import{s as Qt,o as qt,n as X}from"../chunks/scheduler.bdbef820.js";import{S as Et,i as Pt,g as c,s as i,r as g,A as At,h as m,f as s,c as l,j as G,u,x as b,k as B,y as p,a as r,v as f,d as _,t as w,w as v}from"../chunks/index.33f81d56.js";import{T as Je}from"../chunks/Tip.34194030.js";import{D as ve}from"../chunks/Docstring.64554317.js";import{C as ot}from"../chunks/CodeBlock.362b34a4.js";import{E as tt}from"../chunks/ExampleCodeBlock.4f2252c6.js";import{P as Dt}from"../chunks/PipelineTag.d2f354cd.js";import{H as be,E as Ot}from"../chunks/EditOnGithub.a9246e21.js";function Kt($){let t,h="Example:",a,d,M;return d=new ot({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> Swinv2Config, Swinv2Model

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Swinv2 microsoft/swinv2-tiny-patch4-window8-256 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = Swinv2Config()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the microsoft/swinv2-tiny-patch4-window8-256 style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swinv2Model(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,a=i(),g(d.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-11lpom8"&&(t.textContent=h),a=l(n),u(d.$$.fragment,n)},m(n,y){r(n,t,y),r(n,a,y),f(d,n,y),M=!0},p:X,i(n){M||(_(d.$$.fragment,n),M=!0)},o(n){w(d.$$.fragment,n),M=!1},d(n){n&&(s(t),s(a)),v(d,n)}}}function eo($){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=c("p"),t.innerHTML=h},l(a){t=m(a,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(a,d){r(a,t,d)},p:X,d(a){a&&s(t)}}}function to($){let t,h="Example:",a,d,M;return d=new ot({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> AutoImageProcessor, Swinv2Model
<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">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;huggingface/cats-image&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/swinv2-tiny-patch4-window8-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swinv2Model.from_pretrained(<span class="hljs-string">&quot;microsoft/swinv2-tiny-patch4-window8-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</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>last_hidden_states = outputs.last_hidden_state
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">64</span>, <span class="hljs-number">768</span>]`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,a=i(),g(d.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-11lpom8"&&(t.textContent=h),a=l(n),u(d.$$.fragment,n)},m(n,y){r(n,t,y),r(n,a,y),f(d,n,y),M=!0},p:X,i(n){M||(_(d.$$.fragment,n),M=!0)},o(n){w(d.$$.fragment,n),M=!1},d(n){n&&(s(t),s(a)),v(d,n)}}}function oo($){let t,h=`Note that we provide a script to pre-train this model on custom data in our <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining" rel="nofollow">examples
directory</a>.`;return{c(){t=c("p"),t.innerHTML=h},l(a){t=m(a,"P",{"data-svelte-h":!0}),b(t)!=="svelte-7i3y9o"&&(t.innerHTML=h)},m(a,d){r(a,t,d)},p:X,d(a){a&&s(t)}}}function no($){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=c("p"),t.innerHTML=h},l(a){t=m(a,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(a,d){r(a,t,d)},p:X,d(a){a&&s(t)}}}function so($){let t,h="Examples:",a,d,M;return d=new ot({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> AutoImageProcessor, Swinv2ForMaskedImageModeling
<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">from</span> PIL <span class="hljs-keyword">import</span> Image
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests

<span class="hljs-meta">&gt;&gt;&gt; </span>url = <span class="hljs-string">&quot;http://images.cocodataset.org/val2017/000000039769.jpg&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>image = Image.<span class="hljs-built_in">open</span>(requests.get(url, stream=<span class="hljs-literal">True</span>).raw)

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/swinv2-tiny-patch4-window8-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swinv2ForMaskedImageModeling.from_pretrained(<span class="hljs-string">&quot;microsoft/swinv2-tiny-patch4-window8-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>num_patches = (model.config.image_size // model.config.patch_size) ** <span class="hljs-number">2</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>pixel_values = image_processor(images=image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>).pixel_values
