import{s as be,n as we,o as Je}from"../chunks/scheduler.56730f09.js";import{S as fe,i as Te,g as p,s as a,r as c,A as xe,h as i,f as l,c as n,j as ye,u as r,x as m,k as qs,y as Ue,a as e,v as o,d as g,t as h,w as M}from"../chunks/index.1f144517.js";import{C as j}from"../chunks/CodeBlock.738eeccb.js";import{D as $e}from"../chunks/DocNotebookDropdown.243c3df7.js";import{H as Es,E as Ce}from"../chunks/EditOnGithub.854793f1.js";function Ze(xl){let d,Ls,Ys,Ss,w,Ps,J,Ds,f,Ul="시각적 질의응답(VQA)은 이미지를 기반으로 개방형 질문에 대응하는 작업입니다. 이 작업을 지원하는 모델의 입력은 대부분 이미지와 질문의 조합이며, 출력은 자연어로 된 답변입니다.",Ks,T,$l="VQA의 주요 사용 사례는 다음과 같습니다:",Os,x,Cl="<li>시각 장애인을 위한 접근성 애플리케이션을 구축할 수 있습니다.</li> <li>교육: 강의나 교과서에 나온 시각 자료에 대한 질문에 답할 수 있습니다. 또한 체험형 전시와 유적 등에서도 VQA를 활용할 수 있습니다.</li> <li>고객 서비스 및 전자상거래: VQA는 사용자가 제품에 대해 질문할 수 있게 함으로써 사용자 경험을 향상시킬 수 있습니다.</li> <li>이미지 검색: VQA 모델을 사용하여 원하는 특성을 가진 이미지를 검색할 수 있습니다. 예를 들어 사용자는 “강아지가 있어?”라고 물어봐서 주어진 이미지 묶음에서 강아지가 있는 모든 이미지를 받아볼 수 있습니다.</li>",st,U,Zl="이 가이드에서 학습할 내용은 다음과 같습니다:",tt,$,vl='<li>VQA 모델 중 하나인 <a href="../../en/model_doc/vilt">ViLT</a>를 <a href="https://huggingface.co/datasets/Graphcore/vqa" rel="nofollow"><code>Graphcore/vqa</code> 데이터셋</a> 에서 미세조정하는 방법</li> <li>미세조정된 ViLT 모델로 추론하는 방법</li> <li>BLIP-2 같은 생성 모델로 제로샷 VQA 추론을 실행하는 방법</li>',lt,C,et,Z,_l=`ViLT는 Vision Transformer (ViT) 내에 텍스트 임베딩을 포함하여 비전/자연어 사전훈련(VLP; Vision-and-Language Pretraining)을 위한 기본 디자인을 제공합니다.
ViLT 모델은 비전 트랜스포머(ViT)에 텍스트 임베딩을 넣어 비전/언어 사전훈련(VLP; Vision-and-Language Pre-training)을 위한 기본적인 디자인을 갖췄습니다. 이 모델은 여러 다운스트림 작업에 사용할 수 있습니다. VQA 태스크에서는 (<code>[CLS]</code> 토큰의 최종 은닉 상태 위에 선형 레이어인) 분류 헤더가 있으며 무작위로 초기화됩니다.
따라서 여기에서 시각적 질의응답은 <strong>분류 문제</strong>로 취급됩니다.`,at,v,Vl="최근의 BLIP, BLIP-2, InstructBLIP와 같은 모델들은 VQA를 생성형 작업으로 간주합니다. 가이드의 후반부에서는 이런 모델들을 사용하여 제로샷 VQA 추론을 하는 방법에 대해 설명하겠습니다.",nt,_,Gl="시작하기 전 필요한 모든 라이브러리를 설치했는지 확인하세요.",pt,V,it,G,Wl=`커뮤니티에 모델을 공유하는 것을 권장 드립니다. Hugging Face 계정에 로그인하여 🤗 Hub에 업로드할 수 있습니다.
메시지가 나타나면 로그인할 토큰을 입력하세요:`,mt,W,ct,k,kl="모델 체크포인트를 전역 변수로 선언하세요.",rt,I,ot,B,gt,X,Il='이 가이드에서는 <code>Graphcore/vqa</code> 데이터세트의 작은 샘플을 사용합니다. 전체 데이터세트는 <a href="https://huggingface.co/datasets/Graphcore/vqa" rel="nofollow">🤗 Hub</a> 에서 확인할 수 있습니다.',ht,Q,Bl=`<a href="https://huggingface.co/datasets/Graphcore/vqa" rel="nofollow"><code>Graphcore/vqa</code> 데이터세트</a> 의 대안으로 공식 <a href="https://visualqa.org/download.html" rel="nofollow">VQA 데이터세트 페이지</a> 에서 동일한 데이터를 수동으로 다운로드할 수 있습니다. 직접 공수한 데이터로 튜토리얼을 따르고 싶다면 <a href="https://huggingface.co/docs/datasets/image_dataset#loading-script" rel="nofollow">이미지 데이터세트 만들기</a> 라는
