import{S as Ss,i as Ns,s as Bs,e as s,k as f,w as M,t as i,M as Ps,c as n,d as o,m as u,a as r,x as T,h as l,b as d,G as e,g as b,y as V,q as w,o as y,B as $,v as Gs,L as ro}from"../../chunks/vendor-hf-doc-builder.js";import{T as qa}from"../../chunks/Tip-hf-doc-builder.js";import{D as Ae}from"../../chunks/Docstring-hf-doc-builder.js";import{C as io}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as ct}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as no}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function zs(j){let c,v,g,m,_;return m=new io({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> MobileViTV2Config, MobileViTV2Model

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a mobilevitv2-small style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = MobileViTV2Config()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the mobilevitv2-small style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTV2Model(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`}}),{c(){c=s("p"),v=i("Example:"),g=f(),M(m.$$.fragment)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Example:"),p.forEach(o),g=u(a),T(m.$$.fragment,a)},m(a,p){b(a,c,p),e(c,v),b(a,g,p),V(m,a,p),_=!0},p:ro,i(a){_||(w(m.$$.fragment,a),_=!0)},o(a){y(m.$$.fragment,a),_=!1},d(a){a&&o(c),a&&o(g),$(m,a)}}}function As(j){let c,v,g,m,_;return{c(){c=s("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s("code"),m=i("Module"),_=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n(p,"CODE",{});var C=r(g);m=l(C,"Module"),C.forEach(o),_=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){b(a,c,p),e(c,v),e(c,g),e(g,m),e(c,_)},d(a){a&&o(c)}}}function Rs(j){let c,v,g,m,_;return m=new io({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, MobileViTV2Model
<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>)
<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;apple/mobilevitv2-1.0-imagenet1k-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTV2Model.from_pretrained(<span class="hljs-string">&quot;apple/mobilevitv2-1.0-imagenet1k-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">512</span>, <span class="hljs-number">8</span>, <span class="hljs-number">8</span>]`}}),{c(){c=s("p"),v=i("Example:"),g=f(),M(m.$$.fragment)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Example:"),p.forEach(o),g=u(a),T(m.$$.fragment,a)},m(a,p){b(a,c,p),e(c,v),b(a,g,p),V(m,a,p),_=!0},p:ro,i(a){_||(w(m.$$.fragment,a),_=!0)},o(a){y(m.$$.fragment,a),_=!1},d(a){a&&o(c),a&&o(g),$(m,a)}}}function Xs(j){let c,v,g,m,_;return{c(){c=s("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s("code"),m=i("Module"),_=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n(p,"CODE",{});var C=r(g);m=l(C,"Module"),C.forEach(o),_=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){b(a,c,p),e(c,v),e(c,g),e(g,m),e(c,_)},d(a){a&&o(c)}}}function Ys(j){let c,v,g,m,_;return m=new io({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, MobileViTV2ForImageClassification
<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>)
<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;apple/mobilevitv2-1.0-imagenet1k-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTV2ForImageClassification.from_pretrained(<span class="hljs-string">&quot;apple/mobilevitv2-1.0-imagenet1k-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])
tabby, tabby cat`}}),{c(){c=s("p"),v=i("Example:"),g=f(),M(m.$$.fragment)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Example:"),p.forEach(o),g=u(a),T(m.$$.fragment,a)},m(a,p){b(a,c,p),e(c,v),b(a,g,p),V(m,a,p),_=!0},p:ro,i(a){_||(w(m.$$.fragment,a),_=!0)},o(a){y(m.$$.fragment,a),_=!1},d(a){a&&o(c),a&&o(g),$(m,a)}}}function Qs(j){let c,v,g,m,_;return{c(){c=s("p"),v=i("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=s("code"),m=i("Module"),_=i(`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n(p,"CODE",{});var C=r(g);m=l(C,"Module"),C.forEach(o),_=l(p,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),p.forEach(o)},m(a,p){b(a,c,p),e(c,v),e(c,g),e(g,m),e(c,_)},d(a){a&&o(c)}}}function Os(j){let c,v,g,m,_;return m=new io({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> requests
<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">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, MobileViTV2ForSemanticSegmentation