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># create random boolean mask of shape (batch_size, num_patches)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>bool_masked_pos = torch.randint(low=<span class="hljs-number">0</span>, high=<span class="hljs-number">2</span>, size=(<span class="hljs-number">1</span>, num_patches)).<span class="hljs-built_in">bool</span>()

<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(pixel_values, bool_masked_pos=bool_masked_pos)
<span class="hljs-meta">&gt;&gt;&gt; </span>loss, reconstructed_pixel_values = outputs.loss, outputs.reconstruction
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">list</span>(reconstructed_pixel_values.shape)
[<span class="hljs-number">1</span>, <span class="hljs-number">3</span>, <span class="hljs-number">256</span>, <span class="hljs-number">256</span>]`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,a=i(),g(d.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-kvfsh7"&&(t.textContent=h),a=l(n),u(d.$$.fragment,n)},m(n,y){r(n,t,y),r(n,a,y),f(d,n,y),M=!0},p:X,i(n){M||(_(d.$$.fragment,n),M=!0)},o(n){w(d.$$.fragment,n),M=!1},d(n){n&&(s(t),s(a)),v(d,n)}}}function ao($){let t,h=`Note that it’s possible to fine-tune SwinV2 on higher resolution images than the ones it has been trained on, by
setting <code>interpolate_pos_encoding</code> to <code>True</code> in the forward of the model. This will interpolate the pre-trained
position embeddings to the higher resolution.`;return{c(){t=c("p"),t.innerHTML=h},l(a){t=m(a,"P",{"data-svelte-h":!0}),b(t)!=="svelte-1dn4x3r"&&(t.innerHTML=h)},m(a,d){r(a,t,d)},p:X,d(a){a&&s(t)}}}function ro($){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){t=c("p"),t.innerHTML=h},l(a){t=m(a,"P",{"data-svelte-h":!0}),b(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(a,d){r(a,t,d)},p:X,d(a){a&&s(t)}}}function io($){let t,h="Example:",a,d,M;return d=new ot({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> AutoImageProcessor, Swinv2ForImageClassification
<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">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;huggingface/cats-image&quot;</span>, trust_remote_code=<span class="hljs-literal">True</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>image = dataset[<span class="hljs-string">&quot;test&quot;</span>][<span class="hljs-string">&quot;image&quot;</span>][<span class="hljs-number">0</span>]

<span class="hljs-meta">&gt;&gt;&gt; </span>image_processor = AutoImageProcessor.from_pretrained(<span class="hljs-string">&quot;microsoft/swinv2-tiny-patch4-window8-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = Swinv2ForImageClassification.from_pretrained(<span class="hljs-string">&quot;microsoft/swinv2-tiny-patch4-window8-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(image, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

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

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># model predicts one of the 1000 ImageNet classes</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>predicted_label = logits.argmax(-<span class="hljs-number">1</span>).item()
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(model.config.id2label[predicted_label])
Egyptian cat`,wrap:!1}}),{c(){t=c("p"),t.textContent=h,a=i(),g(d.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),b(t)!=="svelte-11lpom8"&&(t.textContent=h),a=l(n),u(d.$$.fragment,n)},m(n,y){r(n,t,y),r(n,a,y),f(d,n,y),M=!0},p:X,i(n){M||(_(d.$$.fragment,n),M=!0)},o(n){w(d.$$.fragment,n),M=!1},d(n){n&&(s(t),s(a)),v(d,n)}}}function lo($){let t,h,a,d,M,n,y,Ue,A,kt='Swin Transformer V2는  Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, Furu Wei, Baining Guo가 제안한 논문 <a href="https://arxiv.org/abs/2111.09883" rel="nofollow">Swin Transformer V2: Scaling Up Capacity and Resolution</a>에서 소개되었습니다.',We,D,jt="논문의 초록은 다음과 같습니다:",Ze,O,St="<em>대규모 NLP 모델들은 언어 작업에서의 성능을 크게 향상하며, 성능이 포화하는 징후를 보이지 않습니다. 또한, 사람과 유사한 few-shot 학습 능력을 보여줍니다. 이 논문은 대규모 모델을 컴퓨터 비전 분야에서 탐구하고자 합니다. 대형 비전 모델을 훈련하고 적용하는 데 있어 세 가지 주요 문제를 다룹니다: 훈련 불안정성, 사전 학습과 파인튜닝 간의 해상도 차이, 그리고 레이블이 달린 데이터에 대한 높은 요구입니다. 