-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathanomaly_detection.html
795 lines (607 loc) · 46.5 KB
/
anomaly_detection.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
<!DOCTYPE html>
<html lang="en" data-content_root="./" >
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<title>Anomaly Detection with PaDiM — Case 1 - Detectie van oppervlaktefouten op vlakke platen</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "";
document.documentElement.dataset.theme = localStorage.getItem("theme") || "";
</script>
<!-- Loaded before other Sphinx assets -->
<link href="_static/styles/theme.css?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
<link href="_static/styles/bootstrap.css?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
<link href="_static/styles/pydata-sphinx-theme.css?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
<link href="_static/vendor/fontawesome/6.5.2/css/all.min.css?digest=3ee479438cf8b5e0d341" rel="stylesheet" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-solid-900.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-brands-400.woff2" />
<link rel="preload" as="font" type="font/woff2" crossorigin href="_static/vendor/fontawesome/6.5.2/webfonts/fa-regular-400.woff2" />
<link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
<link rel="stylesheet" type="text/css" href="_static/styles/sphinx-book-theme.css?v=a3416100" />
<link rel="stylesheet" type="text/css" href="_static/togglebutton.css?v=13237357" />
<link rel="stylesheet" type="text/css" href="_static/copybutton.css?v=76b2166b" />
<link rel="stylesheet" type="text/css" href="_static/mystnb.4510f1fc1dee50b3e5859aac5469c37c29e427902b24a333a5f9fcb2f0b3ac41.css?v=be8a1c11" />
<link rel="stylesheet" type="text/css" href="_static/sphinx-thebe.css?v=4fa983c6" />
<link rel="stylesheet" type="text/css" href="_static/sphinx-design.min.css?v=87e54e7c" />
<!-- Pre-loaded scripts that we'll load fully later -->
<link rel="preload" as="script" href="_static/scripts/bootstrap.js?digest=3ee479438cf8b5e0d341" />
<link rel="preload" as="script" href="_static/scripts/pydata-sphinx-theme.js?digest=3ee479438cf8b5e0d341" />
<script src="_static/vendor/fontawesome/6.5.2/js/all.min.js?digest=3ee479438cf8b5e0d341"></script>
<script src="_static/documentation_options.js?v=9eb32ce0"></script>
<script src="_static/doctools.js?v=9a2dae69"></script>
<script src="_static/sphinx_highlight.js?v=dc90522c"></script>
<script src="_static/clipboard.min.js?v=a7894cd8"></script>
<script src="_static/copybutton.js?v=f281be69"></script>
<script src="_static/scripts/sphinx-book-theme.js?v=887ef09a"></script>
<script>let toggleHintShow = 'Click to show';</script>
<script>let toggleHintHide = 'Click to hide';</script>
<script>let toggleOpenOnPrint = 'true';</script>
<script src="_static/togglebutton.js?v=4a39c7ea"></script>
<script>var togglebuttonSelector = '.toggle, .admonition.dropdown';</script>
<script src="_static/design-tabs.js?v=f930bc37"></script>
<script>const THEBE_JS_URL = "https://unpkg.com/[email protected]/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
<script async="async" src="_static/sphinx-thebe.js?v=c100c467"></script>
<script>var togglebuttonSelector = '.toggle, .admonition.dropdown';</script>
<script>const THEBE_JS_URL = "https://unpkg.com/[email protected]/lib/index.js"; const thebe_selector = ".thebe,.cell"; const thebe_selector_input = "pre"; const thebe_selector_output = ".output, .cell_output"</script>
<script>DOCUMENTATION_OPTIONS.pagename = 'anomaly_detection';</script>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Binary Classification" href="binary_classification.html" />
<link rel="prev" title="Dataset Creation" href="dataset_creation.html" />
<meta name="viewport" content="width=device-width, initial-scale=1"/>
<meta name="docsearch:language" content="en"/>
</head>
<body data-bs-spy="scroll" data-bs-target=".bd-toc-nav" data-offset="180" data-bs-root-margin="0px 0px -60%" data-default-mode="">
<div id="pst-skip-link" class="skip-link d-print-none"><a href="#main-content">Skip to main content</a></div>
<div id="pst-scroll-pixel-helper"></div>
<button type="button" class="btn rounded-pill" id="pst-back-to-top">
<i class="fa-solid fa-arrow-up"></i>Back to top</button>
<input type="checkbox"
class="sidebar-toggle"
id="pst-primary-sidebar-checkbox"/>
<label class="overlay overlay-primary" for="pst-primary-sidebar-checkbox"></label>
<input type="checkbox"
class="sidebar-toggle"
id="pst-secondary-sidebar-checkbox"/>
<label class="overlay overlay-secondary" for="pst-secondary-sidebar-checkbox"></label>
<div class="search-button__wrapper">
<div class="search-button__overlay"></div>
<div class="search-button__search-container">
<form class="bd-search d-flex align-items-center"
action="search.html"
method="get">
<i class="fa-solid fa-magnifying-glass"></i>
<input type="search"
class="form-control"
name="q"
id="search-input"
placeholder="Search this book..."