🤗 Datasets 문서를 참조하세요.`,Mt,R,Xl="검증 데이터의 첫 200개 항목을 불러와 데이터세트의 특성을 확인해 보겠습니다:",jt,H,dt,F,Ql="예제를 하나 뽑아 데이터세트의 특성을 이해해 보겠습니다.",ut,N,yt,z,Rl="데이터세트에는 다음과 같은 특성이 포함되어 있습니다:",bt,E,Hl="<li><code>question</code>: 이미지에 대한 질문</li> <li><code>image_id</code>: 질문과 관련된 이미지의 경로</li> <li><code>label</code>: 데이터의 레이블 (annotations)</li>",wt,Y,Fl="나머지 특성들은 필요하지 않기 때문에 삭제해도 됩니다:",Jt,A,ft,q,Nl="보시다시피 <code>label</code> 특성은 같은 질문마다 답변이 여러 개 있을 수 있습니다. 모두 다른 데이터 라벨러들로부터 수집되었기 때문인데요. 질문의 답변은 주관적일 수 있습니다. 이 경우 질문은 “그는 어디를 보고 있나요?” 였지만, 어떤 사람들은 “아래”로 레이블을 달았고, 다른 사람들은 “테이블” 또는 “스케이트보드” 등으로 주석을 달았습니다.",Tt,L,zl="아래의 이미지를 보고 어떤 답변을 선택할 것인지 생각해 보세요:",xt,S,Ut,u,El='<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/vqa-example.png" alt="VQA Image Example"/>',$t,P,Yl="질문과 답변의 모호성으로 인해 이러한 데이터세트는 여러 개의 답변이 가능하므로 다중 레이블 분류 문제로 처리됩니다. 게다가, 원핫(one-hot) 인코딩 벡터를 생성하기보다는 레이블에서 특정 답변이 나타나는 횟수를 기반으로 소프트 인코딩을 생성합니다.",Ct,D,Al="위의 예시에서 “아래”라는 답변이 다른 답변보다 훨씬 더 자주 선택되었기 때문에 데이터세트에서 <code>weight</code>라고 불리는 점수로 1.0을 가지며, 나머지 답변들은 1.0 미만의 점수를 가집니다.",Zt,K,ql="적절한 분류 헤더로 모델을 나중에 인스턴스화하기 위해 레이블을 정수로 매핑한 딕셔너리 하나, 반대로 정수를 레이블로 매핑한 딕셔너리 하나 총 2개의 딕셔너리를 생성하세요:",vt,O,_t,ss,Ll="이제 매핑이 완료되었으므로 문자열 답변을 해당 id로 교체하고, 데이터세트의 더 편리한 후처리를 위해 편평화 할 수 있습니다.",Vt,ts,Gt,ls,Wt,es,Sl=`다음 단계는 모델을 위해 이미지와 텍스트 데이터를 준비하기 위해 ViLT 프로세서를 가져오는 것입니다.
<code>ViltProcessor</code>는 BERT 토크나이저와 ViLT 이미지 프로세서를 편리하게 하나의 프로세서로 묶습니다:`,kt,as,It,ns,Pl=`데이터를 전처리하려면 이미지와 질문을 <code>ViltProcessor</code>로 인코딩해야 합니다. 프로세서는 <code>BertTokenizerFast</code>로 텍스트를 토크나이즈하고 텍스트 데이터를 위해 <code>input_ids</code>, <code>attention_mask</code> 및 <code>token_type_ids</code>를 생성합니다.
이미지는 <code>ViltImageProcessor</code>로 이미지를 크기 조정하고 정규화하며, <code>pixel_values</code>와 <code>pixel_mask</code>를 생성합니다.`,Bt,ps,Dl="이런 전처리 단계는 모두 내부에서 이루어지므로, <code>processor</code>를 호출하기만 하면 됩니다. 하지만 아직 타겟 레이블이 완성되지 않았습니다. 타겟의 표현에서 각 요소는 가능한 답변(레이블)에 해당합니다. 정확한 답변의 요소는 해당 점수(weight)를 유지시키고 나머지 요소는 0으로 설정해야 합니다.",Xt,is,Kl="아래 함수가 위에서 설명한대로 이미지와 질문에 <code>processor</code>를 적용하고 레이블을 형식에 맞춥니다:",Qt,ms,Rt,cs,Ol="전체 데이터세트에 전처리 함수를 적용하려면 🤗 Datasets의 <code>map</code> 함수를 사용하십시오. <code>batched=True</code>를 설정하여 데이터세트의 여러 요소를 한 번에 처리함으로써 <code>map</code>을 더 빠르게 할 수 있습니다. 이 시점에서 필요하지 않은 열은 제거하세요.",Ht,rs,Ft,os,se="마지막 단계로, <code>DefaultDataCollator</code>를 사용하여 예제로 쓸 배치를 생성하세요:",Nt,gs,zt,hs,Et,Ms,te="이제 모델을 훈련하기 위해 준비되었습니다! <code>ViltForQuestionAnswering</code>으로 ViLT를 가져올 차례입니다. 레이블의 수와 레이블 매핑을 지정하세요:",Yt,js,At,ds,le="이 시점에서는 다음 세 단계만 남았습니다:",qt,us,ee="<li><code>TrainingArguments</code>에서 훈련 하이퍼파라미터를 정의하세요:</li>",Lt,ys,St,y,ae="<li>모델, 데이터세트, 프로세서, 데이터 콜레이터와 함께 훈련 인수를 <code>Trainer</code>에 전달하세요:</li>",Pt,bs,Dt,b,ne="<li><code>train()</code>을 호출하여 모델을 미세 조정하세요:</li>",Kt,ws,Ot,Js,pe="훈련이 완료되면, <code>push_to_hub()</code> 메소드를 사용하여 🤗 Hub에 모델을 공유하세요:",sl,fs,tl,Ts,ll,xs,ie="ViLT 모델을 미세 조정하고 🤗 Hub에 업로드했다면 추론에 사용할 수 있습니다. 미세 조정된 모델을 추론에 사용해보는 가장 간단한 방법은 <code>Pipeline</code>에서 사용하는 것입니다.",el,Us,al,$s,me="이 가이드의 모델은 200개의 예제에서만 훈련되었으므로 그다지 많은 것을 기대할 수는 없습니다. 데이터세트의 첫 번째 예제를 사용하여 추론 결과를 설명해보겠습니다:",nl,Cs,pl,Zs,ce="비록 확신은 별로 없지만, 모델은 실제로 무언가를 배웠습니다. 더 많은 예제와 더 긴 훈련 기간이 주어진다면 분명 더 나은 결과를 얻을 수 있을 것입니다!",il,vs,re="원한다면 파이프라인의 결과를 수동으로 복제할 수도 있습니다:",ml,_s,oe="<li>이미지와 질문을 가져와서 프로세서를 사용하여 모델에 준비합니다.