Update development requirements and enhance document search functionality

Author: tomas-gajarsky

.dockerignore

@@ -4,8 +4,8 @@
     <meta charset="utf-8">
     <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1">
     <title>Search</title>
-    <link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/sanitize.min.css" integrity="sha256-PK9q560IAAa6WVRRh76LtCaI8pjTJ2z11v0miyNNjrs=" crossorigin>
-    <link rel="preload stylesheet" as="style" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/11.0.1/typography.min.css" integrity="sha256-7l/o7C8jubJiy74VsKTidCy1yBkRtiUGbVkYBylBqUg=" crossorigin>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/sanitize.min.css" integrity="sha512-y1dtMcuvtTMJc1yPgEqF0ZjQbhnc/bFhyvIyVNb9Zk5mIGtqVaAB1Ttl28su8AvFMOY0EwRbAe+HCLqj6W7/KA==" crossorigin>
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/typography.min.css" integrity="sha512-Y1DYSb995BAfxobCkKepB1BqJJTPrOp3zPL74AWFugHHmmdcvO+C48WLrUOlhGMc0QG7AE3f7gmvvcrmX2fDoA==" crossorigin>
     <style>
         body {margin: 0 1em;}
         footer,
@@ -40,7 +40,7 @@ <h3 id="search-status"></h3>
 </footer>
 
 <script src="index.js"></script>
-<script src="https://cdnjs.cloudflare.com/ajax/libs/lunr.js/2.3.8/lunr.min.js" integrity="sha512-HiJdkRySzXhiUcX2VweXaiy8yeY212ep/j51zR/z5IPCX4ZUOxaf6naJ/0dQL/2l+ZL+B9in/u4nT8QJZ/3mig==" crossorigin></script>
+<script src="https://cdnjs.cloudflare.com/ajax/libs/lunr.js/2.3.9/lunr.min.js" integrity="sha512-4xUl/d6D6THrAnXAwGajXkoWaeMNwEKK4iNfq5DotEbLPAfk6FSxSP3ydNxqDgCw1c/0Z1Jg6L8h2j+++9BZmg==" crossorigin></script>
 <script>
     'use strict';
 
@@ -52,7 +52,10 @@ <h3 id="search-status"></h3>
     }
 
     async function build_index() {
-        return lunr(function () {
+        try {
+            return lunr.Index.load(_expand(INDEX));  // Prebuilt index
+        } catch {
+            return lunr(function () {
             this.ref('i');
             this.field('name', {boost: 10});
             this.field('ref', {boost: 5});
@@ -67,6 +70,60 @@ <h3 id="search-status"></h3>
                 this.add(doc);
             }, this);
         });
+        }
+    }
+
+    function _expand(compact) {
+        // https://john-millikin.com/compacting-lunr-search-indices
+        const fields = compact["fields"];
+        const fieldVectors = compact["fieldVectors"].map((item) => {
+            const id = item[0];
+            const vectors = item[1];
+            let prev = null;
+            const expanded = vectors.map((v, ii) => {
+                if (ii % 2 === 0) {
+                    if (v === null) {
+                        v = prev + 1;
+                    }
+                    prev = v;
+                }
+                return v;
+            });
+            return [id, expanded];
+        });
+        const invertedIndex = compact["invertedIndex"].map((item, itemIdx) => {
+            const token = item[0];
+            const fieldMap = {"_index": itemIdx};
+            fields.forEach((field, fieldIdx) => {
+                const matches = {};
+                let docRef = null;
+                item[fieldIdx + 1].forEach((v, ii) => {
+                    if (ii % 2 === 0) {
+                        docRef = fieldVectors[v][0].slice((field + '/').length);
+                    } else {
+                        matches[docRef] = v;
+                    }
+                });
+                fieldMap[field] = matches;
+            })
+            return [token, fieldMap];
+        });
+        invertedIndex.sort((a, b) => {
+            if (a[0] < b[0]) {
+                return -1;
+            }
+            if (a[0] > b[0]) {
+                return 1;
+            }
+            return 0;
+        });
+        return {
+            "version": compact["version"],
+            "fields": fields,
+            "fieldVectors": fieldVectors,
+            "invertedIndex": invertedIndex,
+            "pipeline": compact["pipeline"],
+        };
     }
 
