import{S as Ls,i as Rs,s as Ns,e as r,k as d,w as b,t as s,M as Ms,c as n,d as t,m as c,a,x as D,h as i,b as l,N as Ss,G as e,g as p,y as v,q as y,o as w,B as T,v as Us,L as ja}from"../../chunks/vendor-hf-doc-builder.js";import{T as As}from"../../chunks/Tip-hf-doc-builder.js";import{D as z}from"../../chunks/Docstring-hf-doc-builder.js";import{C as Pa}from"../../chunks/CodeBlock-hf-doc-builder.js";import{I as Ee}from"../../chunks/IconCopyLink-hf-doc-builder.js";import{E as ka}from"../../chunks/ExampleCodeBlock-hf-doc-builder.js";import{P as Ws}from"../../chunks/PipelineTag-hf-doc-builder.js";function Hs(F){let h,$,_,u,x;return u=new Pa({props:{code:`from transformers import DeformableDetrConfig, DeformableDetrModel

# Initializing a Deformable DETR SenseTime/deformable-detr style configuration
configuration = DeformableDetrConfig()

# Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration
model = DeformableDetrModel(configuration)

# Accessing the model configuration
configuration = model.config`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> DeformableDetrConfig, DeformableDetrModel

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Deformable DETR SenseTime/deformable-detr style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = DeformableDetrConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model (with random weights) from the SenseTime/deformable-detr style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeformableDetrModel(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(){h=r("p"),$=s("Examples:"),_=d(),b(u.$$.fragment)},l(m){h=n(m,"P",{});var g=a(h);$=i(g,"Examples:"),g.forEach(t),_=c(m),D(u.$$.fragment,m)},m(m,g){p(m,h,g),e(h,$),p(m,_,g),v(u,m,g),x=!0},p:ja,i(m){x||(y(u.$$.fragment,m),x=!0)},o(m){w(u.$$.fragment,m),x=!1},d(m){m&&t(h),m&&t(_),T(u,m)}}}function Vs(F){let h,$,_,u,x;return{c(){h=r("p"),$=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),u=s("Module"),x=s(`
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(m){h=n(m,"P",{});var g=a(h);$=i(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(g,"CODE",{});var O=a(_);u=i(O,"Module"),O.forEach(t),x=i(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(m,g){p(m,h,g),e(h,$),e(h,_),e(_,u),e(h,x)},d(m){m&&t(h)}}}function Bs(F){let h,$,_,u,x;return u=new Pa({props:{code:`from transformers import AutoImageProcessor, DeformableDetrModel
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr")
model = DeformableDetrModel.from_pretrained("SenseTime/deformable-detr")

inputs = image_processor(images=image, return_tensors="pt")

outputs = model(**inputs)

last_hidden_states = outputs.last_hidden_state
list(last_hidden_states.shape)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, DeformableDetrModel
<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;SenseTime/deformable-detr&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeformableDetrModel.from_pretrained(<span class="hljs-string">&quot;SenseTime/deformable-detr&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>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">300</span>, <span class="hljs-number">256</span>]`}}),{c(){h=r("p"),$=s("Examples:"),_=d(),b(u.$$.fragment)},l(m){h=n(m,"P",{});var g=a(h);$=i(g,"Examples:"),g.forEach(t),_=c(m),D(u.$$.fragment,m)},m(m,g){p(m,h,g),e(h,$),p(m,_,g),v(u,m,g),x=!0},p:ja,i(m){x||(y(u.$$.fragment,m),x=!0)},o(m){w(u.$$.fragment,m),x=!1},d(m){m&&t(h),m&&t(_),T(u,m)}}}function Xs(F){let h,$,_,u,x;return{c(){h=r("p"),$=s("Although the recipe for forward pass needs to be defined within this function, one should call the "),_=r("code"),u=s("Module"),x=s(`
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(m){h=n(m,"P",{});var g=a(h);$=i(g,"Although the recipe for forward pass needs to be defined within this function, one should call the "),_=n(g,"CODE",{});var O=a(_);u=i(O,"Module"),O.forEach(t),x=i(g,`
