hljs-string">&quot;sayakpaul/ucf101-subset&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>filename = <span class="hljs-string">&quot;UCF101_subset.tar.gz&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>file_path = hf_hub_download(repo_id=hf_dataset_identifier, filename=filename, repo_type=<span class="hljs-string">&quot;dataset&quot;</span>)`}}),As=new d({props:{code:"aW1wb3J0JTIwdGFyZmlsZSUwQSUwQXdpdGglMjB0YXJmaWxlLm9wZW4oZmlsZV9wYXRoKSUyMGFzJTIwdCUzQSUwQSUyMCUyMCUyMCUyMCUyMHQuZXh0cmFjdGFsbCglMjIuJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tarfile

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tarfile.<span class="hljs-built_in">open</span>(file_path) <span class="hljs-keyword">as</span> t:
<span class="hljs-meta">... </span>     t.extractall(<span class="hljs-string">&quot;.&quot;</span>)`}}),Es=new d({props:{code:"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",highlighted:`UCF101_subset/
    train/
        BandMarching/
            video_1.mp4
            video_2.mp4
            ...
        Archery
            video_1.mp4
            video_2.mp4
            ...
        ...
    val/
        BandMarching/
            video_1.mp4
            video_2.mp4
            ...
        Archery
            video_1.mp4
            video_2.mp4
            ...
        ...
    <span class="hljs-built_in">test</span>/
        BandMarching/
            video_1.mp4
            video_2.mp4
            ...
        Archery
            video_1.mp4
            video_2.mp4
            ...
        ...`}}),Gs=new d({props:{code:"",highlighted:`...
<span class="hljs-string">&#x27;UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c04.avi&#x27;</span>,
<span class="hljs-string">&#x27;UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g07_c06.avi&#x27;</span>,
<span class="hljs-string">&#x27;UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g08_c01.avi&#x27;</span>,
<span class="hljs-string">&#x27;UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c02.avi&#x27;</span>,
<span class="hljs-string">&#x27;UCF101_subset/train/ApplyEyeMakeup/v_ApplyEyeMakeup_g09_c06.avi&#x27;</span>
...`}}),Bs=new d({props:{code:"Y2xhc3NfbGFiZWxzJTIwJTNEJTIwc29ydGVkKCU3QnN0cihwYXRoKS5zcGxpdCglMjIlMkYlMjIpJTVCMiU1RCUyMGZvciUyMHBhdGglMjBpbiUyMGFsbF92aWRlb19maWxlX3BhdGhzJTdEKSUwQWxhYmVsMmlkJTIwJTNEJTIwJTdCbGFiZWwlM0ElMjBpJTIwZm9yJTIwaSUyQyUyMGxhYmVsJTIwaW4lMjBlbnVtZXJhdGUoY2xhc3NfbGFiZWxzKSU3RCUwQWlkMmxhYmVsJTIwJTNEJTIwJTdCaSUzQSUyMGxhYmVsJTIwZm9yJTIwbGFiZWwlMkMlMjBpJTIwaW4lMjBsYWJlbDJpZC5pdGVtcygpJTdEJTBBJTBBcHJpbnQoZiUyMlVuaXF1ZSUyMGNsYXNzZXMlM0ElMjAlN0JsaXN0KGxhYmVsMmlkLmtleXMoKSklN0QuJTIyKSUwQQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>class_labels = <span class="hljs-built_in">sorted</span>({<span class="hljs-built_in">str</span>(path).split(<span class="hljs-string">&quot;/&quot;</span>)[<span class="hljs-number">2</span>] <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> all_video_file_paths})
<span class="hljs-meta">&gt;&gt;&gt; </span>label2id = {label: i <span class="hljs-keyword">for</span> i, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(class_labels)}
<span class="hljs-meta">&gt;&gt;&gt; </span>id2label = {i: label <span class="hljs-keyword">for</span> label, i <span class="hljs-keyword">in</span> label2id.items()}

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">f&quot;Unique classes: <span class="hljs-subst">{<span class="hljs-built_in">list</span>(label2id.keys())}</span>.&quot;</span>)