     function search(query) {

.flake8

@@ -2,18 +2,21 @@
 <html lang="en">
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 <meta charset="utf-8">
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+<meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1">
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 <title>facetorch.analyzer.detector.core API documentation</title>
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+<meta name="description" content="">
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/sanitize.min.css" integrity="sha512-y1dtMcuvtTMJc1yPgEqF0ZjQbhnc/bFhyvIyVNb9Zk5mIGtqVaAB1Ttl28su8AvFMOY0EwRbAe+HCLqj6W7/KA==" crossorigin>
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/10up-sanitize.css/13.0.0/typography.min.css" integrity="sha512-Y1DYSb995BAfxobCkKepB1BqJJTPrOp3zPL74AWFugHHmmdcvO+C48WLrUOlhGMc0QG7AE3f7gmvvcrmX2fDoA==" crossorigin>
+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/github.min.css" crossorigin>
+<style>:root{--highlight-color:#fe9}.flex{display:flex !important}body{line-height:1.5em}#content{padding:20px}#sidebar{padding:1.5em;overflow:hidden}#sidebar > *:last-child{margin-bottom:2cm}#lunr-search{width:100%;font-size:1em;padding:6px 9px 5px 9px;border:1px solid silver}.http-server-breadcrumbs{font-size:130%;margin:0 0 15px 0}#footer{font-size:.75em;padding:5px 30px;border-top:1px solid #ddd;text-align:right}#footer p{margin:0 0 0 1em;display:inline-block}#footer p:last-child{margin-right:30px}h1,h2,h3,h4,h5{font-weight:300}h1{font-size:2.5em;line-height:1.1em}h2{font-size:1.75em;margin:2em 0 .50em 0}h3{font-size:1.4em;margin:1.6em 0 .7em 0}h4{margin:0;font-size:105%}h1:target,h2:target,h3:target,h4:target,h5:target,h6:target{background:var(--highlight-color);padding:.2em 0}a{color:#058;text-decoration:none;transition:color .2s ease-in-out}a:visited{color:#503}a:hover{color:#b62}.title code{font-weight:bold}h2[id^="header-"]{margin-top:2em}.ident{color:#900;font-weight:bold}pre code{font-size:.8em;line-height:1.4em;padding:1em;display:block}code{background:#f3f3f3;font-family:"DejaVu Sans Mono",monospace;padding:1px 4px;overflow-wrap:break-word}h1 code{background:transparent}pre{border-top:1px solid #ccc;border-bottom:1px solid #ccc;margin:1em 0}#http-server-module-list{display:flex;flex-flow:column}#http-server-module-list div{display:flex}#http-server-module-list dt{min-width:10%}#http-server-module-list p{margin-top:0}.toc ul,#index{list-style-type:none;margin:0;padding:0}#index code{background:transparent}#index h3{border-bottom:1px solid #ddd}#index ul{padding:0}#index h4{margin-top:.6em;font-weight:bold}@media (min-width:200ex){#index .two-column{column-count:2}}@media (min-width:300ex){#index .two-column{column-count:3}}dl{margin-bottom:2em}dl dl:last-child{margin-bottom:4em}dd{margin:0 0 1em 3em}#header-classes + dl > dd{margin-bottom:3em}dd dd{margin-left:2em}dd p{margin:10px 0}.name{background:#eee;font-size:.85em;padding:5px 10px;display:inline-block;min-width:40%}.name:hover{background:#e0e0e0}dt:target .name{background:var(--highlight-color)}.name > span:first-child{white-space:nowrap}.name.class > span:nth-child(2){margin-left:.4em}.inherited{color:#999;border-left:5px solid #eee;padding-left:1em}.inheritance em{font-style:normal;font-weight:bold}.desc h2{font-weight:400;font-size:1.25em}.desc h3{font-size:1em}.desc dt code{background:inherit}.source summary,.git-link-div{color:#666;text-align:right;font-weight:400;font-size:.8em;text-transform:uppercase}.source summary > *{white-space:nowrap;cursor:pointer}.git-link{color:inherit;margin-left:1em}.source pre{max-height:500px;overflow:auto;margin:0}.source pre code{font-size:12px;overflow:visible}.hlist{list-style:none}.hlist li{display:inline}.hlist li:after{content:',\2002'}.hlist li:last-child:after{content:none}.hlist .hlist{display:inline;padding-left:1em}img{max-width:100%}td{padding:0 .5em}.admonition{padding:.1em 1em;margin-bottom:1em}.admonition-title{font-weight:bold}.admonition.note,.admonition.info,.admonition.important{background:#aef}.admonition.todo,.admonition.versionadded,.admonition.tip,.admonition.hint{background:#dfd}.admonition.warning,.admonition.versionchanged,.admonition.deprecated{background:#fd4}.admonition.error,.admonition.danger,.admonition.caution{background:lightpink}</style>
+<style media="screen and (min-width: 700px)">@media screen and (min-width:700px){#sidebar{width:30%;height:100vh;overflow:auto;position:sticky;top:0}#content{width:70%;max-width:100ch;padding:3em 4em;border-left:1px solid #ddd}pre code{font-size:1em}.name{font-size:1em}main{display:flex;flex-direction:row-reverse;justify-content:flex-end}.toc ul ul,#index ul ul{padding-left:1em}.toc > ul > li{margin-top:.5em}}</style>