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`),g.forEach(t)},m(m,g){p(m,h,g),e(h,$),e(h,_),e(_,u),e(h,x)},d(m){m&&t(h)}}}function Js(F){let h,$,_,u,x;return u=new Pa({props:{code:`from transformers import AutoImageProcessor, DeformableDetrForObjectDetection
from PIL import Image
import requests

url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)

image_processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr")
model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr")

inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)

# convert outputs (bounding boxes and class logits) to COCO API
target_sizes = torch.tensor([image.size[::-1]])
results = image_processor.post_process_object_detection(outputs, threshold=0.5, target_sizes=target_sizes)[
    0
]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
    box = [round(i, 2) for i in box.tolist()]
    print(
        f"Detected {model.config.id2label[label.item()]} with confidence "
        f"{round(score.item(), 3)} at location {box}"
    )`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoImageProcessor, DeformableDetrForObjectDetection
<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;SenseTime/deformable-detr&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = DeformableDetrForObjectDetection.from_pretrained(<span class="hljs-string">&quot;SenseTime/deformable-detr&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>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># convert outputs (bounding boxes and class logits) to COCO API</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>target_sizes = torch.tensor([image.size[::-<span class="hljs-number">1</span>]])
<span class="hljs-meta">&gt;&gt;&gt; </span>results = image_processor.post_process_object_detection(outputs, threshold=<span class="hljs-number">0.5</span>, target_sizes=target_sizes)[
<span class="hljs-meta">... </span>    <span class="hljs-number">0</span>
<span class="hljs-meta">... </span>]
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">for</span> score, label, box <span class="hljs-keyword">in</span> <span class="hljs-built_in">zip</span>(results[<span class="hljs-string">&quot;scores&quot;</span>], results[<span class="hljs-string">&quot;labels&quot;</span>], results[<span class="hljs-string">&quot;boxes&quot;</span>]):
<span class="hljs-meta">... </span>    box = [<span class="hljs-built_in">round</span>(i, <span class="hljs-number">2</span>) <span class="hljs-keyword">for</span> i <span class="hljs-keyword">in</span> box.tolist()]
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(
<span class="hljs-meta">... </span>        <span class="hljs-string">f&quot;Detected <span class="hljs-subst">{model.config.id2label[label.item()]}</span> with confidence &quot;</span>
<span class="hljs-meta">... </span>        <span class="hljs-string">f&quot;<span class="hljs-subst">{<span class="hljs-built_in">round</span>(score.item(), <span class="hljs-number">3</span>)}</span> at location <span class="hljs-subst">{box}</span>&quot;</span>
<span class="hljs-meta">... </span>    )
Detected cat <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.8</span> at location [<span class="hljs-number">16.5</span>, <span class="hljs-number">52.84</span>, <span class="hljs-number">318.25</span>, <span class="hljs-number">470.78</span>]
Detected cat <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.789</span> at location [<span class="hljs-number">342.19</span>, <span class="hljs-number">24.3</span>, <span class="hljs-number">640.02</span>, <span class="hljs-number">372.25</span>]
