Update development requirements and enhance document search functiona…

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tomas-gajarsky · Nov 9, 2024 · 849d855 · 849d855
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diff --git a/CHANGELOG.md b/CHANGELOG.md
diff --git a/LICENSE b/LICENSE
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diff --git a/conf/analyzer/detector/retinaface.yaml b/conf/analyzer/detector/retinaface.yaml
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diff --git a/conf/analyzer/unifier/img_380.yaml b/conf/analyzer/unifier/img_380.yaml
diff --git a/conf/analyzer/utilizer/align/lmk3d_mesh_pose.yaml b/conf/analyzer/utilizer/align/lmk3d_mesh_pose.yaml
diff --git a/conf/analyzer/utilizer/draw_boxes/torchvision_boxes.yaml b/conf/analyzer/utilizer/draw_boxes/torchvision_boxes.yaml
diff --git a/conf/analyzer/utilizer/draw_landmarks/torchvision_keypoints.yaml b/conf/analyzer/utilizer/draw_landmarks/torchvision_keypoints.yaml
diff --git a/conf/analyzer/utilizer/save/image_saver.yaml b/conf/analyzer/utilizer/save/image_saver.yaml
diff --git a/conf/config.yaml b/conf/config.yaml
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diff --git a/data/input/tensor.pt b/data/input/tensor.pt
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diff --git a/docker-compose.dev.yml b/docker-compose.dev.yml
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diff --git a/docker/Dockerfile.dev b/docker/Dockerfile.dev
diff --git a/docker/Dockerfile.dev.gpu b/docker/Dockerfile.dev.gpu
diff --git a/docker/Dockerfile.gpu b/docker/Dockerfile.gpu
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diff --git a/docs/doc-search.html b/docs/doc-search.html
@@ -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>
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+    <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});
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@@ -67,6 +70,60 @@ <h3 id="search-status"></h3>
                 this.add(doc);
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+        }
+    }
+
+    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) {

diff --git a/docs/facetorch/analyzer/core.html b/docs/facetorch/analyzer/core.html
diff --git a/docs/facetorch/analyzer/detector/core.html b/docs/facetorch/analyzer/detector/core.html
@@ -2,18 +2,21 @@
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 <title>facetorch.analyzer.detector.core API documentation</title>
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 </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>
 </body>
-</html>
+</html>