<span class="hljs-comment"># Unique classes: [&#x27;ApplyEyeMakeup&#x27;, &#x27;ApplyLipstick&#x27;, &#x27;Archery&#x27;, &#x27;BabyCrawling&#x27;, &#x27;BalanceBeam&#x27;, &#x27;BandMarching&#x27;, &#x27;BaseballPitch&#x27;, &#x27;Basketball&#x27;, &#x27;BasketballDunk&#x27;, &#x27;BenchPress&#x27;].</span>`}}),ks=new Il({}),Vs=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> VideoMAEImageProcessor, VideoMAEForVideoClassification

<span class="hljs-meta">&gt;&gt;&gt; </span>model_ckpt = <span class="hljs-string">&quot;MCG-NJU/videomae-base&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = VideoMAEImageProcessor.from_pretrained(model_ckpt)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = VideoMAEForVideoClassification.from_pretrained(
<span class="hljs-meta">... </span>    model_ckpt,
<span class="hljs-meta">... </span>    label2id=label2id,
<span class="hljs-meta">... </span>    id2label=id2label,
<span class="hljs-meta">... </span>    ignore_mismatched_sizes=<span class="hljs-literal">True</span>,  <span class="hljs-comment"># provide this in case you&#x27;re planning to fine-tune an already fine-tuned checkpoint</span>
<span class="hljs-meta">... </span>)`}}),Rs=new d({props:{code:"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",highlighted:`Some weights of the model checkpoint at MCG-NJU/videomae-base were not used when initializing VideoMAEForVideoClassification: [..., <span class="hljs-string">&#x27;decoder.decoder_layers.1.attention.output.dense.bias&#x27;</span>, <span class="hljs-string">&#x27;decoder.decoder_layers.2.attention.attention.key.weight&#x27;</span>]
- This IS expected <span class="hljs-keyword">if</span> you are initializing VideoMAEForVideoClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
- This IS NOT expected <span class="hljs-keyword">if</span> you are initializing VideoMAEForVideoClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
Some weights of VideoMAEForVideoClassification were not initialized from the model checkpoint at MCG-NJU/videomae-base and are newly initialized: [<span class="hljs-string">&#x27;classifier.bias&#x27;</span>, <span class="hljs-string">&#x27;classifier.weight&#x27;</span>]
You should probably TRAIN this model on a down-stream task to be able to use it <span class="hljs-keyword">for</span> predictions and inference.`}}),Fs=new Il({}),Ns=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> pytorchvideo.data

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> pytorchvideo.transforms <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span>    ApplyTransformToKey,
<span class="hljs-meta">... </span>    Normalize,
<span class="hljs-meta">... </span>    RandomShortSideScale,
<span class="hljs-meta">... </span>    RemoveKey,
<span class="hljs-meta">... </span>    ShortSideScale,
<span class="hljs-meta">... </span>    UniformTemporalSubsample,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> torchvision.transforms <span class="hljs-keyword">import</span> (
<span class="hljs-meta">... </span>    Compose,
<span class="hljs-meta">... </span>    Lambda,
<span class="hljs-meta">... </span>    RandomCrop,
<span class="hljs-meta">... </span>    RandomHorizontalFlip,
<span class="hljs-meta">... </span>    Resize,
<span class="hljs-meta">... </span>)`}}),zs=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>mean = image_processor.image_mean
<span class="hljs-meta">&gt;&gt;&gt; </span>std = image_processor.image_std
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">if</span> <span class="hljs-string">&quot;shortest_edge&quot;</span> <span class="hljs-keyword">in</span> image_processor.size:
<span class="hljs-meta">... </span>    height = width = image_processor.size[<span class="hljs-string">&quot;shortest_edge&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">else</span>:
<span class="hljs-meta">... </span>    height = image_processor.size[<span class="hljs-string">&quot;height&quot;</span>]
<span class="hljs-meta">... </span>    width = image_processor.size[<span class="hljs-string">&quot;width&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>resize_to = (height, width)