</li> <li>전처리된 결과를 모델에 전달합니다.</li> <li>로짓에서 가장 가능성 있는 답변의 id를 가져와서 <code>id2label</code>에서 실제 답변을 찾습니다.</li>",cl,Vs,rl,Gs,ol,Ws,ge=`이전 모델은 VQA를 분류 문제로 처리했습니다. BLIP, BLIP-2 및 InstructBLIP와 같은 최근의 모델은 VQA를 생성 작업으로 접근합니다. <a href="../../en/model_doc/blip-2">BLIP-2</a>를 예로 들어 보겠습니다. 이 모델은 사전훈련된 비전 인코더와 LLM의 모든 조합을 사용할 수 있는 새로운 비전-자연어 사전 학습 패러다임을 도입했습니다. (<a href="https://huggingface.co/blog/blip-2" rel="nofollow">BLIP-2 블로그 포스트</a>를 통해 더 자세히 알아볼 수 있어요)
이를 통해 시각적 질의응답을 포함한 여러 비전-자연어 작업에서 SOTA를 달성할 수 있었습니다.`,gl,ks,he="이 모델을 어떻게 VQA에 사용할 수 있는지 설명해 보겠습니다. 먼저 모델을 가져와 보겠습니다. 여기서 GPU가 사용 가능한 경우 모델을 명시적으로 GPU로 전송할 것입니다. 이전에는 훈련할 때 쓰지 않은 이유는 <code>Trainer</code>가 이 부분을 자동으로 처리하기 때문입니다:",hl,Is,Ml,Bs,Me="모델은 이미지와 텍스트를 입력으로 받으므로, VQA 데이터세트의 첫 번째 예제에서와 동일한 이미지/질문 쌍을 사용해 보겠습니다:",jl,Xs,dl,Qs,je="BLIP-2를 시각적 질의응답 작업에 사용하려면 텍스트 프롬프트가 <code>Question: {} Answer:</code> 형식을 따라야 합니다.",ul,Rs,yl,Hs,de="이제 모델의 프로세서로 이미지/프롬프트를 전처리하고, 처리된 입력을 모델을 통해 전달하고, 출력을 디코드해야 합니다:",bl,Fs,wl,Ns,ue="보시다시피 모델은 군중을 인식하고, 얼굴의 방향(아래쪽을 보고 있음)을 인식했지만, 군중이 스케이터 뒤에 있다는 사실을 놓쳤습니다. 그러나 사람이 직접 라벨링한 데이터셋을 얻을 수 없는 경우에, 이 접근법은 빠르게 유용한 결과를 생성할 수 있습니다.",Jl,zs,fl,As,Tl;return w=new Es({props:{title:"시각적 질의응답 (Visual Question Answering)",local:"시각적-질의응답-visual-question-answering",headingTag:"h1"}}),J=new $e({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/visual_question_answering.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/visual_question_answering.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/visual_question_answering.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/visual_question_answering.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/pytorch/visual_question_answering.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/transformers_doc/ko/tensorflow/visual_question_answering.ipynb"}]}}),C=new Es({props:{title:"ViLT 미세 조정",local:"finetuning-vilt",headingTag:"h2"}}),V=new j({props:{code:"cGlwJTIwaW5zdGFsbCUyMC1xJTIwdHJhbnNmb3JtZXJzJTIwZGF0YXNldHM=",highlighted:"pip install -q transformers datasets",wrap:!1}}),W=new j({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()`,wrap:!1}}),I=new j({props:{code:"bW9kZWxfY2hlY2twb2ludCUyMCUzRCUyMCUyMmRhbmRlbGluJTJGdmlsdC1iMzItbWxtJTIy",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>model_checkpoint = <span class="hljs-string">&quot;dandelin/vilt-b32-mlm&quot;</span>',wrap:!1}}),B=new Es({props:{title:"데이터 가져오기",local:"load-the-data",headingTag:"h2"}}),H=new j({props:{code:"ZnJvbSUyMGRhdGFzZXRzJTIwaW1wb3J0JTIwbG9hZF9kYXRhc2V0JTBBJTBBZGF0YXNldCUyMCUzRCUyMGxvYWRfZGF0YXNldCglMjJHcmFwaGNvcmUlMkZ2cWElMjIlMkMlMjBzcGxpdCUzRCUyMnZhbGlkYXRpb24lNUIlM0EyMDAlNUQlMjIpJTBBZGF0YXNldA==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> datasets <span class="hljs-keyword">import</span> load_dataset

