import{S as ds,i as cs,s as ps,e as r,k as h,w as y,t as a,M as ms,c as n,d as o,m as f,a as s,x as w,h as i,b as d,N as hs,G as t,g as m,y as P,q as $,o as N,B as T,v as fs,L as pn}from"../../chunks/vendor-hf-doc-builder.js";import{T as dn}from"../../chunks/Tip-hf-doc-builder.js";import{D as H}from"../../chunks/Docstring-hf-doc-builder.js";import{C as mn}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as _e}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as cn}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";function us(M){let p,v,g,_,b;return{c(){p=r("p"),v=a(`This is a recently introduced model so the API hasn\u2019t been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a `),g=r("a"),_=a("Github Issue"),b=a("."),this.h()},l(l){p=n(l,"P",{});var u=s(p);v=i(u,`This is a recently introduced model so the API hasn\u2019t been tested extensively. There may be some bugs or slight
breaking changes to fix it in the future. If you see something strange, file a `),g=n(u,"A",{href:!0,rel:!0});var G=s(g);_=i(G,"Github Issue"),G.forEach(o),b=i(u,"."),u.forEach(o),this.h()},h(){d(g,"href","https://github.com/huggingface/transformers/issues/new?assignees=&labels=&template=bug-report.md&title"),d(g,"rel","nofollow")},m(l,u){m(l,p,u),t(p,v),t(p,g),t(g,_),t(p,b)},d(l){l&&o(p)}}}function gs(M){let p,v,g,_,b;return _=new mn({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> GLPNModel, GLPNConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a GLPN vinvino02/glpn-kitti style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = GLPNConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the vinvino02/glpn-kitti style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GLPNModel(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(){p=r("p"),v=a("Example:"),g=h(),y(_.$$.fragment)},l(l){p=n(l,"P",{});var u=s(p);v=i(u,"Example:"),u.forEach(o),g=f(l),w(_.$$.fragment,l)},m(l,u){m(l,p,u),t(p,v),m(l,g,u),P(_,l,u),b=!0},p:pn,i(l){b||($(_.$$.fragment,l),b=!0)},o(l){N(_.$$.fragment,l),b=!1},d(l){l&&o(p),l&&o(g),T(_,l)}}}function _s(M){let p,v,g,_,b;return{c(){p=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=r("code"),_=a("Module"),b=a(`
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(l){p=n(l,"P",{});var u=s(p);v=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n(u,"CODE",{});var G=s(g);_=i(G,"Module"),G.forEach(o),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(l,u){m(l,p,u),t(p,v),t(p,g),t(g,_),t(p,b)},d(l){l&&o(p)}}}function bs(M){let p,v,g,_,b;return _=new mn({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, GLPNModel
<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;vinvino02/glpn-kitti&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GLPNModel.from_pretrained(<span class="hljs-string">&quot;vinvino02/glpn-kitti&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">15</span>, <span class="hljs-number">20</span>]`}}),{c(){p=r("p"),v=a("Example:"),g=h(),y(_.$$.fragment)},l(l){p=n(l,"P",{});var u=s(p);v=i(u,"Example:"),u.forEach(o),g=f(l),w(_.$$.fragment,l)},m(l,u){m(l,p,u),t(p,v),m(l,g,u),P(_,l,u),b=!0},p:pn,i(l){b||($(_.$$.fragment,l),b=!0)},o(l){N(_.$$.fragment,l),b=!1},d(l){l&&o(p),l&&o(g),T(_,l)}}}function vs(M){let p,v,g,_,b;return{c(){p=r("p"),v=a("Although the recipe for forward pass needs to be defined within this function, one should call the "),g=r("code"),_=a("Module"),b=a(`
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(l){p=n(l,"P",{});var u=s(p);v=i(u,"Although the recipe for forward pass needs to be defined within this function, one should call the "),g=n(u,"CODE",{});var G=s(g);_=i(G,"Module"),G.forEach(o),b=i(u,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),u.forEach(o)},m(l,u){m(l,p,u),t(p,v),t(p,g),t(g,_),t(p,b)},d(l){l&&o(p)}}}function ys(M){let p,v,g,_,b;return _=new mn({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, GLPNForDepthEstimation
<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">import</span> numpy <span class="hljs-keyword">as</span> np