Detected remote <span class="hljs-keyword">with</span> confidence <span class="hljs-number">0.633</span> at location [<span class="hljs-number">40.79</span>, <span class="hljs-number">72.78</span>, <span class="hljs-number">176.76</span>, <span class="hljs-number">117.25</span>]`}}),{c(){h=r("p"),$=s("Examples:"),_=d(),b(u.$$.fragment)},l(m){h=n(m,"P",{});var g=a(h);$=i(g,"Examples:"),g.forEach(t),_=c(m),D(u.$$.fragment,m)},m(m,g){p(m,h,g),e(h,$),p(m,_,g),v(u,m,g),x=!0},p:ja,i(m){x||(y(u.$$.fragment,m),x=!0)},o(m){w(u.$$.fragment,m),x=!1},d(m){m&&t(h),m&&t(_),T(u,m)}}}function Ys(F){let h,$,_,u,x,m,g,O,lr,ht,S,ee,So,ke,dr,Uo,cr,ut,A,mr,je,fr,pr,fo,hr,ur,gt,po,gr,_t,ho,Wo,_r,bt,uo,br,Dt,oe,Pe,Dr,go,vr,yr,wr,U,Tr,_o,xr,$r,bo,Er,kr,vt,te,Ia,yt,re,jr,Do,Pr,Ir,wt,L,zr,Ie,Fr,Cr,ze,qr,Or,Tt,W,ne,Ho,Fe,Ar,Vo,Lr,xt,vo,Rr,$t,Ce,Et,ae,H,Nr,yo,Mr,Sr,qe,Ur,Wr,Hr,Oe,Vr,wo,Br,Xr,kt,To,Jr,jt,V,se,Bo,Ae,Yr,Xo,Gr,Pt,E,Le,Zr,Jo,Kr,Qr,ie,Re,en,Yo,on,tn,le,Ne,rn,Go,nn,an,de,Me,sn,Se,ln,xo,dn,cn,It,B,ce,Zo,Ue,mn,Ko,fn,zt,P,We,pn,me,He,hn,Qo,un,gn,fe,Ve,_n,et,bn,Dn,pe,Be,vn,Xe,yn,$o,wn,Tn,Ft,X,he,ot,Je,xn,tt,$n,Ct,I,Ye,En,J,kn,Eo,jn,Pn,Ge,In,zn,Fn,Y,Cn,ko,qn,On,jo,An,Ln,Rn,ue,qt,G,ge,rt,Ze,Nn,nt,Mn,Ot,k,Ke,Sn,at,Un,Wn,Qe,Hn,Po,Vn,Bn,Xn,eo,Jn,oo,Yn,Gn,Zn,C,to,Kn,Z,Qn,Io,ea,oa,st,ta,ra,na,_e,aa,be,At,K,De,it,ro,sa,lt,ia,Lt,j,no,la,dt,da,ca,ao,ma,zo,fa,pa,ha,so,ua,io,ga,_a,ba,q,lo,Da,Q,va,Fo,ya,wa,ct,Ta,xa,$a,ve,Ea,ye,Rt;return m=new Ee({}),ke=new Ee({}),Fe=new Ee({}),Ce=new Ws({props:{pipeline:"object-detection"}}),Ae=new Ee({}),Le=new z({props:{name:"class transformers.DeformableDetrImageProcessor",anchor:"transformers.DeformableDetrImageProcessor",parameters:[{name:"format",val:": typing.Union[str, transformers.models.deformable_detr.image_processing_deformable_detr.AnnotionFormat] = <AnnotionFormat.COCO_DETECTION: 'coco_detection'>"},{name:"do_resize",val:": bool = True"},{name:"size",val:": typing.Dict[str, int] = None"},{name:"resample",val:": Resampling = <Resampling.BILINEAR: 2>"},{name:"do_rescale",val:": bool = True"},{name:"rescale_factor",val:": typing.Union[int, float] = 0.00392156862745098"},{name:"do_normalize",val:": bool = True"},{name:"image_mean",val:": typing.Union[float, typing.List[float]] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float]] = None"},{name:"do_pad",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DeformableDetrImageProcessor.format",description:`<strong>format</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;coco_detection&quot;</code>) &#x2014;
Data format of the annotations. One of &#x201C;coco_detection&#x201D; or &#x201C;coco_panoptic&#x201D;.`,name:"format"},{anchor:"transformers.DeformableDetrImageProcessor.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Controls whether to resize the image&#x2019;s (height, width) dimensions to the specified <code>size</code>. Can be
overridden by the <code>do_resize</code> parameter in the <code>preprocess</code> method.`,name:"do_resize"},{anchor:"transformers.DeformableDetrImageProcessor.size",description:`<strong>size</strong> (<code>Dict[str, int]</code> <em>optional</em>, defaults to <code>{&quot;shortest_edge&quot; -- 800, &quot;longest_edge&quot;: 1333}</code>):
Size of the image&#x2019;s (height, width) dimensions after resizing. Can be overridden by the <code>size</code> parameter in
the <code>preprocess</code> method.`,name:"size"},{anchor:"transformers.DeformableDetrImageProcessor.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to <code>PILImageResampling.BILINEAR</code>) &#x2014;
Resampling filter to use if resizing the image.`,name:"resample"},{anchor:"transformers.DeformableDetrImageProcessor.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Controls whether to rescale the image by the specified scale <code>rescale_factor</code>. Can be overridden by the
<code>do_rescale</code> parameter in the <code>preprocess</code> method.`,name:"do_rescale"},{anchor:"transformers.DeformableDetrImageProcessor.rescale_factor",description:`<strong>rescale_factor</strong> (<code>int</code> or <code>float</code>, <em>optional</em>, defaults to <code>1/255</code>) &#x2014;
Scale factor to use if rescaling the image. Can be overridden by the <code>rescale_factor</code> parameter in the
<code>preprocess</code> method.
