transformers/pr_35674/ja/_app/immutable/nodes/36.d2829c7b.js

import{s as ot,o as at,n as st}from"../chunks/scheduler.9bc65507.js";import{S as it,i as ct,g as s,s as r,r as d,A as mt,h as i,f as n,c as o,j as $,u as p,x as M,k as I,y as t,a as m,v as g,d as f,t as h,w as u}from"../chunks/index.707bf1b6.js";import{D as U}from"../chunks/Docstring.570ddb64.js";import{C as lt}from"../chunks/CodeBlock.54a9f38d.js";import{E as dt}from"../chunks/ExampleCodeBlock.bc2be6bc.js";import{H as Pe,E as pt}from"../chunks/EditOnGithub.922df6ba.js";function gt(pe){let l,W="Examples:",C,v,x;return v=new lt({props:{code:"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",highlighted:`<span class="hljs-comment"># We can&#x27;t instantiate directly the base class *ImageProcessingMixin* so let&#x27;s show the examples on a</span>
<span class="hljs-comment"># derived class: *CLIPImageProcessor*</span>
image_processor = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>
)  <span class="hljs-comment"># Download image_processing_config from huggingface.co and cache.</span>
image_processor = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;./test/saved_model/&quot;</span>
)  <span class="hljs-comment"># E.g. image processor (or model) was saved using *save_pretrained(&#x27;./test/saved_model/&#x27;)*</span>
image_processor = CLIPImageProcessor.from_pretrained(<span class="hljs-string">&quot;./test/saved_model/preprocessor_config.json&quot;</span>)
image_processor = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>, do_normalize=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>
)
<span class="hljs-keyword">assert</span> image_processor.do_normalize <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
image_processor, unused_kwargs = CLIPImageProcessor.from_pretrained(
    <span class="hljs-string">&quot;openai/clip-vit-base-patch32&quot;</span>, do_normalize=<span class="hljs-literal">False</span>, foo=<span class="hljs-literal">False</span>, return_unused_kwargs=<span class="hljs-literal">True</span>
)
<span class="hljs-keyword">assert</span> image_processor.do_normalize <span class="hljs-keyword">is</span> <span class="hljs-literal">False</span>
<span class="hljs-keyword">assert</span> unused_kwargs == {<span class="hljs-string">&quot;foo&quot;</span>: <span class="hljs-literal">False</span>}`,wrap:!1}}),{c(){l=s("p"),l.textContent=W,C=r(),d(v.$$.fragment)},l(c){l=i(c,"P",{"data-svelte-h":!0}),M(l)!=="svelte-kvfsh7"&&(l.textContent=W),C=o(c),p(v.$$.fragment,c)},m(c,w){m(c,l,w),m(c,C,w),g(v,c,w),x=!0},p:st,i(c){x||(f(v.$$.fragment,c),x=!0)},o(c){h(v.$$.fragment,c),x=!1},d(c){c&&(n(l),n(C)),u(v,c)}}}function ft(pe){let l,W,C,v,x,c,w,Ye="画像プロセッサは、ビジョン モデルの入力特徴の準備とその出力の後処理を担当します。これには、サイズ変更、正規化、PyTorch、TensorFlow、Flax、Numpy テンソルへの変換などの変換が含まれます。ロジットをセグメンテーション マスクに変換するなど、モデル固有の後処理も含まれる場合があります。",ge,L,fe,y,q,je,ee,He=`This is an image processor mixin used to provide saving/loading functionality for sequential and image feature
extractors.`,Ue,P,E,Be,te,Ae='Instantiate a type of <a href="/docs/transformers/pr_35674/ja/internal/image_processing_utils#transformers.ImageProcessingMixin">ImageProcessingMixin</a> from an image processor.',Ne,B,Ze,N,R,ke,ne,Ge=`Save an image processor object to the directory <code>save_directory</code>, so that it can be re-loaded using the