<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;apple/mobilevitv2-1.0-imagenet1k-256&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = MobileViTV2ForSemanticSegmentation.from_pretrained(<span class="hljs-string">&quot;apple/mobilevitv2-1.0-imagenet1k-256&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = image_processor(images=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><span class="hljs-comment"># logits are of shape (batch_size, num_labels, height, width)</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`}}),{c(){c=s("p"),v=i("Examples:"),g=f(),M(m.$$.fragment)},l(a){c=n(a,"P",{});var p=r(c);v=l(p,"Examples:"),p.forEach(o),g=u(a),T(m.$$.fragment,a)},m(a,p){b(a,c,p),e(c,v),b(a,g,p),V(m,a,p),_=!0},p:ro,i(a){_||(w(m.$$.fragment,a),_=!0)},o(a){y(m.$$.fragment,a),_=!1},d(a){a&&o(c),a&&o(g),$(m,a)}}}function qs(j){let c,v,g,m,_,a,p,C,lo,Et,S,O,dt,fe,co,mt,mo,xt,q,po,ue,ho,fo,Ut,Re,uo,Zt,Xe,go,Ft,Ye,pt,bo,St,Qe,_o,Nt,W,ht,vo,Mo,ge,To,Oe,Vo,wo,yo,be,$o,_e,jo,Co,ko,N,Jo,ve,Io,Wo,Me,Eo,xo,Bt,F,Uo,Te,Zo,Fo,Ve,So,No,Pt,B,H,ft,we,Bo,ut,Po,Gt,k,ye,Go,P,zo,qe,Ao,Ro,$e,Xo,Yo,Qo,G,Oo,He,qo,Ho,Le,Lo,Do,Ko,L,zt,z,D,gt,je,ea,bt,ta,At,Z,Ce,oa,ke,aa,Je,sa,na,ra,E,Ie,ia,A,la,De,ca,da,_t,ma,pa,ha,K,fa,ee,Rt,R,te,vt,We,ua,Mt,ga,Xt,J,Ee,ba,Tt,_a,va,xe,Ma,Ue,Ta,Va,wa,x,Ze,ya,X,$a,Ke,ja,Ca,Vt,ka,Ja,Ia,oe,Wa,ae,Yt,Y,se,wt,Fe,Ea,yt,xa,Qt,I,Se,Ua,$t,Za,Fa,Ne,Sa,Be,Na,Ba,Pa,U,Pe,Ga,Q,za,et,Aa,Ra,jt,Xa,Ya,Qa,ne,Oa,re,Ot;return a=new ct({}),fe=new ct({}),we=new ct({}),ye=new Ae({props:{name:"class transformers.MobileViTV2Config",anchor:"transformers.MobileViTV2Config",parameters:[{name:"num_channels",val:" = 3"},{name:"image_size",val:" = 256"},{name:"patch_size",val:" = 2"},{name:"expand_ratio",val:" = 2.0"},{name:"hidden_act",val:" = 'swish'"},{name:"conv_kernel_size",val:" = 3"},{name:"output_stride",val:" = 32"},{name:"classifier_dropout_prob",val:" = 0.1"},{name:"initializer_range",val:" = 0.02"},{name:"layer_norm_eps",val:" = 1e-05"},{name:"aspp_out_channels",val:" = 512"},{name:"atrous_rates",val:" = [6, 12, 18]"},{name:"aspp_dropout_prob",val:" = 0.1"},{name:"semantic_loss_ignore_index",val:" = 255"},{name:"n_attn_blocks",val:" = [2, 4, 3]"},{name:"base_attn_unit_dims",val:" = [128, 192, 256]"},{name:"width_multiplier",val:" = 1.0"},{name:"ffn_multiplier",val:" = 2"},{name:"attn_dropout",val:" = 0.0"},{name:"ffn_dropout",val:" = 0.0"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.MobileViTV2Config.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.MobileViTV2Config.image_size",description:`<strong>image_size</strong> (<code>int</code>, <em>optional</em>, defaults to 256) &#x2014;
The size (resolution) of each image.`,name:"image_size"},{anchor:"transformers.MobileViTV2Config.patch_size",description:`<strong>patch_size</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The size (resolution) of each patch.`,name:"patch_size"},{anchor:"transformers.MobileViTV2Config.expand_ratio",description:`<strong>expand_ratio</strong> (<code>float</code>, <em>optional</em>, defaults to 2.0) &#x2014;
Expansion factor for the MobileNetv2 layers.`,name:"expand_ratio"},{anchor:"transformers.MobileViTV2Config.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;swish&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the Transformer encoder and convolution layers.`,name:"hidden_act"},{anchor:"transformers.MobileViTV2Config.conv_kernel_size",description:`<strong>conv_kernel_size</strong> (<code>int</code>, <em>optional</em>, defaults to 3) &#x2014;
The size of the convolutional kernel in the MobileViTV2 layer.`,name:"conv_kernel_size"},{anchor:"transformers.MobileViTV2Config.output_stride",description:`<strong>output_stride</strong> (<code>int</code>, <code>optional</code>, defaults to 32) &#x2014;
The ratio of the spatial resolution of the output to the resolution of the input image.`,name:"output_stride"},{anchor:"transformers.MobileViTV2Config.classifier_dropout_prob",description:`<strong>classifier_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for attached classifiers.`,name:"classifier_dropout_prob"},{anchor:"transformers.MobileViTV2Config.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.MobileViTV2Config.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-5) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.MobileViTV2Config.aspp_out_channels",description:`<strong>aspp_out_channels</strong> (<code>int</code>, <code>optional</code>, defaults to 512) &#x2014;