<span class="hljs-meta">&gt;&gt;&gt; </span>num_frames_to_sample = model.config.num_frames
<span class="hljs-meta">&gt;&gt;&gt; </span>sample_rate = <span class="hljs-number">4</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>fps = <span class="hljs-number">30</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>clip_duration = num_frames_to_sample * sample_rate / fps`}}),Qs=new d({props:{code:"dHJhaW5fdHJhbnNmb3JtJTIwJTNEJTIwQ29tcG9zZSglMEElMjAlMjAlMjAlMjAlNUIlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBBcHBseVRyYW5zZm9ybVRvS2V5KCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMGtleSUzRCUyMnZpZGVvJTIyJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwdHJhbnNmb3JtJTNEQ29tcG9zZSglMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlNUIlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBVbmlmb3JtVGVtcG9yYWxTdWJzYW1wbGUobnVtX2ZyYW1lc190b19zYW1wbGUpJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwTGFtYmRhKGxhbWJkYSUyMHglM0ElMjB4JTIwJTJGJTIwMjU1LjApJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwTm9ybWFsaXplKG1lYW4lMkMlMjBzdGQpJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwUmFuZG9tU2hvcnRTaWRlU2NhbGUobWluX3NpemUlM0QyNTYlMkMlMjBtYXhfc2l6ZSUzRDMyMCklMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBSYW5kb21Dcm9wKHJlc2l6ZV90byklMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBSYW5kb21Ib3Jpem9udGFsRmxpcChwJTNEMC41KSUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCU1RCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCklMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjApJTJDJTBBJTIwJTIwJTIwJTIwJTVEJTBBKSUwQSUwQXRyYWluX2RhdGFzZXQlMjAlM0QlMjBweXRvcmNodmlkZW8uZGF0YS5VY2YxMDEoJTBBJTIwJTIwJTIwJTIwZGF0YV9wYXRoJTNEb3MucGF0aC5qb2luKGRhdGFzZXRfcm9vdF9wYXRoJTJDJTIwJTIydHJhaW4lMjIpJTJDJTBBJTIwJTIwJTIwJTIwY2xpcF9zYW1wbGVyJTNEcHl0b3JjaHZpZGVvLmRhdGEubWFrZV9jbGlwX3NhbXBsZXIoJTIycmFuZG9tJTIyJTJDJTIwY2xpcF9kdXJhdGlvbiklMkMlMEElMjAlMjAlMjAlMjBkZWNvZGVfYXVkaW8lM0RGYWxzZSUyQyUwQSUyMCUyMCUyMCUyMHRyYW5zZm9ybSUzRHRyYWluX3RyYW5zZm9ybSUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>train_transform = Compose(
<span class="hljs-meta">... </span>    [
<span class="hljs-meta">... </span>        ApplyTransformToKey(
<span class="hljs-meta">... </span>            key=<span class="hljs-string">&quot;video&quot;</span>,
<span class="hljs-meta">... </span>            transform=Compose(
<span class="hljs-meta">... </span>                [
<span class="hljs-meta">... </span>                    UniformTemporalSubsample(num_frames_to_sample),
<span class="hljs-meta">... </span>                    Lambda(<span class="hljs-keyword">lambda</span> x: x / <span class="hljs-number">255.0</span>),
<span class="hljs-meta">... </span>                    Normalize(mean, std),
<span class="hljs-meta">... </span>                    RandomShortSideScale(min_size=<span class="hljs-number">256</span>, max_size=<span class="hljs-number">320</span>),
<span class="hljs-meta">... </span>                    RandomCrop(resize_to),
<span class="hljs-meta">... </span>                    RandomHorizontalFlip(p=<span class="hljs-number">0.5</span>),
<span class="hljs-meta">... </span>                ]
<span class="hljs-meta">... </span>            ),
<span class="hljs-meta">... </span>        ),
<span class="hljs-meta">... </span>    ]
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>train_dataset = pytorchvideo.data.Ucf101(
<span class="hljs-meta">... </span>    data_path=os.path.join(dataset_root_path, <span class="hljs-string">&quot;train&quot;</span>),
<span class="hljs-meta">... </span>    clip_sampler=pytorchvideo.data.make_clip_sampler(<span class="hljs-string">&quot;random&quot;</span>, clip_duration),
<span class="hljs-meta">... </span>    decode_audio=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    transform=train_transform,
<span class="hljs-meta">... </span>)`}}),xs=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>val_transform = Compose(
<span class="hljs-meta">... </span>    [
<span class="hljs-meta">... </span>        ApplyTransformToKey(
<span class="hljs-meta">... </span>            key=<span class="hljs-string">&quot;video&quot;</span>,