<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = load_dataset(<span class="hljs-string">&quot;Graphcore/vqa&quot;</span>, split=<span class="hljs-string">&quot;validation[:200]&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>dataset
Dataset({
    features: [<span class="hljs-string">&#x27;question&#x27;</span>, <span class="hljs-string">&#x27;question_type&#x27;</span>, <span class="hljs-string">&#x27;question_id&#x27;</span>, <span class="hljs-string">&#x27;image_id&#x27;</span>, <span class="hljs-string">&#x27;answer_type&#x27;</span>, <span class="hljs-string">&#x27;label&#x27;</span>],
    num_rows: <span class="hljs-number">200</span>
})`,wrap:!1}}),N=new j({props:{code:"ZGF0YXNldCU1QjAlNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>dataset[<span class="hljs-number">0</span>]
{<span class="hljs-string">&#x27;question&#x27;</span>: <span class="hljs-string">&#x27;Where is he looking?&#x27;</span>,
 <span class="hljs-string">&#x27;question_type&#x27;</span>: <span class="hljs-string">&#x27;none of the above&#x27;</span>,
 <span class="hljs-string">&#x27;question_id&#x27;</span>: <span class="hljs-number">262148000</span>,
 <span class="hljs-string">&#x27;image_id&#x27;</span>: <span class="hljs-string">&#x27;/root/.cache/huggingface/datasets/downloads/extracted/ca733e0e000fb2d7a09fbcc94dbfe7b5a30750681d0e965f8e0a23b1c2f98c75/val2014/COCO_val2014_000000262148.jpg&#x27;</span>,
 <span class="hljs-string">&#x27;answer_type&#x27;</span>: <span class="hljs-string">&#x27;other&#x27;</span>,
 <span class="hljs-string">&#x27;label&#x27;</span>: {<span class="hljs-string">&#x27;ids&#x27;</span>: [<span class="hljs-string">&#x27;at table&#x27;</span>, <span class="hljs-string">&#x27;down&#x27;</span>, <span class="hljs-string">&#x27;skateboard&#x27;</span>, <span class="hljs-string">&#x27;table&#x27;</span>],
  <span class="hljs-string">&#x27;weights&#x27;</span>: [<span class="hljs-number">0.30000001192092896</span>,
   <span class="hljs-number">1.0</span>,
   <span class="hljs-number">0.30000001192092896</span>,
   <span class="hljs-number">0.30000001192092896</span>]}}`,wrap:!1}}),A=new j({props:{code:"ZGF0YXNldCUyMCUzRCUyMGRhdGFzZXQucmVtb3ZlX2NvbHVtbnMoJTVCJ3F1ZXN0aW9uX3R5cGUnJTJDJTIwJ3F1ZXN0aW9uX2lkJyUyQyUyMCdhbnN3ZXJfdHlwZSclNUQp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.remove_columns([<span class="hljs-string">&#x27;question_type&#x27;</span>, <span class="hljs-string">&#x27;question_id&#x27;</span>, <span class="hljs-string">&#x27;answer_type&#x27;</span>])',wrap:!1}}),S=new j({props:{code:"ZnJvbSUyMFBJTCUyMGltcG9ydCUyMEltYWdlJTBBJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKGRhdGFzZXQlNUIwJTVEJTVCJ2ltYWdlX2lkJyU1RCklMEFpbWFnZQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image