세 가지 주요 기법을 제안합니다: 1) 훈련 안정성을 개선하기 위한 residual-post-norm 방법과 cosine attention의 결합; 2) 저해상도 이미지로 사전 학습된 모델을 고해상도 입력으로 전이할 수 있는 log-spaced continuous position bias 방법; 3) 레이블이 달린 방대한 이미지의 필요성을 줄이기 위한 self-supervised 사전 학습 방법인 SimMIM입니다. 이러한 기법들을 통해 30억 개의 파라미터를 가진 Swin Transformer V2 모델을 성공적으로 훈련하였으며, 이는 현재까지 가장 크고 고밀도의 비전 모델로, 최대 1,536×1,536 해상도의 이미지를 다룰 수 있습니다. 이 모델은 ImageNet-V2 이미지 분류, COCO 객체 탐지, ADE20K 의미론적 분할, Kinetics-400 비디오 행동 분류 등 네 가지 대표적인 비전 작업에서 새로운 성능 기록을 세웠습니다. 또한, 우리의 훈련은 Google의 billion-level 비전 모델과 비교해 40배 적은 레이블이 달린 데이터와 40배 적은 훈련 시간으로 이루어졌다는 점에서 훨씬 더 효율적입니다.</em>",ze,K,Ct=`이 모델은 <a href="https://huggingface.co/nandwalritik" rel="nofollow">nandwalritik</a>이 기여하였습니다.
원본 코드는 <a href="https://github.com/microsoft/Swin-Transformer" rel="nofollow">여기</a>에서 확인할 수 있습니다.`,Ne,ee,Ge,te,xt="Swin Transformer v2의 사용을 도울 수 있는 Hugging Face 및 커뮤니티(🌎로 표시)의 공식 자료 목록입니다.",Be,oe,Xe,ne,Ft='<li><a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a>은 이 <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-classification" rel="nofollow">예제 스크립트</a>와 <a href="https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/image_classification.ipynb" rel="nofollow">노트북</a>을 통해 지원됩니다.</li> <li>관련 자료: <a href="../tasks/image_classification">이미지 분류 작업 가이드</a></li>',Re,se,It="또한:",Ve,ae,Jt='<li><a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a>는 이 <a href="https://github.com/huggingface/transformers/tree/main/examples/pytorch/image-pretraining" rel="nofollow">예제 스크립트</a>를 통해 지원됩니다.</li>',Ye,re,Ut="새로운 자료를 추가하고 싶으시다면, 언제든지 Pull Request를 열어주세요! 저희가 검토해 드릴게요. 이때, 추가하는 자료는 기존 자료와 중복되지 않고 새로운 내용을 보여주는 자료여야 합니다.",He,ie,Le,j,le,nt,Me,Wt=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a>. It is used to instantiate a Swin
Transformer v2 model according to the specified arguments, defining the model architecture. Instantiating a
configuration with the defaults will yield a similar configuration to that of the Swin Transformer v2
<a href="https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256" rel="nofollow">microsoft/swinv2-tiny-patch4-window8-256</a>
architecture.`,st,ye,Zt=`Configuration objects inherit from <code>PretrainedConfig</code> and can be used to control the model outputs. Read the
documentation from <code>PretrainedConfig</code> for more information.`,at,R,Qe,de,qe,J,ce,rt,$e,zt=`The bare Swinv2 Model transformer outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,it,x,me,lt,Te,Nt='The <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Model">Swinv2Model</a> forward method, overrides the <code>__call__</code> special method.',dt,V,ct,Y,Ee,pe,Pe,T,he,mt,ke,Gt=`Swinv2 Model with a decoder on top for masked image modeling, as proposed in
<a href="https://arxiv.org/abs/2111.09886" rel="nofollow">SimMIM</a>.`,pt,H,ht,je,Bt=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,gt,F,ge,ut,Se,Xt='The <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForMaskedImageModeling">Swinv2ForMaskedImageModeling</a> forward method, overrides the <code>__call__</code> special method.',ft,L,_t,Q,Ae,ue,De,k,fe,wt,Ce,Rt=`Swinv2 Model transformer with an image classification head on top (a linear layer on top of the final hidden state
of the [CLS] token) e.g. for ImageNet.`,vt,q,bt,xe,Vt=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Mt,I,_e,yt,Fe,Yt='The <a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2ForImageClassification">Swinv2ForImageClassification</a> forward method, overrides the <code>__call__</code> special method.',$t,E,Tt,P,Oe,we,Ke,Ie,et;return M=new be({props:{title:"Swin Transformer V2",local:"swin-transformer-v2",headingTag:"h1"}}),y=new be({props:{title:"개요",local:"overview",headingTag:"h2"}}),ee=new be({props:{title:"리소스",local:"resources",headingTag:"h2"}}),oe=new Dt({props:{pipeline:"image-classification"}}),ie=new be({props:{title:"Swinv2Config",local:"transformers.Swinv2Config ][ transformers.Swinv2Config",headingTag:"h2"}}),le=new ve({props:{name:"class transformers.Swinv2Config",anchor:"transformers.Swinv2Config",parameters:[{name:"image_size",val:" = 224"},{name:"patch_size",val:" = 4"},{name:"num_channels",val:" = 3"},{name:"embed_dim",val:" = 96"},{name:"depths",val:" = [2, 2, 6, 2]"},{name:"num_heads",val:" = [3, 6, 12, 24]"},{name:"window_size",val:" = 7"},{name:"pretrained_window_sizes",val:" = [0, 0, 0, 0]"},{name:"mlp_ratio",val:" = 4.0"},{name:"qkv_bias",val:" = True"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"drop_path_rate",val:" = 0.1"},{name:"hidden_act",val:" = 'gelu'"},{name:"use_absolute_embeddings",val:" = False"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"encoder_stride",val:" = 32"},{name:"out_features",val:" = None"},{name:"out_indices",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.Swinv2Config.