<a href="/docs/transformers/pr_35674/ja/internal/image_processing_utils#transformers.ImageProcessingMixin.from_pretrained">from_pretrained()</a> class method.`,he,X,ue,_,V,ze,re,Qe='Holds the output of the <a href="/docs/transformers/pr_35674/ja/main_classes/feature_extractor#transformers.SequenceFeatureExtractor.pad">pad()</a> and feature extractor specific <code>__call__</code> methods.',Je,oe,Ke="This class is derived from a python dictionary and can be used as a dictionary.",De,Z,S,Fe,ae,Oe="Convert the inner content to tensors.",We,k,Y,Le,se,et=`Send all values to device by calling <code>v.to(*args, **kwargs)</code> (PyTorch only). This should support casting in
different <code>dtypes</code> and sending the <code>BatchFeature</code> to a different <code>device</code>.`,_e,H,ye,b,A,qe,z,G,Ee,ie,tt=`Center crop an image to <code>(size[&quot;height&quot;], size[&quot;width&quot;])</code>. If the input size is smaller than <code>crop_size</code> along
any edge, the image is padded with 0’s and then center cropped.`,Re,J,Q,Xe,ce,nt="Normalize an image. image = (image - image_mean) / image_std.",Ve,D,K,Se,me,rt="Rescale an image by a scale factor. image = image * scale.",be,O,ve,de,xe;return x=new Pe({props:{title:"Image Processor",local:"image-processor",headingTag:"h1"}}),L=new Pe({props:{title:"ImageProcessingMixin",local:"transformers.ImageProcessingMixin",headingTag:"h2"}}),q=new U({props:{name:"class transformers.ImageProcessingMixin",anchor:"transformers.ImageProcessingMixin",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_base.py#L72"}}),E=new U({props:{name:"from_pretrained",anchor:"transformers.ImageProcessingMixin.from_pretrained",parameters:[{name:"pretrained_model_name_or_path",val:": typing.Union[str, os.PathLike]"},{name:"cache_dir",val:": typing.Union[str, os.PathLike, NoneType] = None"},{name:"force_download",val:": bool = False"},{name:"local_files_only",val:": bool = False"},{name:"token",val:": typing.Union[str, bool, NoneType] = None"},{name:"revision",val:": str = 'main'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.from_pretrained.pretrained_model_name_or_path",description:`<strong>pretrained_model_name_or_path</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
This can be either:</p>
<ul>
<li>a string, the <em>model id</em> of a pretrained image_processor hosted inside a model repo on
huggingface.co.</li>
<li>a path to a <em>directory</em> containing a image processor file saved using the
<a href="/docs/transformers/pr_35674/ja/internal/image_processing_utils#transformers.ImageProcessingMixin.save_pretrained">save_pretrained()</a> method, e.g.,
<code>./my_model_directory/</code>.</li>
<li>a path or url to a saved image processor JSON <em>file</em>, e.g.,
<code>./my_model_directory/preprocessor_config.json</code>.</li>
</ul>`,name:"pretrained_model_name_or_path"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.cache_dir",description:`<strong>cache_dir</strong> (<code>str</code> or <code>os.PathLike</code>, <em>optional</em>) &#x2014;
Path to a directory in which a downloaded pretrained model image processor should be cached if the
standard cache should not be used.`,name:"cache_dir"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.force_download",description:`<strong>force_download</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to force to (re-)download the image processor files and override the cached versions if
they exist.`,name:"force_download"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.resume_download",description:`<strong>resume_download</strong> &#x2014;
Deprecated and ignored. All downloads are now resumed by default when possible.