<span class="hljs-meta">... </span>            transform=Compose(
<span class="hljs-meta">... </span>                [
<span class="hljs-meta">... </span>                    UniformTemporalSubsample(num_frames_to_sample),
<span class="hljs-meta">... </span>                    Lambda(<span class="hljs-keyword">lambda</span> x: x / <span class="hljs-number">255.0</span>),
<span class="hljs-meta">... </span>                    Normalize(mean, std),
<span class="hljs-meta">... </span>                    Resize(resize_to),
<span class="hljs-meta">... </span>                ]
<span class="hljs-meta">... </span>            ),
<span class="hljs-meta">... </span>        ),
<span class="hljs-meta">... </span>    ]
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>val_dataset = pytorchvideo.data.Ucf101(
<span class="hljs-meta">... </span>    data_path=os.path.join(dataset_root_path, <span class="hljs-string">&quot;val&quot;</span>),
<span class="hljs-meta">... </span>    clip_sampler=pytorchvideo.data.make_clip_sampler(<span class="hljs-string">&quot;uniform&quot;</span>, clip_duration),
<span class="hljs-meta">... </span>    decode_audio=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    transform=val_transform,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>test_dataset = pytorchvideo.data.Ucf101(
<span class="hljs-meta">... </span>    data_path=os.path.join(dataset_root_path, <span class="hljs-string">&quot;test&quot;</span>),
<span class="hljs-meta">... </span>    clip_sampler=pytorchvideo.data.make_clip_sampler(<span class="hljs-string">&quot;uniform&quot;</span>, clip_duration),
<span class="hljs-meta">... </span>    decode_audio=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    transform=val_transform,
<span class="hljs-meta">... </span>)`}}),Ls=new d({props:{code:"cHJpbnQodHJhaW5fZGF0YXNldC5udW1fdmlkZW9zJTJDJTIwdmFsX2RhdGFzZXQubnVtX3ZpZGVvcyUyQyUyMHRlc3RfZGF0YXNldC5udW1fdmlkZW9zKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(train_dataset.num_videos, val_dataset.num_videos, test_dataset.num_videos)
<span class="hljs-comment"># (300, 30, 75)</span>`}}),Ks=new Il({}),Os=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> imageio
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> IPython.display <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">unnormalize_img</span>(<span class="hljs-params">img</span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&quot;&quot;Un-normalizes the image pixels.&quot;&quot;&quot;</span>
<span class="hljs-meta">... </span>    img = (img * std) + mean
<span class="hljs-meta">... </span>    img = (img * <span class="hljs-number">255</span>).astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> img.clip(<span class="hljs-number">0</span>, <span class="hljs-number">255</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">create_gif</span>(<span class="hljs-params">video_tensor, filename=<span class="hljs-string">&quot;sample.gif&quot;</span></span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&quot;&quot;Prepares a GIF from a video tensor.
<span class="hljs-meta">... </span>    
<span class="hljs-meta">... </span>    The video tensor is expected to have the following shape:
<span class="hljs-meta">... </span>    (num_frames, num_channels, height, width).
<span class="hljs-meta">... </span>    &quot;&quot;&quot;</span>
<span class="hljs-meta">... </span>    frames = []
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> video_frame <span class="hljs-keyword">in</span> video_tensor:
<span class="hljs-meta">... </span>        frame_unnormalized = unnormalize_img(video_frame.permute(<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">0</span>).numpy())
<span class="hljs-meta">... </span>        frames.append(frame_unnormalized)
<span class="hljs-meta">... </span>    kargs = {<span class="hljs-string">&quot;duration&quot;</span>: <span class="hljs-number">0.25</span>}
<span class="hljs-meta">... </span>    imageio.mimsave(filename, frames, <span class="hljs-string">&quot;GIF&quot;</span>, **kargs)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> filename