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 224) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.Swinv2Config.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.Swinv2Config.num_channels",description:`<strong>num_channels</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The number of input channels.`,name:"num_channels"},{anchor:"transformers.Swinv2Config.embed_dim",description:`<strong>embed_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 96) &#x2014;
Dimensionality of patch embedding.`,name:"embed_dim"},{anchor:"transformers.Swinv2Config.depths",description:`<strong>depths</strong> (<code>list(int)</code>, <em>optional</em>, defaults to <code>[2, 2, 6, 2]</code>) &#x2014;
Depth of each layer in the Transformer encoder.`,name:"depths"},{anchor:"transformers.Swinv2Config.num_heads",description:`<strong>num_heads</strong> (<code>list(int)</code>, <em>optional</em>, defaults to <code>[3, 6, 12, 24]</code>) &#x2014;
Number of attention heads in each layer of the Transformer encoder.`,name:"num_heads"},{anchor:"transformers.Swinv2Config.window_size",description:`<strong>window_size</strong> (<code>int</code>, <em>optional</em>, defaults to 7) &#x2014;
Size of windows.`,name:"window_size"},{anchor:"transformers.Swinv2Config.pretrained_window_sizes",description:`<strong>pretrained_window_sizes</strong> (<code>list(int)</code>, <em>optional</em>, defaults to <code>[0, 0, 0, 0]</code>) &#x2014;
Size of windows during pretraining.`,name:"pretrained_window_sizes"},{anchor:"transformers.Swinv2Config.mlp_ratio",description:`<strong>mlp_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 4.0) &#x2014;
Ratio of MLP hidden dimensionality to embedding dimensionality.`,name:"mlp_ratio"},{anchor:"transformers.Swinv2Config.qkv_bias",description:`<strong>qkv_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not a learnable bias should be added to the queries, keys and values.`,name:"qkv_bias"},{anchor:"transformers.Swinv2Config.hidden_dropout_prob",description:`<strong>hidden_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probability for all fully connected layers in the embeddings and encoder.`,name:"hidden_dropout_prob"},{anchor:"transformers.Swinv2Config.attention_probs_dropout_prob",description:`<strong>attention_probs_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_probs_dropout_prob"},{anchor:"transformers.Swinv2Config.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
Stochastic depth rate.`,name:"drop_path_rate"},{anchor:"transformers.Swinv2Config.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;gelu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder. If string, <code>&quot;gelu&quot;</code>, <code>&quot;relu&quot;</code>,
<code>&quot;selu&quot;</code> and <code>&quot;gelu_new&quot;</code> are supported.`,name:"hidden_act"},{anchor:"transformers.Swinv2Config.use_absolute_embeddings",description:`<strong>use_absolute_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add absolute position embeddings to the patch embeddings.`,name:"use_absolute_embeddings"},{anchor:"transformers.Swinv2Config.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.Swinv2Config.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-05) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.Swinv2Config.encoder_stride",description:`<strong>encoder_stride</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Factor to increase the spatial resolution by in the decoder head for masked image modeling.`,name:"encoder_stride"},{anchor:"transformers.Swinv2Config.out_features",description:`<strong>out_features</strong> (<code>List[str]</code>, <em>optional</em>) &#x2014;
If used as backbone, list of features to output. Can be any of <code>&quot;stem&quot;</code>, <code>&quot;stage1&quot;</code>, <code>&quot;stage2&quot;</code>, etc.