Number of output channels used in the ASPP layer for semantic segmentation.`,name:"aspp_out_channels"},{anchor:"transformers.MobileViTV2Config.atrous_rates",description:`<strong>atrous_rates</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[6, 12, 18]</code>) &#x2014;
Dilation (atrous) factors used in the ASPP layer for semantic segmentation.`,name:"atrous_rates"},{anchor:"transformers.MobileViTV2Config.aspp_dropout_prob",description:`<strong>aspp_dropout_prob</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout ratio for the ASPP layer for semantic segmentation.`,name:"aspp_dropout_prob"},{anchor:"transformers.MobileViTV2Config.semantic_loss_ignore_index",description:`<strong>semantic_loss_ignore_index</strong> (<code>int</code>, <em>optional</em>, defaults to 255) &#x2014;
The index that is ignored by the loss function of the semantic segmentation model.`,name:"semantic_loss_ignore_index"},{anchor:"transformers.MobileViTV2Config.n_attn_blocks",description:`<strong>n_attn_blocks</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[2, 4, 3]</code>) &#x2014;
The number of attention blocks in each MobileViTV2Layer`,name:"n_attn_blocks"},{anchor:"transformers.MobileViTV2Config.base_attn_unit_dims",description:`<strong>base_attn_unit_dims</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[128, 192, 256]</code>) &#x2014;
The base multiplier for dimensions of attention blocks in each MobileViTV2Layer`,name:"base_attn_unit_dims"},{anchor:"transformers.MobileViTV2Config.width_multiplier",description:`<strong>width_multiplier</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
The width multiplier for MobileViTV2.`,name:"width_multiplier"},{anchor:"transformers.MobileViTV2Config.ffn_multiplier",description:`<strong>ffn_multiplier</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
The FFN multiplier for MobileViTV2.`,name:"ffn_multiplier"},{anchor:"transformers.MobileViTV2Config.attn_dropout",description:`<strong>attn_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout in the attention layer.`,name:"attn_dropout"},{anchor:"transformers.MobileViTV2Config.ffn_dropout",description:`<strong>ffn_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout between FFN layers.`,name:"ffn_dropout"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilevitv2/configuration_mobilevitv2.py#L34"}}),L=new no({props:{anchor:"transformers.MobileViTV2Config.example",$$slots:{default:[zs]},$$scope:{ctx:j}}}),je=new ct({}),Ce=new Ae({props:{name:"class transformers.MobileViTV2Model",anchor:"transformers.MobileViTV2Model",parameters:[{name:"config",val:": MobileViTV2Config"},{name:"expand_output",val:": bool = True"}],parametersDescription:[{anchor:"transformers.MobileViTV2Model.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</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 <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py#L665"}}),Ie=new Ae({props:{name:"forward",anchor:"transformers.MobileViTV2Model.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileViTV2Model.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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileViTV2Model.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.MobileViTV2Model.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py#L699",returnDescription:`
<p>A <code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</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/v4.31.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config"
>MobileViTV2Config</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_channels, height, width)</code>) \u2014 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>) \u2014 Last layer hidden-state after a pooling operation on the spatial dimensions.</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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, num_channels, height, width)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
</ul>
`,returnType:`
<p><code>transformers.modeling_outputs.BaseModelOutputWithPoolingAndNoAttention</code> or <code>tuple(torch.FloatTensor)</code></p>