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">display_gif</span>(<span class="hljs-params">video_tensor, gif_name=<span class="hljs-string">&quot;sample.gif&quot;</span></span>):
<span class="hljs-meta">... </span>    <span class="hljs-string">&quot;&quot;&quot;Prepares and displays a GIF from a video tensor.&quot;&quot;&quot;</span>
<span class="hljs-meta">... </span>    video_tensor = video_tensor.permute(<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>)
<span class="hljs-meta">... </span>    gif_filename = create_gif(video_tensor, gif_name)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> Image(filename=gif_filename)

<span class="hljs-meta">&gt;&gt;&gt; </span>sample_video = <span class="hljs-built_in">next</span>(<span class="hljs-built_in">iter</span>(train_dataset))
<span class="hljs-meta">&gt;&gt;&gt; </span>video_tensor = sample_video[<span class="hljs-string">&quot;video&quot;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>display_gif(video_tensor)`}}),ll=new Il({}),tl=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> TrainingArguments, Trainer

<span class="hljs-meta">&gt;&gt;&gt; </span>model_name = model_ckpt.split(<span class="hljs-string">&quot;/&quot;</span>)[-<span class="hljs-number">1</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>new_model_name = <span class="hljs-string">f&quot;<span class="hljs-subst">{model_name}</span>-finetuned-ucf101-subset&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>num_epochs = <span class="hljs-number">4</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>args = TrainingArguments(
<span class="hljs-meta">... </span>    new_model_name,
<span class="hljs-meta">... </span>    remove_unused_columns=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    evaluation_strategy=<span class="hljs-string">&quot;epoch&quot;</span>,
<span class="hljs-meta">... </span>    save_strategy=<span class="hljs-string">&quot;epoch&quot;</span>,
<span class="hljs-meta">... </span>    learning_rate=<span class="hljs-number">5e-5</span>,
<span class="hljs-meta">... </span>    per_device_train_batch_size=batch_size,
<span class="hljs-meta">... </span>    per_device_eval_batch_size=batch_size,
<span class="hljs-meta">... </span>    warmup_ratio=<span class="hljs-number">0.1</span>,
<span class="hljs-meta">... </span>    logging_steps=<span class="hljs-number">10</span>,
<span class="hljs-meta">... </span>    load_best_model_at_end=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    metric_for_best_model=<span class="hljs-string">&quot;accuracy&quot;</span>,
<span class="hljs-meta">... </span>    push_to_hub=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>    max_steps=(train_dataset.num_videos // batch_size) * num_epochs,
<span class="hljs-meta">... </span>)`}}),pl=new d({props:{code:"aW1wb3J0JTIwZXZhbHVhdGUlMEElMEFtZXRyaWMlMjAlM0QlMjBldmFsdWF0ZS5sb2FkKCUyMmFjY3VyYWN5JTIyKSUwQSUwQSUwQWRlZiUyMGNvbXB1dGVfbWV0cmljcyhldmFsX3ByZWQpJTNBJTBBJTIwJTIwJTIwJTIwcHJlZGljdGlvbnMlMjAlM0QlMjBucC5hcmdtYXgoZXZhbF9wcmVkLnByZWRpY3Rpb25zJTJDJTIwYXhpcyUzRDEpJTBBJTIwJTIwJTIwJTIwcmV0dXJuJTIwbWV0cmljLmNvbXB1dGUocHJlZGljdGlvbnMlM0RwcmVkaWN0aW9ucyUyQyUyMHJlZmVyZW5jZXMlM0RldmFsX3ByZWQubGFiZWxfaWRzKQ==",highlighted:`<span class="hljs-keyword">import</span> evaluate

metric = evaluate.load(<span class="hljs-string">&quot;accuracy&quot;</span>)