Will be removed in v5 of Transformers.`,name:"resume_download"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.proxies",description:`<strong>proxies</strong> (<code>Dict[str, str]</code>, <em>optional</em>) &#x2014;
A dictionary of proxy servers to use by protocol or endpoint, e.g., <code>{&apos;http&apos;: &apos;foo.bar:3128&apos;, &apos;http://hostname&apos;: &apos;foo.bar:4012&apos;}.</code> The proxies are used on each request.`,name:"proxies"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.token",description:`<strong>token</strong> (<code>str</code> or <code>bool</code>, <em>optional</em>) &#x2014;
The token to use as HTTP bearer authorization for remote files. If <code>True</code>, or not specified, will use
the token generated when running <code>huggingface-cli login</code> (stored in <code>~/.huggingface</code>).`,name:"token"},{anchor:"transformers.ImageProcessingMixin.from_pretrained.revision",description:`<strong>revision</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;main&quot;</code>) &#x2014;
The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
git-based system for storing models and other artifacts on huggingface.co, so <code>revision</code> can be any
identifier allowed by git.`,name:"revision"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_base.py#L99",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A image processor of type <a
  href="/docs/transformers/pr_35674/ja/internal/image_processing_utils#transformers.ImageProcessingMixin"
>ImageProcessingMixin</a>.</p>
`}}),B=new dt({props:{anchor:"transformers.ImageProcessingMixin.from_pretrained.example",$$slots:{default:[gt]},$$scope:{ctx:pe}}}),R=new U({props:{name:"save_pretrained",anchor:"transformers.ImageProcessingMixin.save_pretrained",parameters:[{name:"save_directory",val:": typing.Union[str, os.PathLike]"},{name:"push_to_hub",val:": bool = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.ImageProcessingMixin.save_pretrained.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>os.PathLike</code>) &#x2014;
Directory where the image processor JSON file will be saved (will be created if it does not exist).`,name:"save_directory"},{anchor:"transformers.ImageProcessingMixin.save_pretrained.push_to_hub",description:`<strong>push_to_hub</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to push your model to the Hugging Face model hub after saving it. You can specify the
repository you want to push to with <code>repo_id</code> (will default to the name of <code>save_directory</code> in your
namespace).`,name:"push_to_hub"},{anchor:"transformers.ImageProcessingMixin.save_pretrained.kwargs",description:`<strong>kwargs</strong> (<code>Dict[str, Any]</code>, <em>optional</em>) &#x2014;
Additional key word arguments passed along to the <a href="/docs/transformers/pr_35674/ja/main_classes/model#transformers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method.`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_base.py#L213"}}),X=new Pe({props:{title:"BatchFeature",local:"transformers.BatchFeature",headingTag:"h2"}}),V=new U({props:{name:"class transformers.BatchFeature",anchor:"transformers.BatchFeature",parameters:[{name:"data",val:": typing.Optional[typing.Dict[str, typing.Any]] = None"},{name:"tensor_type",val:": typing.Union[NoneType, str, transformers.utils.generic.TensorType] = None"}],parametersDescription:[{anchor:"transformers.BatchFeature.data",description:`<strong>data</strong> (<code>dict</code>, <em>optional</em>) &#x2014;
Dictionary of lists/arrays/tensors returned by the <strong>call</strong>/pad methods (&#x2018;input_values&#x2019;, &#x2018;attention_mask&#x2019;,
etc.).`,name:"data"},{anchor:"transformers.BatchFeature.tensor_type",description:`<strong>tensor_type</strong> (<code>Union[None, str, TensorType]</code>, <em>optional</em>) &#x2014;
You can give a tensor_type here to convert the lists of integers in PyTorch/TensorFlow/Numpy Tensors at
initialization.`,name:"tensor_type"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/feature_extraction_utils.py#L62"}}),S=new U({props:{name:"convert_to_tensors",anchor:"transformers.BatchFeature.convert_to_tensors",parameters:[{name:"tensor_type",val:": typing.Union[str, transformers.utils.generic.TensorType, NoneType] = None"}],parametersDescription:[{anchor:"transformers.BatchFeature.convert_to_tensors.tensor_type",description:`<strong>tensor_type</strong> (<code>str</code> or <a href="/docs/transformers/pr_35674/ja/internal/file_utils#transformers.TensorType">TensorType</a>, <em>optional</em>) &#x2014;
The type of tensors to use. If <code>str</code>, should be one of the values of the enum <a href="/docs/transformers/pr_35674/ja/internal/file_utils#transformers.TensorType">TensorType</a>. If
<code>None</code>, no modification is done.`,name:"tensor_type"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/feature_extraction_utils.py#L175"}}),Y=new U({props:{name:"to",anchor:"transformers.BatchFeature.to",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BatchFeature.to.args",description:`<strong>args</strong> (<code>Tuple</code>) &#x2014;
Will be passed to the <code>to(...)</code> function of the tensors.`,name:"args"},{anchor:"transformers.BatchFeature.to.kwargs",description:`<strong>kwargs</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>to(...)</code> function of the tensors.