`}}),K=new qa({props:{$$slots:{default:[As]},$$scope:{ctx:j}}}),ee=new no({props:{anchor:"transformers.MobileViTV2Model.forward.example",$$slots:{default:[Rs]},$$scope:{ctx:j}}}),We=new ct({}),Ee=new Ae({props:{name:"class transformers.MobileViTV2ForImageClassification",anchor:"transformers.MobileViTV2ForImageClassification",parameters:[{name:"config",val:": MobileViTV2Config"}],parametersDescription:[{anchor:"transformers.MobileViTV2ForImageClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</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 <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py#L756"}}),Ze=new Ae({props:{name:"forward",anchor:"transformers.MobileViTV2ForImageClassification.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileViTV2ForImageClassification.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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileViTV2ForImageClassification.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.MobileViTV2ForImageClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MobileViTV2ForImageClassification.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/v4.31.0/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py#L774",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> 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/v4.31.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config"
>MobileViTV2Config</a>) and inputs.</p>
<ul>
<li><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) \u2014 Classification (or regression if config.num_labels==1) loss.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) \u2014 Classification (or regression if config.num_labels==1) scores (before SoftMax).</li>
<li><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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each stage) of shape <code>(batch_size, num_channels, height, width)</code>. Hidden-states (also
called feature maps) of the model at the output of each stage.</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.ImageClassifierOutputWithNoAttention"
>transformers.modeling_outputs.ImageClassifierOutputWithNoAttention</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),oe=new qa({props:{$$slots:{default:[Xs]},$$scope:{ctx:j}}}),ae=new no({props:{anchor:"transformers.MobileViTV2ForImageClassification.forward.example",$$slots:{default:[Ys]},$$scope:{ctx:j}}}),Fe=new ct({}),Se=new Ae({props:{name:"class transformers.MobileViTV2ForSemanticSegmentation",anchor:"transformers.MobileViTV2ForSemanticSegmentation",parameters:[{name:"config",val:": MobileViTV2Config"}],parametersDescription:[{anchor:"transformers.MobileViTV2ForSemanticSegmentation.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.31.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config">MobileViTV2Config</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 <a href="/docs/transformers/v4.31.0/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py#L956"}}),Pe=new Ae({props:{name:"forward",anchor:"transformers.MobileViTV2ForSemanticSegmentation.forward",parameters:[{name:"pixel_values",val:": typing.Optional[torch.Tensor] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"}],parametersDescription:[{anchor:"transformers.MobileViTV2ForSemanticSegmentation.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/v4.31.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See
<a href="/docs/transformers/v4.31.0/en/model_doc/perceiver#transformers.PerceiverFeatureExtractor.__call__">MobileViTImageProcessor.<strong>call</strong>()</a> for details.`,name:"pixel_values"},{anchor:"transformers.MobileViTV2ForSemanticSegmentation.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.MobileViTV2ForSemanticSegmentation.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.MobileViTV2ForSemanticSegmentation.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Ground truth semantic segmentation maps for computing the loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels &gt; 1</code>, a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.31.0/src/transformers/models/mobilevitv2/modeling_mobilevitv2.py#L967",returnDescription:`
<p>A <a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> 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/v4.31.0/en/model_doc/mobilevitv2#transformers.MobileViTV2Config"