<span class="hljs-keyword">def</span> <span class="hljs-title function_">compute_metrics</span>(<span class="hljs-params">eval_pred</span>):
    predictions = np.argmax(eval_pred.predictions, axis=<span class="hljs-number">1</span>)
    <span class="hljs-keyword">return</span> metric.compute(predictions=predictions, references=eval_pred.label_ids)`}}),il=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">collate_fn</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    <span class="hljs-comment"># permute to (num_frames, num_channels, height, width)</span>
<span class="hljs-meta">... </span>    pixel_values = torch.stack(
<span class="hljs-meta">... </span>        [example[<span class="hljs-string">&quot;video&quot;</span>].permute(<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>) <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples]
<span class="hljs-meta">... </span>    )
<span class="hljs-meta">... </span>    labels = torch.tensor([example[<span class="hljs-string">&quot;label&quot;</span>] <span class="hljs-keyword">for</span> example <span class="hljs-keyword">in</span> examples])
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> {<span class="hljs-string">&quot;pixel_values&quot;</span>: pixel_values, <span class="hljs-string">&quot;labels&quot;</span>: labels}`}}),rl=new d({props:{code:"dHJhaW5lciUyMCUzRCUyMFRyYWluZXIoJTBBJTIwJTIwJTIwJTIwbW9kZWwlMkMlMEElMjAlMjAlMjAlMjBhcmdzJTJDJTBBJTIwJTIwJTIwJTIwdHJhaW5fZGF0YXNldCUzRHRyYWluX2RhdGFzZXQlMkMlMEElMjAlMjAlMjAlMjBldmFsX2RhdGFzZXQlM0R2YWxfZGF0YXNldCUyQyUwQSUyMCUyMCUyMCUyMHRva2VuaXplciUzRGltYWdlX3Byb2Nlc3NvciUyQyUwQSUyMCUyMCUyMCUyMGNvbXB1dGVfbWV0cmljcyUzRGNvbXB1dGVfbWV0cmljcyUyQyUwQSUyMCUyMCUyMCUyMGRhdGFfY29sbGF0b3IlM0Rjb2xsYXRlX2ZuJTJDJTBBKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model,
<span class="hljs-meta">... </span>    args,
<span class="hljs-meta">... </span>    train_dataset=train_dataset,
<span class="hljs-meta">... </span>    eval_dataset=val_dataset,
<span class="hljs-meta">... </span>    tokenizer=image_processor,
<span class="hljs-meta">... </span>    compute_metrics=compute_metrics,
<span class="hljs-meta">... </span>    data_collator=collate_fn,
<span class="hljs-meta">... </span>)`}}),ml=new d({props:{code:"dHJhaW5fcmVzdWx0cyUyMCUzRCUyMHRyYWluZXIudHJhaW4oKQ==",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>train_results = trainer.train()'}}),yl=new d({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()'}}),Jl=new Il({}),hl=new d({props:{code:"c2FtcGxlX3Rlc3RfdmlkZW8lMjAlM0QlMjBuZXh0KGl0ZXIodGVzdF9kYXRhc2V0KSk=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>sample_test_video = <span class="hljs-built_in">next</span>(<span class="hljs-built_in">iter</span>(test_dataset))'}}),wl=new d({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBdmlkZW9fY2xzJTIwJTNEJTIwcGlwZWxpbmUobW9kZWwlM0QlMjJteV9hd2Vzb21lX3ZpZGVvX2Nsc19tb2RlbCUyMiklMEF2aWRlb19jbHMoJTIyaHR0cHMlM0ElMkYlMkZodWdnaW5nZmFjZS5jbyUyRmRhdGFzZXRzJTJGc2F5YWtwYXVsJTJGdWNmMTAxLXN1YnNldCUyRnJlc29sdmUlMkZtYWluJTJGdl9CYXNrZXRiYWxsRHVua19nMTRfYzA2LmF2aSUyMik=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> pipeline

<span class="hljs-meta">&gt;&gt;&gt; </span>video_cls = pipeline(model=<span class="hljs-string">&quot;my_awesome_video_cls_model&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>video_cls(<span class="hljs-string">&quot;https://huggingface.co/datasets/sayakpaul/ucf101-subset/resolve/main/v_BasketballDunk_g14_c06.avi&quot;</span>)
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.9272987842559814</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;BasketballDunk&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.017777055501937866</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;BabyCrawling&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.01663011871278286</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;BalanceBeam&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.009560945443809032</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;BandMarching&#x27;</span>},
 {<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.0068979403004050255</span>, <span class="hljs-string">&#x27;label&#x27;</span>: <span class="hljs-string">&#x27;BaseballPitch&#x27;</span>}]`}}),dl=new d({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">run_inference</span>(<span class="hljs-params">model, video</span>):
<span class="hljs-meta">... </span>    <span class="hljs-comment"># (num_frames, num_channels, height, width)</span>
<span class="hljs-meta">... </span>    perumuted_sample_test_video = video.permute(<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>)
<span class="hljs-meta">... </span>    inputs = {
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;pixel_values&quot;</span>: perumuted_sample_test_video.unsqueeze(<span class="hljs-number">0</span>),
<span class="hljs-meta">... </span>        <span class="hljs-string">&quot;labels&quot;</span>: torch.tensor(
<span class="hljs-meta">... </span>            [sample_test_video[<span class="hljs-string">&quot;label&quot;</span>]]
<span class="hljs-meta">... </span>        ),  <span class="hljs-comment"># this can be skipped if you don&#x27;t have labels available.</span>
<span class="hljs-meta">... </span>    }