do_normalize &#x2014;
Controls whether to normalize the image. Can be overridden by the <code>do_normalize</code> parameter in the
<code>preprocess</code> method.`,name:"rescale_factor"},{anchor:"transformers.DeformableDetrImageProcessor.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_DEFAULT_MEAN</code>) &#x2014;
Mean values to use when normalizing the image. Can be a single value or a list of values, one for each
channel. Can be overridden by the <code>image_mean</code> parameter in the <code>preprocess</code> method.`,name:"image_mean"},{anchor:"transformers.DeformableDetrImageProcessor.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to <code>IMAGENET_DEFAULT_STD</code>) &#x2014;
Standard deviation values to use when normalizing the image. Can be a single value or a list of values, one
for each channel. Can be overridden by the <code>image_std</code> parameter in the <code>preprocess</code> method.`,name:"image_std"},{anchor:"transformers.DeformableDetrImageProcessor.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Controls whether to pad the image to the largest image in a batch and create a pixel mask. Can be
overridden by the <code>do_pad</code> parameter in the <code>preprocess</code> method.`,name:"do_pad"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deformable_detr/image_processing_deformable_detr.py#L742"}}),Re=new z({props:{name:"preprocess",anchor:"transformers.DeformableDetrImageProcessor.preprocess",parameters:[{name:"images",val:": typing.Union[ForwardRef('PIL.Image.Image'), numpy.ndarray, ForwardRef('torch.Tensor'), typing.List[ForwardRef('PIL.Image.Image')], typing.List[numpy.ndarray], typing.List[ForwardRef('torch.Tensor')]]"},{name:"annotations",val:": typing.Union[typing.Dict[str, typing.Union[int, str, typing.List[typing.Dict]]], typing.List[typing.Dict[str, typing.Union[int, str, typing.List[typing.Dict]]]], NoneType] = None"},{name:"return_segmentation_masks",val:": bool = None"},{name:"masks_path",val:": typing.Union[str, pathlib.Path, NoneType] = None"},{name:"do_resize",val:": typing.Optional[bool] = None"},{name:"size",val:": typing.Union[typing.Dict[str, int], NoneType] = None"},{name:"resample",val:" = None"},{name:"do_rescale",val:": typing.Optional[bool] = None"},{name:"rescale_factor",val:": typing.Union[int, float, NoneType] = None"},{name:"do_normalize",val:": typing.Optional[bool] = None"},{name:"image_mean",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"image_std",val:": typing.Union[float, typing.List[float], NoneType] = None"},{name:"do_pad",val:": typing.Optional[bool] = None"},{name:"format",val:": typing.Union[str, transformers.models.deformable_detr.image_processing_deformable_detr.AnnotionFormat, NoneType] = None"},{name:"return_tensors",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"},{name:"data_format",val:": typing.Union[str, transformers.image_utils.ChannelDimension] = <ChannelDimension.FIRST: 'channels_first'>"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.DeformableDetrImageProcessor.preprocess.images",description:`<strong>images</strong> (<code>ImageInput</code>) &#x2014;
Image or batch of images to preprocess.`,name:"images"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.annotations",description:`<strong>annotations</strong> (<code>AnnotationType</code> or <code>List[AnnotationType]</code>, <em>optional</em>) &#x2014;
List of annotations associated with the image or batch of images. If annotation is for object
detection, the annotations should be a dictionary with the following keys:<ul>
<li>&#x201C;image_id&#x201D; (<code>int</code>): The image id.</li>
<li>&#x201C;annotations&#x201D; (<code>List[Dict]</code>): List of annotations for an image. Each annotation should be a
dictionary. An image can have no annotations, in which case the list should be empty.
If annotation is for segmentation, the annotations should be a dictionary with the following keys:</li>
<li>&#x201C;image_id&#x201D; (<code>int</code>): The image id.</li>
<li>&#x201C;segments_info&#x201D; (<code>List[Dict]</code>): List of segments for an image. Each segment should be a dictionary.
An image can have no segments, in which case the list should be empty.</li>
<li>&#x201C;file_name&#x201D; (<code>str</code>): The file name of the image.</li>
</ul>`,name:"annotations"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.return_segmentation_masks",description:`<strong>return_segmentation_masks</strong> (<code>bool</code>, <em>optional</em>, defaults to self.return_segmentation_masks) &#x2014;
Whether to return segmentation masks.`,name:"return_segmentation_masks"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.masks_path",description:`<strong>masks_path</strong> (<code>str</code> or <code>pathlib.Path</code>, <em>optional</em>) &#x2014;
Path to the directory containing the segmentation masks.`,name:"masks_path"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.do_resize",description:`<strong>do_resize</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_resize) &#x2014;
Whether to resize the image.`,name:"do_resize"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>, <em>optional</em>, defaults to self.size) &#x2014;
Size of the image after resizing.`,name:"size"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.resample",description:`<strong>resample</strong> (<code>PILImageResampling</code>, <em>optional</em>, defaults to self.resample) &#x2014;
Resampling filter to use when resizing the image.`,name:"resample"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.do_rescale",description:`<strong>do_rescale</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_rescale) &#x2014;