<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(dataset[<span class="hljs-number">0</span>][<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>image`,wrap:!1}}),O=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> itertools

<span class="hljs-meta">&gt;&gt;&gt; </span>labels = [item[<span class="hljs-string">&#x27;ids&#x27;</span>] <span class="hljs-keyword">for</span> item <span class="hljs-keyword">in</span> dataset[<span class="hljs-string">&#x27;label&#x27;</span>]]
<span class="hljs-meta">&gt;&gt;&gt; </span>flattened_labels = <span class="hljs-built_in">list</span>(itertools.chain(*labels))
<span class="hljs-meta">&gt;&gt;&gt; </span>unique_labels = <span class="hljs-built_in">list</span>(<span class="hljs-built_in">set</span>(flattened_labels))

<span class="hljs-meta">&gt;&gt;&gt; </span>label2id = {label: idx <span class="hljs-keyword">for</span> idx, label <span class="hljs-keyword">in</span> <span class="hljs-built_in">enumerate</span>(unique_labels)}
<span class="hljs-meta">&gt;&gt;&gt; </span>id2label = {idx: label <span class="hljs-keyword">for</span> label, idx <span class="hljs-keyword">in</span> label2id.items()} `,wrap:!1}}),ts=new j({props:{code:"ZGVmJTIwcmVwbGFjZV9pZHMoaW5wdXRzKSUzQSUwQSUyMCUyMGlucHV0cyU1QiUyMmxhYmVsJTIyJTVEJTVCJTIyaWRzJTIyJTVEJTIwJTNEJTIwJTVCbGFiZWwyaWQlNUJ4JTVEJTIwZm9yJTIweCUyMGluJTIwaW5wdXRzJTVCJTIybGFiZWwlMjIlNUQlNUIlMjJpZHMlMjIlNUQlNUQlMEElMjAlMjByZXR1cm4lMjBpbnB1dHMlMEElMEElMEFkYXRhc2V0JTIwJTNEJTIwZGF0YXNldC5tYXAocmVwbGFjZV9pZHMpJTBBZmxhdF9kYXRhc2V0JTIwJTNEJTIwZGF0YXNldC5mbGF0dGVuKCklMEFmbGF0X2RhdGFzZXQuZmVhdHVyZXM=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">replace_ids</span>(<span class="hljs-params">inputs</span>):
<span class="hljs-meta">... </span>  inputs[<span class="hljs-string">&quot;label&quot;</span>][<span class="hljs-string">&quot;ids&quot;</span>] = [label2id[x] <span class="hljs-keyword">for</span> x <span class="hljs-keyword">in</span> inputs[<span class="hljs-string">&quot;label&quot;</span>][<span class="hljs-string">&quot;ids&quot;</span>]]
<span class="hljs-meta">... </span>  <span class="hljs-keyword">return</span> inputs


<span class="hljs-meta">&gt;&gt;&gt; </span>dataset = dataset.<span class="hljs-built_in">map</span>(replace_ids)
<span class="hljs-meta">&gt;&gt;&gt; </span>flat_dataset = dataset.flatten()
<span class="hljs-meta">&gt;&gt;&gt; </span>flat_dataset.features
{<span class="hljs-string">&#x27;question&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;image_id&#x27;</span>: Value(dtype=<span class="hljs-string">&#x27;string&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;label.ids&#x27;</span>: <span class="hljs-type">Sequence</span>(feature=Value(dtype=<span class="hljs-string">&#x27;int64&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>), length=-<span class="hljs-number">1</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>),
 <span class="hljs-string">&#x27;label.weights&#x27;</span>: <span class="hljs-type">Sequence</span>(feature=Value(dtype=<span class="hljs-string">&#x27;float64&#x27;</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>), length=-<span class="hljs-number">1</span>, <span class="hljs-built_in">id</span>=<span class="hljs-literal">None</span>)}`,wrap:!1}}),ls=new Es({props:{title:"데이터 전처리",local:"preprocessing-data",headingTag:"h2"}}),as=new j({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpbHRQcm9jZXNzb3IlMEElMEFwcm9jZXNzb3IlMjAlM0QlMjBWaWx0UHJvY2Vzc29yLmZyb21fcHJldHJhaW5lZChtb2RlbF9jaGVja3BvaW50KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltProcessor

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(model_checkpoint)`,wrap:!1}}),ms=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">preprocess_data</span>(<span class="hljs-params">examples</span>):
<span class="hljs-meta">... </span>    image_paths = examples[<span class="hljs-string">&#x27;image_id&#x27;</span>]
<span class="hljs-meta">... </span>    images = [Image.<span class="hljs-built_in">open</span>(image_path) <span class="hljs-keyword">for</span> image_path <span class="hljs-keyword">in</span> image_paths]
<span class="hljs-meta">... </span>    texts = examples[<span class="hljs-string">&#x27;question&#x27;</span>]    

<span class="hljs-meta">... </span>    encoding = processor(images, texts, padding=<span class="hljs-string">&quot;max_length&quot;</span>, truncation=<span class="hljs-literal">True</span>, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> k, v <span class="hljs-keyword">in</span> encoding.items():
<span class="hljs-meta">... </span>          encoding[k] = v.squeeze()
    
<span class="hljs-meta">... </span>    targets = []

<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> labels, scores <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(examples[<span class="hljs-string">&#x27;label.ids&#x27;</span>], examples[<span class="hljs-string">&#x27;label.weights&#x27;</span>]):
<span class="hljs-meta">... </span>        target = torch.zeros(<span class="hljs-built_in">len</span>(id2label))

<span class="hljs-meta">... </span>        <span class="hljs-keyword">for</span> label, score <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(labels, scores):
<span class="hljs-meta">... </span>            target[label] = score
      
<span class="hljs-meta">... </span>        targets.append(target)

<span class="hljs-meta">... </span>    encoding[<span class="hljs-string">&quot;labels&quot;</span>] = targets
    
<span class="hljs-meta">... </span>    <span class="hljs-keyword">return</span> encoding`,wrap:!1}}),rs=new j({props:{code:"cHJvY2Vzc2VkX2RhdGFzZXQlMjAlM0QlMjBmbGF0X2RhdGFzZXQubWFwKHByZXByb2Nlc3NfZGF0YSUyQyUyMGJhdGNoZWQlM0RUcnVlJTJDJTIwcmVtb3ZlX2NvbHVtbnMlM0QlNUIncXVlc3Rpb24nJTJDJ3F1ZXN0aW9uX3R5cGUnJTJDJTIwJTIwJ3F1ZXN0aW9uX2lkJyUyQyUyMCdpbWFnZV9pZCclMkMlMjAnYW5zd2VyX3R5cGUnJTJDJTIwJ2xhYmVsLmlkcyclMkMlMjAnbGFiZWwud2VpZ2h0cyclNUQpJTBBcHJvY2Vzc2VkX2RhdGFzZXQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset = flat_dataset.<span class="hljs-built_in">map</span>(preprocess_data, batched=<span class="hljs-literal">True</span>, remove_columns=[<span class="hljs-string">&#x27;question&#x27;</span>,<span class="hljs-string">&#x27;question_type&#x27;</span>,  <span class="hljs-string">&#x27;question_id&#x27;</span>, <span class="hljs-string">&#x27;image_id&#x27;</span>, <span class="hljs-string">&#x27;answer_type&#x27;</span>, <span class="hljs-string">&#x27;label.ids&#x27;</span>, <span class="hljs-string">&#x27;label.weights&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>processed_dataset
Dataset({
    features: [<span class="hljs-string">&#x27;input_ids&#x27;</span>, <span class="hljs-string">&#x27;token_type_ids&#x27;</span>, <span class="hljs-string">&#x27;attention_mask&#x27;</span>, <span class="hljs-string">&#x27;pixel_values&#x27;</span>, <span class="hljs-string">&#x27;pixel_mask&#x27;</span>, <span class="hljs-string">&#x27;labels&#x27;</span>],
    num_rows: <span class="hljs-number">200</span>
})`,wrap:!1}}),gs=new j({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMERlZmF1bHREYXRhQ29sbGF0b3IlMEElMEFkYXRhX2NvbGxhdG9yJTIwJTNEJTIwRGVmYXVsdERhdGFDb2xsYXRvcigp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DefaultDataCollator