To enable asynchronous data transfer, set the <code>non_blocking</code> flag in <code>kwargs</code> (defaults to <code>False</code>).`,name:"kwargs"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/feature_extraction_utils.py#L206",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The same instance after modification.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_35674/ja/main_classes/feature_extractor#transformers.BatchFeature"
>BatchFeature</a></p>
`}}),H=new Pe({props:{title:"BaseImageProcessor",local:"transformers.BaseImageProcessor",headingTag:"h2"}}),A=new U({props:{name:"class transformers.BaseImageProcessor",anchor:"transformers.BaseImageProcessor",parameters:[{name:"**kwargs",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_utils.py#L35"}}),G=new U({props:{name:"center_crop",anchor:"transformers.BaseImageProcessor.center_crop",parameters:[{name:"image",val:": ndarray"},{name:"size",val:": typing.Dict[str, int]"},{name:"data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BaseImageProcessor.center_crop.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
Image to center crop.`,name:"image"},{anchor:"transformers.BaseImageProcessor.center_crop.size",description:`<strong>size</strong> (<code>Dict[str, int]</code>) &#x2014;
Size of the output image.`,name:"size"},{anchor:"transformers.BaseImageProcessor.center_crop.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:<ul>
<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>
</ul>`,name:"data_format"},{anchor:"transformers.BaseImageProcessor.center_crop.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<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>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_utils.py#L115"}}),Q=new U({props:{name:"normalize",anchor:"transformers.BaseImageProcessor.normalize",parameters:[{name:"image",val:": ndarray"},{name:"mean",val:": typing.Union[float, typing.Iterable[float]]"},{name:"std",val:": typing.Union[float, typing.Iterable[float]]"},{name:"data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BaseImageProcessor.normalize.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
Image to normalize.`,name:"image"},{anchor:"transformers.BaseImageProcessor.normalize.mean",description:`<strong>mean</strong> (<code>float</code> or <code>Iterable[float]</code>) &#x2014;
Image mean to use for normalization.`,name:"mean"},{anchor:"transformers.BaseImageProcessor.normalize.std",description:`<strong>std</strong> (<code>float</code> or <code>Iterable[float]</code>) &#x2014;
Image standard deviation to use for normalization.`,name:"std"},{anchor:"transformers.BaseImageProcessor.normalize.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:<ul>
<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>
</ul>`,name:"data_format"},{anchor:"transformers.BaseImageProcessor.normalize.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<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>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_utils.py#L78",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The normalized image.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>np.ndarray</code></p>
`}}),K=new U({props:{name:"rescale",anchor:"transformers.BaseImageProcessor.rescale",parameters:[{name:"image",val:": ndarray"},{name:"scale",val:": float"},{name:"data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"input_data_format",val:": typing.Union[transformers.image_utils.ChannelDimension, str, NoneType] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.BaseImageProcessor.rescale.image",description:`<strong>image</strong> (<code>np.ndarray</code>) &#x2014;
Image to rescale.`,name:"image"},{anchor:"transformers.BaseImageProcessor.rescale.scale",description:`<strong>scale</strong> (<code>float</code>) &#x2014;
The scaling factor to rescale pixel values by.`,name:"scale"},{anchor:"transformers.BaseImageProcessor.rescale.data_format",description:`<strong>data_format</strong> (<code>str</code> or <code>ChannelDimension</code>, <em>optional</em>) &#x2014;
The channel dimension format for the output image. If unset, the channel dimension format of the input
image is used. Can be one of:<ul>
<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>
</ul>`,name:"data_format"},{anchor:"transformers.BaseImageProcessor.rescale.input_data_format",description:`<strong>input_data_format</strong> (<code>ChannelDimension</code> or <code>str</code>, <em>optional</em>) &#x2014;
The channel dimension format for the input image. If unset, the channel dimension format is inferred
from the input image. Can be one of:<ul>
<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>
</ul>`,name:"input_data_format"}],source:"https://github.com/huggingface/transformers/blob/vr_35674/src/transformers/image_processing_utils.py#L46",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The rescaled image.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>np.ndarray</code></p>
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