Whether to rescale the image.`,name:"do_rescale"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.rescale_factor",description:`<strong>rescale_factor</strong> (<code>float</code>, <em>optional</em>, defaults to self.rescale_factor) &#x2014;
Rescale factor to use when rescaling the image.`,name:"rescale_factor"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.do_normalize",description:`<strong>do_normalize</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_normalize) &#x2014;
Whether to normalize the image.`,name:"do_normalize"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.image_mean",description:`<strong>image_mean</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to self.image_mean) &#x2014;
Mean to use when normalizing the image.`,name:"image_mean"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.image_std",description:`<strong>image_std</strong> (<code>float</code> or <code>List[float]</code>, <em>optional</em>, defaults to self.image_std) &#x2014;
Standard deviation to use when normalizing the image.`,name:"image_std"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.do_pad",description:`<strong>do_pad</strong> (<code>bool</code>, <em>optional</em>, defaults to self.do_pad) &#x2014;
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Format of the annotations.`,name:"format"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.return_tensors",description:`<strong>return_tensors</strong> (<code>str</code> or <code>TensorType</code>, <em>optional</em>, defaults to self.return_tensors) &#x2014;
Type of tensors to return. If <code>None</code>, will return the list of images.`,name:"return_tensors"},{anchor:"transformers.DeformableDetrImageProcessor.preprocess.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>, defaults to self.data_format) &#x2014;
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<li><code>TensorType.PYTORCH</code> or <code>&apos;pt&apos;</code>: Return a batch of type <code>torch.Tensor</code>.</li>
<li><code>TensorType.NUMPY</code> or <code>&apos;np&apos;</code>: Return a batch of type <code>np.ndarray</code>.</li>
<li><code>TensorType.JAX</code> or <code>&apos;jax&apos;</code>: Return a batch of type <code>jax.numpy.ndarray</code>.</li>
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Score threshold to keep object detection predictions.`,name:"threshold"},{anchor:"transformers.DeformableDetrImageProcessor.post_process_object_detection.target_sizes",description:`<strong>target_sizes</strong> (<code>torch.Tensor</code> or <code>List[Tuple[int, int]]</code>, <em>optional</em>) &#x2014;
Tensor of shape <code>(batch_size, 2)</code> or list of tuples (<code>Tuple[int, int]</code>) containing the target size
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<li><code>TensorType.TENSORFLOW</code> or <code>&apos;tf&apos;</code>: Return a batch of type <code>tf.Tensor</code>.</li>
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Tensor of shape <code>(batch_size, 2)</code> or list of tuples (<code>Tuple[int, int]</code>) containing the target size
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`,returnType:`
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The configuration of the backbone model. Only used in case <code>use_timm_backbone</code> is set to <code>False</code> in which
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Number of object queries, i.e. detection slots. This is the maximal number of objects
<a href="/docs/transformers/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrModel">DeformableDetrModel</a> can detect in a single image. In case <code>two_stage</code> is set to <code>True</code>, we use
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Number of encoder layers.`,name:"encoder_layers"},{anchor:"transformers.DeformableDetrConfig.decoder_layers",description:`<strong>decoder_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 6) &#x2014;
Number of decoder layers.`,name:"decoder_layers"},{anchor:"transformers.DeformableDetrConfig.encoder_attention_heads",description:`<strong>encoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
Number of attention heads for each attention layer in the Transformer encoder.`,name:"encoder_attention_heads"},{anchor:"transformers.DeformableDetrConfig.decoder_attention_heads",description:`<strong>decoder_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 8) &#x2014;
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Dimension of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"decoder_ffn_dim"},{anchor:"transformers.DeformableDetrConfig.encoder_ffn_dim",description:`<strong>encoder_ffn_dim</strong> (<code>int</code>, <em>optional</em>, defaults to 1024) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (often named feed-forward) layer in decoder.`,name:"encoder_ffn_dim"},{anchor:"transformers.DeformableDetrConfig.activation_function",description:`<strong>activation_function</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;relu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the encoder and pooler. If string, <code>&quot;gelu&quot;</code>,
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"dropout"},{anchor:"transformers.DeformableDetrConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.DeformableDetrConfig.activation_dropout",description:`<strong>activation_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for activations inside the fully connected layer.`,name:"activation_dropout"},{anchor:"transformers.DeformableDetrConfig.init_std",description:`<strong>init_std</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:"init_std"},{anchor:"transformers.DeformableDetrConfig.init_xavier_std",description:`<strong>init_xavier_std</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
The scaling factor used for the Xavier initialization gain in the HM Attention map module.