(depending on how many stages the model has). If unset and <code>out_indices</code> is set, will default to the
corresponding stages. If unset and <code>out_indices</code> is unset, will default to the last stage.`,name:"out_features"},{anchor:"transformers.Swinv2Config.out_indices",description:`<strong>out_indices</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
If used as backbone, list of indices of features to output. Can be any of 0, 1, 2, etc. (depending on how
many stages the model has). If unset and <code>out_features</code> is set, will default to the corresponding stages.
If unset and <code>out_features</code> is unset, will default to the last stage.`,name:"out_indices"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swinv2/configuration_swinv2.py#L25"}}),R=new tt({props:{anchor:"transformers.Swinv2Config.example",$$slots:{default:[Kt]},$$scope:{ctx:$}}}),de=new be({props:{title:"Swinv2Model",local:"transformers.Swinv2Model ][ transformers.Swinv2Model",headingTag:"h2"}}),ce=new ve({props:{name:"class transformers.Swinv2Model",anchor:"transformers.Swinv2Model",parameters:[{name:"config",val:""},{name:"add_pooling_layer",val:" = True"},{name:"use_mask_token",val:" = False"}],parametersDescription:[{anchor:"transformers.Swinv2Model.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <code>from_pretrained()</code> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swinv2/modeling_swinv2.py#L1037"}}),me=new ve({props:{name:"forward",anchor:"transformers.Swinv2Model.forward",parameters:[{name:"pixel_values",val:": Optional = None"},{name:"bool_masked_pos",val:": Optional = None"},{name:"head_mask",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"interpolate_pos_encoding",val:": bool = False"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.Swinv2Model.forward.pixel_values",description:`<strong>pixel_values</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) &#x2014;
Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.Swinv2Model.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.Swinv2Model.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.Swinv2Model.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.Swinv2Model.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, default <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.Swinv2Model.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <code>ModelOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Swinv2Model.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, num_patches)</code>, <em>optional</em>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swinv2/modeling_swinv2.py#L1069",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.swinv2.modeling_swinv2.Swinv2ModelOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config"
>Swinv2Config</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>pooler_output</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, hidden_size)</code>, <em>optional</em>, returned when <code>add_pooling_layer=True</code> is passed) — Average pooling of the last layer hidden-state.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each stage) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each stage) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>reshaped_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each stage) of
shape <code>(batch_size, hidden_size, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.swinv2.modeling_swinv2.Swinv2ModelOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),V=new Je({props:{$$slots:{default:[eo]},$$scope:{ctx:$}}}),Y=new tt({props:{anchor:"transformers.Swinv2Model.forward.example",$$slots:{default:[to]},$$scope:{ctx:$}}}),pe=new be({props:{title:"Swinv2ForMaskedImageModeling",local:"transformers.Swinv2ForMaskedImageModeling ][ transformers.Swinv2ForMaskedImageModeling",headingTag:"h2"}}),he=new ve({props:{name:"class transformers.Swinv2ForMaskedImageModeling",anchor:"transformers.Swinv2ForMaskedImageModeling",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.Swinv2ForMaskedImageModeling.config",description:`<strong>config</strong> (<a href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config">Swinv2Config</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.Swinv2ForMaskedImageModeling.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.Swinv2ForMaskedImageModeling.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