aria-label="Search this book..."
autocomplete="off"
autocorrect="off"
autocapitalize="off"
spellcheck="false"/>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
</form></div>
</div>
<div class="pst-async-banner-revealer d-none">
<aside id="bd-header-version-warning" class="d-none d-print-none" aria-label="Version warning"></aside>
</div>
<header class="bd-header navbar navbar-expand-lg bd-navbar d-print-none">
</header>
<div class="bd-container">
<div class="bd-container__inner bd-page-width">
<div class="bd-sidebar-primary bd-sidebar">
<div class="sidebar-header-items sidebar-primary__section">
</div>
<div class="sidebar-primary-items__start sidebar-primary__section">
<div class="sidebar-primary-item">
<a class="navbar-brand logo" href="intro.html">
<img src="_static/kul_logo.png" class="logo__image only-light" alt="Case 1 - Detectie van oppervlaktefouten op vlakke platen - Home"/>
<script>document.write(`<img src="_static/kul_logo.png" class="logo__image only-dark" alt="Case 1 - Detectie van oppervlaktefouten op vlakke platen - Home"/>`);</script>
</a></div>
<div class="sidebar-primary-item">
<script>
document.write(`
<button class="btn navbar-btn search-button-field search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass"></i>
<span class="search-button__default-text">Search</span>
<span class="search-button__kbd-shortcut"><kbd class="kbd-shortcut__modifier">Ctrl</kbd>+<kbd class="kbd-shortcut__modifier">K</kbd></span>
</button>
`);
</script></div>
<div class="sidebar-primary-item"><nav class="bd-links bd-docs-nav" aria-label="Main">
<div class="bd-toc-item navbar-nav active">
<ul class="nav bd-sidenav bd-sidenav__home-link">
<li class="toctree-l1">
<a class="reference internal" href="intro.html">
Case 1 - Surface Defect Detection on Flat Plates
</a>
</li>
</ul>
<ul class="current nav bd-sidenav">
<li class="toctree-l1"><a class="reference internal" href="dataset_creation.html">Dataset Creation</a></li>
<li class="toctree-l1 current active"><a class="current reference internal" href="#">Anomaly Detection with PaDiM</a></li>
<li class="toctree-l1"><a class="reference internal" href="binary_classification.html">Binary Classification</a></li>
<li class="toctree-l1"><a class="reference internal" href="multi_class_classification.html">Multi-Class Classification</a></li>
</ul>
</div>
</nav></div>
</div>
<div class="sidebar-primary-items__end sidebar-primary__section">
</div>
<div id="rtd-footer-container"></div>
</div>
<main id="main-content" class="bd-main" role="main">
<div class="sbt-scroll-pixel-helper"></div>
<div class="bd-content">
<div class="bd-article-container">
<div class="bd-header-article d-print-none">
<div class="header-article-items header-article__inner">
<div class="header-article-items__start">
<div class="header-article-item"><button class="sidebar-toggle primary-toggle btn btn-sm" title="Toggle primary sidebar" data-bs-placement="bottom" data-bs-toggle="tooltip">
<span class="fa-solid fa-bars"></span>
</button></div>
</div>
<div class="header-article-items__end">
<div class="header-article-item">
<div class="article-header-buttons">
<div class="dropdown dropdown-source-buttons">
<button class="btn dropdown-toggle" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Source repositories">
<i class="fab fa-github"></i>
</button>
<ul class="dropdown-menu">
<li><a href="https://github.com/executablebooks/jupyter-book" target="_blank"
class="btn btn-sm btn-source-repository-button dropdown-item"
title="Source repository"
data-bs-placement="left" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fab fa-github"></i>
</span>
<span class="btn__text-container">Repository</span>
</a>
</li>
<li><a href="https://github.com/executablebooks/jupyter-book/issues/new?title=Issue%20on%20page%20%2Fanomaly_detection.html&body=Your%20issue%20content%20here." target="_blank"
class="btn btn-sm btn-source-issues-button dropdown-item"
title="Open an issue"
data-bs-placement="left" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-lightbulb"></i>
</span>
<span class="btn__text-container">Open issue</span>
</a>
</li>
</ul>
</div>
<div class="dropdown dropdown-download-buttons">
<button class="btn dropdown-toggle" type="button" data-bs-toggle="dropdown" aria-expanded="false" aria-label="Download this page">
<i class="fas fa-download"></i>
</button>
<ul class="dropdown-menu">
<li><a href="_sources/anomaly_detection.ipynb" target="_blank"
class="btn btn-sm btn-download-source-button dropdown-item"
title="Download source file"
data-bs-placement="left" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-file"></i>
</span>
<span class="btn__text-container">.ipynb</span>
</a>
</li>
<li>
<button onclick="window.print()"
class="btn btn-sm btn-download-pdf-button dropdown-item"
title="Print to PDF"
data-bs-placement="left" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-file-pdf"></i>