encoder_layerdrop &#x2014; (<code>float</code>, <em>optional</em>, defaults to 0.0):
The LayerDrop probability for the encoder. See the [LayerDrop paper](see <a href="https://arxiv.org/abs/1909.11556" rel="nofollow">https://arxiv.org/abs/1909.11556</a>)
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Whether auxiliary decoding losses (loss at each decoder layer) are to be used.`,name:"auxiliary_loss"},{anchor:"transformers.DeformableDetrConfig.position_embedding_type",description:`<strong>position_embedding_type</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;sine&quot;</code>) &#x2014;
Type of position embeddings to be used on top of the image features. One of <code>&quot;sine&quot;</code> or <code>&quot;learned&quot;</code>.`,name:"position_embedding_type"},{anchor:"transformers.DeformableDetrConfig.backbone",description:`<strong>backbone</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;resnet50&quot;</code>) &#x2014;
Name of convolutional backbone to use in case <code>use_timm_backbone</code> = <code>True</code>. Supports any convolutional
backbone from the timm package. For a list of all available models, see <a href="https://rwightman.github.io/pytorch-image-models/#load-a-pretrained-model" rel="nofollow">this
page</a>.`,name:"backbone"},{anchor:"transformers.DeformableDetrConfig.use_pretrained_backbone",description:`<strong>use_pretrained_backbone</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use pretrained weights for the backbone. Only supported when <code>use_timm_backbone</code> = <code>True</code>.`,name:"use_pretrained_backbone"},{anchor:"transformers.DeformableDetrConfig.dilation",description:`<strong>dilation</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to replace stride with dilation in the last convolutional block (DC5). Only supported when
<code>use_timm_backbone</code> = <code>True</code>.`,name:"dilation"},{anchor:"transformers.DeformableDetrConfig.class_cost",description:`<strong>class_cost</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
Relative weight of the classification error in the Hungarian matching cost.`,name:"class_cost"},{anchor:"transformers.DeformableDetrConfig.bbox_cost",description:`<strong>bbox_cost</strong> (<code>float</code>, <em>optional</em>, defaults to 5) &#x2014;
Relative weight of the L1 error of the bounding box coordinates in the Hungarian matching cost.`,name:"bbox_cost"},{anchor:"transformers.DeformableDetrConfig.giou_cost",description:`<strong>giou_cost</strong> (<code>float</code>, <em>optional</em>, defaults to 2) &#x2014;
Relative weight of the generalized IoU loss of the bounding box in the Hungarian matching cost.`,name:"giou_cost"},{anchor:"transformers.DeformableDetrConfig.mask_loss_coefficient",description:`<strong>mask_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
Relative weight of the Focal loss in the panoptic segmentation loss.`,name:"mask_loss_coefficient"},{anchor:"transformers.DeformableDetrConfig.dice_loss_coefficient",description:`<strong>dice_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 1) &#x2014;
Relative weight of the DICE/F-1 loss in the panoptic segmentation loss.`,name:"dice_loss_coefficient"},{anchor:"transformers.DeformableDetrConfig.bbox_loss_coefficient",description:`<strong>bbox_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 5) &#x2014;
Relative weight of the L1 bounding box loss in the object detection loss.`,name:"bbox_loss_coefficient"},{anchor:"transformers.DeformableDetrConfig.giou_loss_coefficient",description:`<strong>giou_loss_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 2) &#x2014;
Relative weight of the generalized IoU loss in the object detection loss.`,name:"giou_loss_coefficient"},{anchor:"transformers.DeformableDetrConfig.eos_coefficient",description:`<strong>eos_coefficient</strong> (<code>float</code>, <em>optional</em>, defaults to 0.1) &#x2014;
Relative classification weight of the &#x2018;no-object&#x2019; class in the object detection loss.`,name:"eos_coefficient"},{anchor:"transformers.DeformableDetrConfig.num_feature_levels",description:`<strong>num_feature_levels</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of input feature levels.`,name:"num_feature_levels"},{anchor:"transformers.DeformableDetrConfig.encoder_n_points",description:`<strong>encoder_n_points</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of sampled keys in each feature level for each attention head in the encoder.`,name:"encoder_n_points"},{anchor:"transformers.DeformableDetrConfig.decoder_n_points",description:`<strong>decoder_n_points</strong> (<code>int</code>, <em>optional</em>, defaults to 4) &#x2014;
The number of sampled keys in each feature level for each attention head in the decoder.`,name:"decoder_n_points"},{anchor:"transformers.DeformableDetrConfig.two_stage",description:`<strong>two_stage</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to apply a two-stage deformable DETR, where the region proposals are also generated by a variant of
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The number of region proposals to be generated, in case <code>two_stage</code> is set to <code>True</code>.`,name:"two_stage_num_proposals"},{anchor:"transformers.DeformableDetrConfig.with_box_refine",description:`<strong>with_box_refine</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to apply iterative bounding box refinement, where each decoder layer refines the bounding boxes
based on the predictions from the previous layer.`,name:"with_box_refine"},{anchor:"transformers.DeformableDetrConfig.focal_alpha",description:`<strong>focal_alpha</strong> (<code>float</code>, <em>optional</em>, defaults to 0.25) &#x2014;
Alpha parameter in the focal loss.`,name:"focal_alpha"},{anchor:"transformers.DeformableDetrConfig.disable_custom_kernels",description:`<strong>disable_custom_kernels</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Disable the use of custom CUDA and CPU kernels. This option is necessary for the ONNX export, as custom
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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/v4.29.0/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/v4.29.0/src/transformers/models/deformable_detr/modeling_deformable_detr.py#L1467"}}),to=new z({props:{name:"forward",anchor:"transformers.DeformableDetrModel.forward",parameters:[{name:"pixel_values",val:""},{name:"pixel_mask",val:" = None"},{name:"decoder_attention_mask",val:" = None"},{name:"encoder_outputs",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"decoder_inputs_embeds",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],parametersDescription:[{anchor:"transformers.DeformableDetrModel.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. Padding will be ignored by default should you provide it.</p>