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-<script>window.addEventListener('DOMContentLoaded', () => hljs.initHighlighting())</script>
+<script defer src="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js" integrity="sha512-D9gUyxqja7hBtkWpPWGt9wfbfaMGVt9gnyCvYa+jojwwPHLCzUm5i8rpk7vD7wNee9bA35eYIjobYPaQuKS1MQ==" crossorigin></script>
+<script>window.addEventListener('DOMContentLoaded', () => {
+hljs.configure({languages: ['bash', 'css', 'diff', 'graphql', 'ini', 'javascript', 'json', 'plaintext', 'python', 'python-repl', 'rust', 'shell', 'sql', 'typescript', 'xml', 'yaml']});
+hljs.highlightAll();
+})</script>
 </head>
 <body>
 <main>
@@ -22,64 +25,6 @@
 <h1 class="title">Module <code>facetorch.analyzer.detector.core</code></h1>
 </header>
 <section id="section-intro">
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">import torch
-from codetiming import Timer
-from facetorch.base import BaseDownloader, BaseModel
-from facetorch.datastruct import ImageData
-from facetorch.logger import LoggerJsonFile
-
-from .post import BaseDetPostProcessor
-from .pre import BaseDetPreProcessor
-
-logger = LoggerJsonFile().logger
-
-
-class FaceDetector(BaseModel):
-    @Timer(
-        &#34;FaceDetector.__init__&#34;, &#34;{name}: {milliseconds:.2f} ms&#34;, logger=logger.debug
-    )
-    def __init__(
-        self,
-        downloader: BaseDownloader,
-        device: torch.device,
-        preprocessor: BaseDetPreProcessor,
-        postprocessor: BaseDetPostProcessor,
-        **kwargs
-    ):
-        &#34;&#34;&#34;FaceDetector is a wrapper around a neural network model that is trained to detect faces.
-
-        Args:
-            downloader (BaseDownloader): Downloader that downloads the model.
-            device (torch.device): Torch device cpu or cuda for the model.
-            preprocessor (BaseDetPreProcessor): Preprocessor that runs before the model.
-            postprocessor (BaseDetPostProcessor): Postprocessor that runs after the model.
-        &#34;&#34;&#34;
-        self.__dict__.update(kwargs)
-        super().__init__(downloader, device)
-
-        self.preprocessor = preprocessor
-        self.postprocessor = postprocessor
-
-    @Timer(&#34;FaceDetector.run&#34;, &#34;{name}: {milliseconds:.2f} ms&#34;, logger=logger.debug)
-    def run(self, data: ImageData) -&gt; ImageData:
-        &#34;&#34;&#34;Detect all faces in the image.
-
-        Args:
-            ImageData: ImageData object containing the image tensor with values between 0 - 255 and shape (batch_size, channels, height, width).
-
-        Returns:
-            ImageData: Image data object with Detection tensors and detected Face objects.
-        &#34;&#34;&#34;
-        data = self.preprocessor.run(data)
-        logits = self.inference(data.tensor)
-        data = self.postprocessor.run(data, logits)
-
-        return data</code></pre>
-</details>
 </section>
 <section>
 </section>
@@ -174,26 +119,6 @@ <h2 id="returns">Returns</h2>
 <dt><code>ImageData</code></dt>
 <dd>Image data object with Detection tensors and detected Face objects.</dd>
 </dl></div>
-<details class="source">
-<summary>
-<span>Expand source code</span>
-</summary>
-<pre><code class="python">@Timer(&#34;FaceDetector.run&#34;, &#34;{name}: {milliseconds:.2f} ms&#34;, logger=logger.debug)
-def run(self, data: ImageData) -&gt; ImageData:
-    &#34;&#34;&#34;Detect all faces in the image.
-
-    Args:
-        ImageData: ImageData object containing the image tensor with values between 0 - 255 and shape (batch_size, channels, height, width).
-
-    Returns:
-        ImageData: Image data object with Detection tensors and detected Face objects.
-    &#34;&#34;&#34;
-    data = self.preprocessor.run(data)
-    logits = self.inference(data.tensor)
-    data = self.postprocessor.run(data, logits)
-
-    return data</code></pre>
-</details>
 </dd>
 </dl>
 <h3>Inherited members</h3>
@@ -256,7 +181,6 @@ <h3>Inherited members</h3>
 }).setContent('<iframe src="' + url + '"></iframe>').open();
 }
 </script>
-<h1>Index</h1>
 <div class="toc">
 <ul></ul>
 </div>
@@ -280,7 +204,7 @@ <h4><code><a title="facetorch.analyzer.detector.core.FaceDetector" href="#faceto
 </nav>
 </main>
 <footer id="footer">
-<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.10.0</a>.</p>
+<p>Generated by <a href="https://pdoc3.github.io/pdoc" title="pdoc: Python API documentation generator"><cite>pdoc</cite> 0.11.1</a>.</p>
 </footer>
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+</html>