</span>
<span class="btn__text-container">.pdf</span>
</button>
</li>
</ul>
</div>
<button onclick="toggleFullScreen()"
class="btn btn-sm btn-fullscreen-button"
title="Fullscreen mode"
data-bs-placement="bottom" data-bs-toggle="tooltip"
>
<span class="btn__icon-container">
<i class="fas fa-expand"></i>
</span>
</button>
<script>
document.write(`
<button class="btn btn-sm navbar-btn theme-switch-button" title="light/dark" aria-label="light/dark" data-bs-placement="bottom" data-bs-toggle="tooltip">
<span class="theme-switch nav-link" data-mode="light"><i class="fa-solid fa-sun fa-lg"></i></span>
<span class="theme-switch nav-link" data-mode="dark"><i class="fa-solid fa-moon fa-lg"></i></span>
<span class="theme-switch nav-link" data-mode="auto"><i class="fa-solid fa-circle-half-stroke fa-lg"></i></span>
</button>
`);
</script>
<script>
document.write(`
<button class="btn btn-sm navbar-btn search-button search-button__button" title="Search" aria-label="Search" data-bs-placement="bottom" data-bs-toggle="tooltip">
<i class="fa-solid fa-magnifying-glass fa-lg"></i>
</button>
`);
</script>
<button class="sidebar-toggle secondary-toggle btn btn-sm" title="Toggle secondary sidebar" data-bs-placement="bottom" data-bs-toggle="tooltip">
<span class="fa-solid fa-list"></span>
</button>
</div></div>
</div>
</div>
</div>
<div id="jb-print-docs-body" class="onlyprint">
<h1>Anomaly Detection with PaDiM</h1>
<!-- Table of contents -->
<div id="print-main-content">
<div id="jb-print-toc">
<div>
<h2> Contents </h2>
</div>
<nav aria-label="Page">
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#introduction">Introduction</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#imports">Imports</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#configurable-variables">Configurable variables</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-definition">Dataset definition</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#test-inference">Test inference</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-a-test-image">Load a Test Image</a></li>
</ul>
</nav>
</div>
</div>
</div>
<div id="searchbox"></div>
<article class="bd-article">
<section class="tex2jax_ignore mathjax_ignore" id="anomaly-detection-with-padim">
<h1>Anomaly Detection with PaDiM<a class="headerlink" href="#anomaly-detection-with-padim" title="Link to this heading">#</a></h1>
<section id="introduction">
<h2>Introduction<a class="headerlink" href="#introduction" title="Link to this heading">#</a></h2>
<p>In industrial settings, the occurrence of defects or anomalies is often infrequent, but their detection is crucial for maintaining quality and operational standards. Traditional supervised learning approaches require large amounts of labeled data, which is impractical to collect and annotate for rare defect types. This notebook introduces the use of the PaDiM (Patch Distribution Modeling) framework, an unsupervised anomaly detection technique that addresses this challenge effectively.</p>
<p>PaDiM is particularly valuable for scenarios where anomalies are sparse and varied, making manual dataset compilation both time-consuming and challenging. By leveraging a model trained on normal samples without the need for anomaly-labeled data, PaDiM can detect subtle deviations from the norm, highlighting potential defects. This approach not only saves significant time and effort in dataset preparation but also enhances the ability to identify unseen types of anomalies.</p>
<p>This notebook will guide through the implementation of PaDiM using the “Decospan fineerplaten dataset”. It will cover the setup, training, and evaluation stages, providing a comprehensive tool for initiating anomaly detection in environments where anomalies are rare yet critical to detect.</p>
</section>
<section id="imports">
<h2>Imports<a class="headerlink" href="#imports" title="Link to this heading">#</a></h2>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">anomalib.data.folder</span> <span class="kn">import</span> <span class="n">Folder</span>
<span class="kn">from</span> <span class="nn">anomalib.data.task_type</span> <span class="kn">import</span> <span class="n">TaskType</span>
<span class="kn">from</span> <span class="nn">anomalib.data.utils</span> <span class="kn">import</span> <span class="n">read_image</span>
<span class="kn">from</span> <span class="nn">anomalib.data</span> <span class="kn">import</span> <span class="n">InferenceDataset</span>
<span class="kn">from</span> <span class="nn">anomalib.data.mvtec</span> <span class="kn">import</span> <span class="n">MVTec</span>
<span class="kn">from</span> <span class="nn">anomalib.deploy</span> <span class="kn">import</span> <span class="n">OpenVINOInferencer</span>