<p>Pixel values can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTFeatureExtractor.__call__">DeformableDetrImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.DeformableDetrModel.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"pixel_mask"},{anchor:"transformers.DeformableDetrModel.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_queries)</code>, <em>optional</em>) &#x2014;
Not used by default. Can be used to mask object queries.`,name:"decoder_attention_mask"},{anchor:"transformers.DeformableDetrModel.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
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Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
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Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
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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.DeformableDetrModel.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/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deformable_detr/modeling_deformable_detr.py#L1616",returnDescription:`
<p>A <code>transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrModelOutput</code> or a tuple of
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elements depending on the configuration (<a
  href="/docs/transformers/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig"
>DeformableDetrConfig</a>) and inputs.</p>
<ul>
<li><strong>init_reference_points</strong> (<code>torch.FloatTensor</code> of shape  <code>(batch_size, num_queries, 4)</code>) \u2014 Initial reference points sent through the Transformer decoder.</li>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, hidden_size)</code>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</li>
<li><strong>intermediate_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, hidden_size)</code>) \u2014 Stacked intermediate hidden states (output of each layer of the decoder).</li>
<li><strong>intermediate_reference_points</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, 4)</code>) \u2014 Stacked intermediate reference points (reference points of each layer of the decoder).</li>
<li><strong>decoder_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 + one for the output of each layer) of
shape <code>(batch_size, num_queries, hidden_size)</code>. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.</li>
<li><strong>decoder_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, num_queries, num_queries)</code>. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.</li>
<li><strong>cross_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_queries, num_heads, 4, 4)</code>.
Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</li>
<li><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</li>
<li><strong>encoder_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 + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.</li>
<li><strong>encoder_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_queries, num_heads, 4, 4)</code>.
Attentions weights of the encoder, after the attention softmax, used to compute the weighted average in the
self-attention heads.</li>
<li><strong>enc_outputs_class</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Predicted bounding boxes scores where the top <code>config.two_stage_num_proposals</code> scoring bounding boxes are
picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
foreground and background).</li>
<li><strong>enc_outputs_coord_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, 4)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Logits of predicted bounding boxes coordinates in the first stage.</li>
</ul>
`,returnType:`
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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/v4.29.0/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/v4.29.0/src/transformers/models/deformable_detr/modeling_deformable_detr.py#L1824"}}),lo=new z({props:{name:"forward",anchor:"transformers.DeformableDetrForObjectDetection.forward",parameters:[{name:"pixel_values",val:""},{name:"pixel_mask",val:" = None"},{name:"decoder_attention_mask",val:" = None"},{name:"encoder_outputs",val:" = None"},{name:"inputs_embeds",val:" = None"},{name:"decoder_inputs_embeds",val:" = None"},{name:"labels",val:" = None"},{name:"output_attentions",val:" = None"},{name:"output_hidden_states",val:" = None"},{name:"return_dict",val:" = None"}],parametersDescription:[{anchor:"transformers.DeformableDetrForObjectDetection.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. Padding will be ignored by default should you provide it.</p>
<p>Pixel values can be obtained using <a href="/docs/transformers/v4.29.0/en/model_doc/auto#transformers.AutoImageProcessor">AutoImageProcessor</a>. See <a href="/docs/transformers/v4.29.0/en/model_doc/dpt#transformers.DPTFeatureExtractor.__call__">DeformableDetrImageProcessor.<strong>call</strong>()</a>
for details.`,name:"pixel_values"},{anchor:"transformers.DeformableDetrForObjectDetection.forward.pixel_mask",description:`<strong>pixel_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, height, width)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding pixel values. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for pixels that are real (i.e. <strong>not masked</strong>),</li>
<li>0 for pixels that are padding (i.e. <strong>masked</strong>).</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"pixel_mask"},{anchor:"transformers.DeformableDetrForObjectDetection.forward.decoder_attention_mask",description:`<strong>decoder_attention_mask</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, num_queries)</code>, <em>optional</em>) &#x2014;
Not used by default. Can be used to mask object queries.`,name:"decoder_attention_mask"},{anchor:"transformers.DeformableDetrForObjectDetection.forward.encoder_outputs",description:`<strong>encoder_outputs</strong> (<code>tuple(tuple(torch.FloatTensor)</code>, <em>optional</em>) &#x2014;
Tuple consists of (<code>last_hidden_state</code>, <em>optional</em>: <code>hidden_states</code>, <em>optional</em>: <code>attentions</code>)