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Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.Swinv2ForMaskedImageModeling.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, default <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.Swinv2ForMaskedImageModeling.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <code>ModelOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Swinv2ForMaskedImageModeling.forward.bool_masked_pos",description:`<strong>bool_masked_pos</strong> (<code>torch.BoolTensor</code> of shape <code>(batch_size, num_patches)</code>) &#x2014;
Boolean masked positions. Indicates which patches are masked (1) and which aren&#x2019;t (0).`,name:"bool_masked_pos"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swinv2/modeling_swinv2.py#L1173",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.swinv2.modeling_swinv2.Swinv2MaskedImageModelingOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config"
>Swinv2Config</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>bool_masked_pos</code> is provided) — Masked image modeling (MLM) loss.</p>
</li>
<li>
<p><strong>reconstruction</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) — Reconstructed pixel values.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each stage) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each stage) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>reshaped_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each stage) of
shape <code>(batch_size, hidden_size, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.swinv2.modeling_swinv2.Swinv2MaskedImageModelingOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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Pixel values. Pixel values can be obtained using <a href="/docs/transformers/main/ko/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/main/ko/model_doc/vit#transformers.ViTFeatureExtractor.__call__">ViTImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.Swinv2ForImageClassification.forward.head_mask",description:`<strong>head_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(num_heads,)</code> or <code>(num_layers, num_heads)</code>, <em>optional</em>) &#x2014;
Mask to nullify selected heads of the self-attention modules. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"head_mask"},{anchor:"transformers.Swinv2ForImageClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.Swinv2ForImageClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.Swinv2ForImageClassification.forward.interpolate_pos_encoding",description:`<strong>interpolate_pos_encoding</strong> (<code>bool</code>, <em>optional</em>, default <code>False</code>) &#x2014;
Whether to interpolate the pre-trained position encodings.`,name:"interpolate_pos_encoding"},{anchor:"transformers.Swinv2ForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <code>ModelOutput</code> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.Swinv2ForImageClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the image classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/swinv2/modeling_swinv2.py#L1293",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <code>transformers.models.swinv2.modeling_swinv2.Swinv2ImageClassifierOutput</code> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/main/ko/model_doc/swinv2#transformers.Swinv2Config"
>Swinv2Config</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each stage) of
shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each stage) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
<li>
<p><strong>reshaped_hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings + one for the output of each stage) of
shape <code>(batch_size, hidden_size, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs reshaped to
include the spatial dimensions.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>transformers.models.swinv2.modeling_swinv2.Swinv2ImageClassifierOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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