<span class="hljs-meta">&gt;&gt;&gt; </span>data_collator = DefaultDataCollator()`,wrap:!1}}),hs=new Es({props:{title:"모델 훈련",local:"train-the-model",headingTag:"h2"}}),js=new j({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFZpbHRGb3JRdWVzdGlvbkFuc3dlcmluZyUwQSUwQW1vZGVsJTIwJTNEJTIwVmlsdEZvclF1ZXN0aW9uQW5zd2VyaW5nLmZyb21fcHJldHJhaW5lZChtb2RlbF9jaGVja3BvaW50JTJDJTIwbnVtX2xhYmVscyUzRGxlbihpZDJsYWJlbCklMkMlMjBpZDJsYWJlbCUzRGlkMmxhYmVsJTJDJTIwbGFiZWwyaWQlM0RsYWJlbDJpZCk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> ViltForQuestionAnswering

<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForQuestionAnswering.from_pretrained(model_checkpoint, num_labels=<span class="hljs-built_in">len</span>(id2label), id2label=id2label, label2id=label2id)`,wrap:!1}}),ys=new j({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

<span class="hljs-meta">&gt;&gt;&gt; </span>repo_id = <span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>

<span class="hljs-meta">&gt;&gt;&gt; </span>training_args = TrainingArguments(
<span class="hljs-meta">... </span>    output_dir=repo_id,
<span class="hljs-meta">... </span>    per_device_train_batch_size=<span class="hljs-number">4</span>,
<span class="hljs-meta">... </span>    num_train_epochs=<span class="hljs-number">20</span>,
<span class="hljs-meta">... </span>    save_steps=<span class="hljs-number">200</span>,
<span class="hljs-meta">... </span>    logging_steps=<span class="hljs-number">50</span>,
<span class="hljs-meta">... </span>    learning_rate=<span class="hljs-number">5e-5</span>,
<span class="hljs-meta">... </span>    save_total_limit=<span class="hljs-number">2</span>,
<span class="hljs-meta">... </span>    remove_unused_columns=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>    push_to_hub=<span class="hljs-literal">True</span>,
<span class="hljs-meta">... </span>)`,wrap:!1}}),bs=new j({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMFRyYWluZXIlMEElMEF0cmFpbmVyJTIwJTNEJTIwVHJhaW5lciglMEElMjAlMjAlMjAlMjBtb2RlbCUzRG1vZGVsJTJDJTBBJTIwJTIwJTIwJTIwYXJncyUzRHRyYWluaW5nX2FyZ3MlMkMlMEElMjAlMjAlMjAlMjBkYXRhX2NvbGxhdG9yJTNEZGF0YV9jb2xsYXRvciUyQyUwQSUyMCUyMCUyMCUyMHRyYWluX2RhdGFzZXQlM0Rwcm9jZXNzZWRfZGF0YXNldCUyQyUwQSUyMCUyMCUyMCUyMHRva2VuaXplciUzRHByb2Nlc3NvciUyQyUwQSk=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> Trainer