<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;vinvino02/glpn-kitti&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = GLPNForDepthEstimation.from_pretrained(<span class="hljs-string">&quot;vinvino02/glpn-kitti&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># prepare image for the model</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">... </span>    predicted_depth = outputs.predicted_depth

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># interpolate to original size</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>prediction = torch.nn.functional.interpolate(
<span class="hljs-meta">... </span>    predicted_depth.unsqueeze(<span class="hljs-number">1</span>),
<span class="hljs-meta">... </span>    size=image.size[::-<span class="hljs-number">1</span>],
<span class="hljs-meta">... </span>    mode=<span class="hljs-string">&quot;bicubic&quot;</span>,
<span class="hljs-meta">... </span>    align_corners=<span class="hljs-literal">False</span>,
<span class="hljs-meta">... </span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># visualize the prediction</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>output = prediction.squeeze().cpu().numpy()
<span class="hljs-meta">&gt;&gt;&gt; </span>formatted = (output * <span class="hljs-number">255</span> / np.<span class="hljs-built_in">max</span>(output)).astype(<span class="hljs-string">&quot;uint8&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>depth = Image.fromarray(formatted)`}}),{c(){p=r("p"),v=a("Examples:"),g=h(),y(_.$$.fragment)},l(l){p=n(l,"P",{});var u=s(p);v=i(u,"Examples:"),u.forEach(o),g=f(l),w(_.$$.fragment,l)},m(l,u){m(l,p,u),t(p,v),m(l,g,u),P(_,l,u),b=!0},p:pn,i(l){b||($(_.$$.fragment,l),b=!0)},o(l){N(_.$$.fragment,l),b=!1},d(l){l&&o(p),l&&o(g),T(_,l)}}}function ws(M){let p,v,g,_,b,l,u,G,Go,Bt,Q,At,J,Y,_t,be,Eo,bt,Lo,Dt,U,Io,ve,jo,xo,Ve,Co,Uo,St,qe,ko,Xt,He,vt,Jo,Vt,Qe,Fo,qt,Ye,ye,zo,Oe,Ro,Zo,Ht,O,hn,Qt,K,Wo,we,Bo,Ao,Yt,k,Do,Pe,So,Xo,$e,Vo,qo,Ot,F,ee,yt,Ne,Ho,wt,Qo,Kt,Ke,Yo,eo,te,z,Oo,et,Ko,er,Te,tr,or,rr,Pt,tt,nr,to,R,oe,$t,Me,sr,Nt,ar,oo,E,Ge,ir,Z,lr,ot,dr,cr,Ee,pr,mr,hr,W,fr,rt,ur,gr,nt,_r,br,vr,re,ro,B,ne,Tt,Le,yr,Mt,wr,no,A,Ie,Pr,se,je,$r,Gt,Nr,so,D,ae,Et,xe,Tr,Lt,Mr,ao,j,Ce,Gr,It,Er,Lr,ie,Ue,Ir,jt,jr,io,S,le,xt,ke,xr,Ct,Cr,lo,x,Je,Ur,Fe,kr,ze,Jr,Fr,zr,L,Re,Rr,X,Zr,st,Wr,Br,Ut,Ar,Dr,Sr,de,Xr,ce,co,V,pe,kt,Ze,Vr,Jt,qr,po,C,We,Hr,Be,Qr,Ae,Yr,Or,Kr,I,De,en,q,tn,at,on,rn,Ft,nn,sn,an,me,ln,he,mo;return l=new _e({}),Q=new dn({props:{$$slots:{default:[us]},$$scope:{ctx:M}}}),be=new _e({}),Ne=new _e({}),Me=new _e({}),Ge=new H({props:{name:"class transformers.GLPNConfig",anchor:"transformers.GLPNConfig",parameters:[{name:"num_channels",val:" = 3"},{name:"num_encoder_blocks",val:" = 4"},{name:"depths",val:" = [2, 2, 2, 2]"},{name:"sr_ratios",val:" = [8, 4, 2, 1]"},{name:"hidden_sizes",val:" = [32, 64, 160, 256]"},{name:"patch_sizes",val:" = [7, 3, 3, 3]"},{name:"strides",val:" = [4, 2, 2, 2]"},{name:"num_attention_heads",val:" = [1, 2, 5, 8]"},{name:"mlp_ratios",val:" = [4, 4, 4, 4]"},{name:"hidden_act",val:" = 'gelu'"},{name:"hidden_dropout_prob",val:" = 0.0"},{name:"attention_probs_dropout_prob",val:" = 0.0"},{name:"initializer_range",val:" = 0.02"},{name:"drop_path_rate",val:" = 0.1"},{name:"layer_norm_eps",val:" = 1e-06"},{name:"decoder_hidden_size",val:" = 64"},{name:"max_depth",val:" = 10"},{name:"head_in_index",val:" = -1"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.GLPNConfig.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.GLPNConfig.num_encoder_blocks",description:`<strong>num_encoder_blocks</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of encoder blocks (i.e. stages in the Mix Transformer encoder).`,name:"num_encoder_blocks"},{anchor:"transformers.GLPNConfig.depths",description:`<strong>depths</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[2, 2, 2, 2]</code>) &#x2014;
The number of layers in each encoder block.`,name:"depths"},{anchor:"transformers.GLPNConfig.sr_ratios",description:`<strong>sr_ratios</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[8, 4, 2, 1]</code>) &#x2014;
Sequence reduction ratios in each encoder block.`,name:"sr_ratios"},{anchor:"transformers.GLPNConfig.hidden_sizes",description:`<strong>hidden_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[32, 64, 160, 256]</code>) &#x2014;