<span class="hljs-meta">... </span>    device = torch.device(<span class="hljs-string">&quot;cuda&quot;</span> <span class="hljs-keyword">if</span> torch.cuda.is_available() <span class="hljs-keyword">else</span> <span class="hljs-string">&quot;cpu&quot;</span>)
<span class="hljs-meta">... </span>    inputs = {k: v.to(device) <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> inputs.items()}
<span class="hljs-meta">... </span>    model = model.to(device)

<span class="hljs-meta">... </span>    <span class="hljs-comment"># forward pass</span>
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> torch.no_grad():
<span class="hljs-meta">... </span>        outputs = model(**inputs)
<span class="hljs-meta">... </span>        logits = outputs.logits

<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> logits`}}),Tl=new d({props:{code:"bG9naXRzJTIwJTNEJTIwcnVuX2luZmVyZW5jZSh0cmFpbmVkX21vZGVsJTJDJTIwc2FtcGxlX3Rlc3RfdmlkZW8lNUIlMjJ2aWRlbyUyMiU1RCk=",highlighted:'&gt;&gt;&gt; logits = run<span class="hljs-constructor">_inference(<span class="hljs-params">trained_model</span>, <span class="hljs-params">sample_test_video</span>[<span class="hljs-string">&quot;video&quot;</span>])</span>'}}),ul=new d({props:{code:"cHJlZGljdGVkX2NsYXNzX2lkeCUyMCUzRCUyMGxvZ2l0cy5hcmdtYXgoLTEpLml0ZW0oKSUwQXByaW50KCUyMlByZWRpY3RlZCUyMGNsYXNzJTNBJTIyJTJDJTIwbW9kZWwuY29uZmlnLmlkMmxhYmVsJTVCcHJlZGljdGVkX2NsYXNzX2lkeCU1RCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_class_idx = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(<span class="hljs-string">&quot;Predicted class:&quot;</span>, model.config.id2label[predicted_class_idx])
<span class="hljs-comment"># Predicted class: BasketballDunk</span>`}}),{c(){b=n("meta"),q=r(),C=n("h1"),f=n("a"),g=n("span"),y(B.$$.fragment),ds=r(),A=n("span"),Ts=t("\uC601\uC0C1 \uBD84\uB958"),I=r(),y(v.$$.fragment),$=r(),F=n("p"),Cl=t("\uC601\uC0C1 \uBD84\uB958\uB294 \uC601\uC0C1 \uC804\uCCB4\uC5D0 \uB808\uC774\uBE14 \uB610\uB294 \uD074\uB798\uC2A4\uB97C \uC9C0\uC815\uD558\uB294 \uC791\uC5C5\uC785\uB2C8\uB2E4. \uAC01 \uC601\uC0C1\uC5D0\uB294 \uD558\uB098\uC758 \uD074\uB798\uC2A4\uAC00 \uC788\uC744 \uAC83\uC73C\uB85C \uC608\uC0C1\uB429\uB2C8\uB2E4. \uC601\uC0C1 \uBD84\uB958 \uBAA8\uB378\uC740 \uC601\uC0C1\uC744 \uC785\uB825\uC73C\uB85C \uBC1B\uC544 \uC5B4\uB290 \uD074\uB798\uC2A4\uC5D0 \uC18D\uD558\uB294\uC9C0\uC5D0 \uB300\uD55C \uC608\uCE21\uC744 \uBC18\uD658\uD569\uB2C8\uB2E4. \uC774\uB7EC\uD55C \uBAA8\uB378\uC740 \uC601\uC0C1\uC774 \uC5B4\uB5A4 \uB0B4\uC6A9\uC778\uC9C0 \uBD84\uB958\uD558\uB294 \uB370 \uC0AC\uC6A9\uB420 \uC218 \uC788\uC2B5\uB2C8\uB2E4. \uC601\uC0C1 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