<span class="hljs-meta">&gt;&gt;&gt; </span>trainer = Trainer(
<span class="hljs-meta">... </span>    model=model,
<span class="hljs-meta">... </span>    args=training_args,
<span class="hljs-meta">... </span>    data_collator=data_collator,
<span class="hljs-meta">... </span>    train_dataset=processed_dataset,
<span class="hljs-meta">... </span>    tokenizer=processor,
<span class="hljs-meta">... </span>)`,wrap:!1}}),ws=new j({props:{code:"dHJhaW5lci50cmFpbigpJTIw",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.train() ',wrap:!1}}),fs=new j({props:{code:"dHJhaW5lci5wdXNoX3RvX2h1Yigp",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>trainer.push_to_hub()',wrap:!1}}),Ts=new Es({props:{title:"추론",local:"inference",headingTag:"h2"}}),Us=new j({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMHBpcGVsaW5lJTBBJTBBcGlwZSUyMCUzRCUyMHBpcGVsaW5lKCUyMnZpc3VhbC1xdWVzdGlvbi1hbnN3ZXJpbmclMjIlMkMlMjBtb2RlbCUzRCUyMk1hcmlhSyUyRnZpbHRfZmluZXR1bmVkXzIwMCUyMik=",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>pipe = pipeline(<span class="hljs-string">&quot;visual-question-answering&quot;</span>, model=<span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>)`,wrap:!1}}),Cs=new j({props:{code:"ZXhhbXBsZSUyMCUzRCUyMGRhdGFzZXQlNUIwJTVEJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKGV4YW1wbGUlNUInaW1hZ2VfaWQnJTVEKSUwQXF1ZXN0aW9uJTIwJTNEJTIwZXhhbXBsZSU1QidxdWVzdGlvbiclNUQlMEFwcmludChxdWVzdGlvbiklMEFwaXBlKGltYWdlJTJDJTIwcXVlc3Rpb24lMkMlMjB0b3BfayUzRDEp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>example = dataset[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(example[<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>question = example[<span class="hljs-string">&#x27;question&#x27;</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(question)
<span class="hljs-meta">&gt;&gt;&gt; </span>pipe(image, question, top_k=<span class="hljs-number">1</span>)
<span class="hljs-string">&quot;Where is he looking?&quot;</span>
[{<span class="hljs-string">&#x27;score&#x27;</span>: <span class="hljs-number">0.5498199462890625</span>, <span class="hljs-string">&#x27;answer&#x27;</span>: <span class="hljs-string">&#x27;down&#x27;</span>}]`,wrap:!1}}),Vs=new j({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>processor = ViltProcessor.from_pretrained(<span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(example[<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>question = example[<span class="hljs-string">&#x27;question&#x27;</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare inputs</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, question, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>model = ViltForQuestionAnswering.from_pretrained(<span class="hljs-string">&quot;MariaK/vilt_finetuned_200&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits
<span class="hljs-meta">&gt;&gt;&gt; </span>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 answer:&quot;</span>, model.config.id2label[idx])
Predicted answer: down`,wrap:!1}}),Gs=new Es({props:{title:"제로샷 VQA",local:"zeroshot-vqa",headingTag:"h2"}}),Is=new j({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> AutoProcessor, Blip2ForConditionalGeneration
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>processor = AutoProcessor.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Blip2ForConditionalGeneration.from_pretrained(<span class="hljs-string">&quot;Salesforce/blip2-opt-2.7b&quot;</span>, torch_dtype=torch.float16)
<span class="hljs-meta">&gt;&gt;&gt; </span>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">&gt;&gt;&gt; </span>model.to(device)`,wrap:!1}}),Xs=new j({props:{code:"ZXhhbXBsZSUyMCUzRCUyMGRhdGFzZXQlNUIwJTVEJTBBaW1hZ2UlMjAlM0QlMjBJbWFnZS5vcGVuKGV4YW1wbGUlNUInaW1hZ2VfaWQnJTVEKSUwQXF1ZXN0aW9uJTIwJTNEJTIwZXhhbXBsZSU1QidxdWVzdGlvbiclNUQ=",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>example = dataset[<span class="hljs-number">0</span>]
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(example[<span class="hljs-string">&#x27;image_id&#x27;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>question = example[<span class="hljs-string">&#x27;question&#x27;</span>]`,wrap:!1}}),Rs=new j({props:{code:"cHJvbXB0JTIwJTNEJTIwZiUyMlF1ZXN0aW9uJTNBJTIwJTdCcXVlc3Rpb24lN0QlMjBBbnN3ZXIlM0ElMjIlMjA=",highlighted:'<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">f&quot;Question: <span class="hljs-subst">{question}</span> Answer:&quot;</span> ',wrap:!1}}),Fs=new j({props:{code:"aW5wdXRzJTIwJTNEJTIwcHJvY2Vzc29yKGltYWdlJTJDJTIwdGV4dCUzRHByb21wdCUyQyUyMHJldHVybl90ZW5zb3JzJTNEJTIycHQlMjIpLnRvKGRldmljZSUyQyUyMHRvcmNoLmZsb2F0MTYpJTBBJTBBZ2VuZXJhdGVkX2lkcyUyMCUzRCUyMG1vZGVsLmdlbmVyYXRlKCoqaW5wdXRzJTJDJTIwbWF4X25ld190b2tlbnMlM0QxMCklMEFnZW5lcmF0ZWRfdGV4dCUyMCUzRCUyMHByb2Nlc3Nvci5iYXRjaF9kZWNvZGUoZ2VuZXJhdGVkX2lkcyUyQyUyMHNraXBfc3BlY2lhbF90b2tlbnMlM0RUcnVlKSU1QjAlNUQuc3RyaXAoKSUwQXByaW50KGdlbmVyYXRlZF90ZXh0KQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = processor(image, text=prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).to(device, torch.float16)

<span class="hljs-meta">&gt;&gt;&gt; </span>generated_ids = model.generate(**inputs, max_new_tokens=<span class="hljs-number">10</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>generated_text = processor.batch_decode(generated_ids, skip_special_tokens=<span class="hljs-literal">True</span>)[<span class="hljs-number">0</span>].strip()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(generated_text)
<span class="hljs-string">&quot;He is looking at the crowd&quot;</span> `,wrap:!1}}),zs=new 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