Dimension of each of the encoder blocks.`,name:"hidden_sizes"},{anchor:"transformers.GLPNConfig.patch_sizes",description:`<strong>patch_sizes</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[7, 3, 3, 3]</code>) &#x2014;
Patch size before each encoder block.`,name:"patch_sizes"},{anchor:"transformers.GLPNConfig.strides",description:`<strong>strides</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[4, 2, 2, 2]</code>) &#x2014;
Stride before each encoder block.`,name:"strides"},{anchor:"transformers.GLPNConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[1, 2, 4, 8]</code>) &#x2014;
Number of attention heads for each attention layer in each block of the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.GLPNConfig.mlp_ratios",description:`<strong>mlp_ratios</strong> (<code>List[int]</code>, <em>optional</em>, defaults to <code>[4, 4, 4, 4]</code>) &#x2014;
Ratio of the size of the hidden layer compared to the size of the input layer of the Mix FFNs in the
encoder blocks.`,name:"mlp_ratios"},{anchor:"transformers.GLPNConfig.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 and pooler. 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.GLPNConfig.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, encoder, and pooler.`,name:"hidden_dropout_prob"},{anchor:"transformers.GLPNConfig.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.GLPNConfig.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.GLPNConfig.drop_path_rate",description:`<strong>drop_path_rate</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
The dropout probability for stochastic depth, used in the blocks of the Transformer encoder.`,name:"drop_path_rate"},{anchor:"transformers.GLPNConfig.layer_norm_eps",description:`<strong>layer_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-6) &#x2014;
The epsilon used by the layer normalization layers.`,name:"layer_norm_eps"},{anchor:"transformers.GLPNConfig.decoder_hidden_size",description:`<strong>decoder_hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
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<code>PIL.Image</code> resampling filter to use if resizing the image e.g. <code>PILImageResampling.BILINEAR</code>. Only has
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The channel dimension format for the output image. Can be one of:<ul>
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<li><code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
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The channel dimension format for the input image. If unset, the channel dimension format is inferred
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<li><code>&quot;channels_first&quot;</code> or <code>ChannelDimension.FIRST</code>: image in (num_channels, height, width) format.</li>
<li><code>&quot;channels_last&quot;</code> or <code>ChannelDimension.LAST</code>: image in (height, width, num_channels) format.</li>
<li><code>&quot;none&quot;</code> or <code>ChannelDimension.NONE</code>: image in (height, width) format.</li>
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<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the model.</p>
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<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, +
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<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, sequence_length, sequence_length)</code>.</p>
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</li>
</ul>
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<p><a
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<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>
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<li>
<p><strong>predicted_depth</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, height, width)</code>) \u2014 Predicted depth for each pixel.</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, +
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<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>
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<p><a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.DepthEstimatorOutput"
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