<code>last_hidden_state</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) is a sequence of
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Optionally, instead of passing the flattened feature map (output of the backbone + projection layer), you
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Optionally, instead of initializing the queries with a tensor of zeros, you can choose to directly pass an
embedded representation.`,name:"decoder_inputs_embeds"},{anchor:"transformers.DeformableDetrForObjectDetection.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.DeformableDetrForObjectDetection.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.DeformableDetrForObjectDetection.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/v4.29.0/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.DeformableDetrForObjectDetection.forward.labels",description:`<strong>labels</strong> (<code>List[Dict]</code> of len <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the bipartite matching loss. List of dicts, each dictionary containing at least the
following 2 keys: &#x2018;class_labels&#x2019; and &#x2018;boxes&#x2019; (the class labels and bounding boxes of an image in the batch
respectively). The class labels themselves should be a <code>torch.LongTensor</code> of len <code>(number of bounding boxes in the image,)</code> and the boxes a <code>torch.FloatTensor</code> of shape <code>(number of bounding boxes in the image, 4)</code>.`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/v4.29.0/src/transformers/models/deformable_detr/modeling_deformable_detr.py#L1876",returnDescription:`
<p>A <code>transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrObjectDetectionOutput</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/v4.29.0/en/model_doc/deformable_detr#transformers.DeformableDetrConfig"
>DeformableDetrConfig</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> are provided)) \u2014 Total loss as a linear combination of a negative log-likehood (cross-entropy) for class prediction and a
bounding box loss. The latter is defined as a linear combination of the L1 loss and the generalized
scale-invariant IoU loss.</li>
<li><strong>loss_dict</strong> (<code>Dict</code>, <em>optional</em>) \u2014 A dictionary containing the individual losses. Useful for logging.</li>
<li><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, num_classes + 1)</code>) \u2014 Classification logits (including no-object) for all queries.</li>
<li><strong>pred_boxes</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, 4)</code>) \u2014 Normalized boxes coordinates for all queries, represented as (center_x, center_y, width, height). These
values are normalized in [0, 1], relative to the size of each individual image in the batch (disregarding
possible padding). You can use <code>~DeformableDetrProcessor.post_process_object_detection</code> to retrieve the
unnormalized bounding boxes.</li>
<li><strong>auxiliary_outputs</strong> (<code>list[Dict]</code>, <em>optional</em>) \u2014 Optional, only returned when auxilary losses are activated (i.e. <code>config.auxiliary_loss</code> is set to <code>True</code>)
and labels are provided. It is a list of dictionaries containing the two above keys (<code>logits</code> and
<code>pred_boxes</code>) for each decoder layer.</li>
<li><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, num_queries, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the decoder of the model.</li>
<li><strong>decoder_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 + one for the output of each layer) of
shape <code>(batch_size, num_queries, hidden_size)</code>. Hidden-states of the decoder at the output of each layer
plus the initial embedding outputs.</li>
<li><strong>decoder_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, num_queries, num_queries)</code>. Attentions weights of the decoder, after the attention softmax, used to compute the weighted
average in the self-attention heads.</li>
<li><strong>cross_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_queries, num_heads, 4, 4)</code>.
Attentions weights of the decoder\u2019s cross-attention layer, after the attention softmax, used to compute the
weighted average in the cross-attention heads.</li>
<li><strong>encoder_last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) \u2014 Sequence of hidden-states at the output of the last layer of the encoder of the model.</li>
<li><strong>encoder_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 + one for the output of each layer) of
shape <code>(batch_size, sequence_length, hidden_size)</code>. Hidden-states of the encoder at the output of each
layer plus the initial embedding outputs.</li>
<li><strong>encoder_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, sequence_length, num_heads, 4, 4)</code>. Attentions weights of the encoder, after the attention softmax, used to compute the weighted average
in the self-attention heads.</li>
<li><strong>intermediate_hidden_states</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, hidden_size)</code>) \u2014 Stacked intermediate hidden states (output of each layer of the decoder).</li>
<li><strong>intermediate_reference_points</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.decoder_layers, num_queries, 4)</code>) \u2014 Stacked intermediate reference points (reference points of each layer of the decoder).</li>
<li><strong>init_reference_points</strong> (<code>torch.FloatTensor</code> of shape  <code>(batch_size, num_queries, 4)</code>) \u2014 Initial reference points sent through the Transformer decoder.</li>
<li><strong>enc_outputs_class</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.num_labels)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Predicted bounding boxes scores where the top <code>config.two_stage_num_proposals</code> scoring bounding boxes are
picked as region proposals in the first stage. Output of bounding box binary classification (i.e.
foreground and background).</li>
<li><strong>enc_outputs_coord_logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, 4)</code>, <em>optional</em>, returned when <code>config.with_box_refine=True</code> and <code>config.two_stage=True</code>) \u2014 Logits of predicted bounding boxes coordinates in the first stage.</li>
</ul>
`,returnType:`
<p><code>transformers.models.deformable_detr.modeling_deformable_detr.DeformableDetrObjectDetectionOutput</code> or <code>tuple(torch.FloatTensor)</code></p>
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