>MobileViTV2Config</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) \u2014 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, logits_height, logits_width)</code>) \u2014 Classification scores for each pixel.</p>
<Tip warning={true}>
<p>The logits returned do not necessarily have the same size as the <code>pixel_values</code> passed as inputs. This is
to avoid doing two interpolations and lose some quality when a user needs to resize the logits to the
original image size as post-processing. You should always check your logits shape and resize as needed.</p>
</Tip>
</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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, patch_size, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional 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>) \u2014 Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, patch_size, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`
<p><a
  href="/docs/transformers/v4.31.0/en/main_classes/output#transformers.modeling_outputs.SemanticSegmenterOutput"
>transformers.modeling_outputs.SemanticSegmenterOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ne=new qa({props:{$$slots:{default:[Qs]},$$scope:{ctx:j}}}),re=new no({props:{anchor:"transformers.MobileViTV2ForSemanticSegmentation.forward.example",$$slots:{default:[Os]},$$scope:{ctx:j}}}),{c(){c=s("meta"),v=f(),g=s("h1"),m=s("a"),_=s("span"),M(a.$$.fragment),p=f(),C=s("span"),lo=i("MobileViTV2"),Et=f(),S=s("h2"),O=s("a"),dt=s("span"),M(fe.$$.fragment),co=f(),mt=s("span"),mo=i("Overview"),xt=f(),q=s("p"),po=i("The MobileViTV2 model was proposed in "),ue=s("a"),ho=i("Separable Self-attention for Mobile Vision Transformers"),fo=i(" by Sachin Mehta and Mohammad Rastegari."),Ut=f(),Re=s("p"),uo=i("MobileViTV2 is the second version of MobileViT, constructed by replacing the multi-headed self-attention in MobileViT with separable self-attention."),Zt=f(),Xe=s("p"),go=i("The abstract from the paper is the following:"),Ft=f(),Ye=s("p"),pt=s("em"),bo=i("Mobile vision transformers (MobileViT) can achieve state-of-the-art performance across several mobile vision tasks, including classification and detection. Though these models have fewer parameters, they have high latency as compared to convolutional neural network-based models. The main efficiency bottleneck in MobileViT is the multi-headed self-attention (MHA) in transformers, which requires O(k2) time complexity with respect to the number of tokens (or patches) k. Moreover, MHA requires costly operations (e.g., batch-wise matrix multiplication) for computing self-attention, impacting latency on resource-constrained devices. This paper introduces a separable self-attention method with linear complexity, i.e. O(k). A simple yet effective characteristic of the proposed method is that it uses element-wise operations for computing self-attention, making it a good choice for resource-constrained devices. The improved model, MobileViTV2, is state-of-the-art on several mobile vision tasks, including ImageNet object classification and MS-COCO object detection. With about three million parameters, MobileViTV2 achieves a top-1 accuracy of 75.6% on the ImageNet dataset, outperforming MobileViT by about 1% while running 3.2\xD7 faster on a mobile device."),St=f(),Qe=s("p"),_o=i("Tips:"),Nt=f(),W=s("ul"),ht=s("li"),vo=i("MobileViTV2 is more like a CNN than a Transformer model. It does not work on sequence data but on batches of images. Unlike ViT, there are no embeddings. The backbone model outputs a feature map."),Mo=f(),ge=s("li"),To=i("One can use "),Oe=s("a"),Vo=i("MobileViTImageProcessor"),wo=i(" to prepare images for the model. Note that if you do your own preprocessing, the pretrained checkpoints expect images to be in BGR pixel order (not RGB)."),yo=f(),be=s("li"),$o=i("The available image classification checkpoints are pre-trained on "),_e=s("a"),jo=i("ImageNet-1k"),Co=i(" (also referred to as ILSVRC 2012, a collection of 1.3 million images and 1,000 classes)."),ko=f(),N=s("li"),Jo=i("The segmentation model uses a "),ve=s("a"),Io=i("DeepLabV3"),Wo=i(" head. The available semantic segmentation checkpoints are pre-trained on "),Me=s("a"),Eo=i("PASCAL VOC"),xo=i("."),Bt=f(),F=s("p"),Uo=i("This model was contributed by "),Te=s("a"),Zo=i("shehan97"),Fo=i(`.
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