<span class="kn">from</span> <span class="nn">anomalib.models</span> <span class="kn">import</span> <span class="n">Padim</span><span class="p">,</span> <span class="n">Fastflow</span>
<span class="kn">from</span> <span class="nn">anomalib.post_processing</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">NormalizationMethod</span><span class="p">,</span>
<span class="n">ThresholdMethod</span><span class="p">,</span>
<span class="n">superimpose_anomaly_map</span><span class="p">,</span>
<span class="n">Visualizer</span><span class="p">,</span>
<span class="n">VisualizationMode</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">anomalib.pre_processing.transforms</span> <span class="kn">import</span> <span class="n">Denormalize</span>
<span class="kn">from</span> <span class="nn">anomalib.utils.callbacks</span> <span class="kn">import</span> <span class="p">(</span>
<span class="n">ImageVisualizerCallback</span><span class="p">,</span>
<span class="n">MetricsConfigurationCallback</span><span class="p">,</span>
<span class="n">MetricVisualizerCallback</span><span class="p">,</span>
<span class="n">PostProcessingConfigurationCallback</span><span class="p">,</span>
<span class="n">ExportCallback</span><span class="p">,</span>
<span class="n">ExportMode</span><span class="p">,</span>
<span class="n">MinMaxNormalizationCallback</span>
<span class="p">)</span>
<span class="kn">from</span> <span class="nn">functools</span> <span class="kn">import</span> <span class="n">partial</span><span class="p">,</span> <span class="n">update_wrapper</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
<span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span><span class="p">,</span> <span class="n">ImageDraw</span><span class="p">,</span> <span class="n">ImageFont</span>
<span class="kn">from</span> <span class="nn">pytorch_lightning</span> <span class="kn">import</span> <span class="n">Trainer</span><span class="p">,</span> <span class="n">LightningModule</span>
<span class="kn">from</span> <span class="nn">pytorch_lightning.callbacks</span> <span class="kn">import</span> <span class="n">EarlyStopping</span><span class="p">,</span> <span class="n">ModelCheckpoint</span>
<span class="kn">from</span> <span class="nn">torch.optim</span> <span class="kn">import</span> <span class="n">Optimizer</span>
<span class="kn">from</span> <span class="nn">torch.optim.adam</span> <span class="kn">import</span> <span class="n">Adam</span>
<span class="kn">from</span> <span class="nn">torch.utils.data</span> <span class="kn">import</span> <span class="n">DataLoader</span>
<span class="kn">import</span> <span class="nn">os</span>
</pre></div>
</div>
</div>
</div>
</section>
<section id="configurable-variables">
<h2>Configurable variables<a class="headerlink" href="#configurable-variables" title="Link to this heading">#</a></h2>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model_name</span> <span class="o">=</span> <span class="s2">"padim"</span>
<span class="c1"># Input directory</span>
<span class="n">input_directory</span> <span class="o">=</span> <span class="s2">"dataset"</span>
<span class="n">fault_dir</span> <span class="o">=</span> <span class="s2">"fault"</span>
<span class="n">bg_dir</span> <span class="o">=</span> <span class="s2">"bg"</span>
<span class="c1"># Training settings</span>
<span class="n">split_ratio</span> <span class="o">=</span> <span class="mf">0.2</span>
<span class="n">batch_size</span> <span class="o">=</span> <span class="mi">32</span>
<span class="n">patch_size</span> <span class="o">=</span> <span class="mi">480</span>
</pre></div>
</div>
</div>
</div>
</section>
<section id="dataset-definition">
<h2>Dataset definition<a class="headerlink" href="#dataset-definition" title="Link to this heading">#</a></h2>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">datamodule</span> <span class="o">=</span> <span class="n">Folder</span><span class="p">(</span>
<span class="n">root</span><span class="o">=</span><span class="n">input_directory</span><span class="p">,</span>
<span class="n">normal_dir</span><span class="o">=</span><span class="n">bg_dir</span><span class="p">,</span>
<span class="n">abnormal_dir</span><span class="o">=</span><span class="n">fault_dir</span><span class="p">,</span>
<span class="n">normal_split_ratio</span><span class="o">=</span><span class="n">split_ratio</span><span class="p">,</span>
<span class="n">image_size</span><span class="o">=</span><span class="p">(</span><span class="n">patch_size</span><span class="p">,</span> <span class="n">patch_size</span><span class="p">),</span>
<span class="n">train_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
<span class="n">eval_batch_size</span><span class="o">=</span><span class="n">batch_size</span><span class="p">,</span>
<span class="n">task</span><span class="o">=</span><span class="n">TaskType</span><span class="o">.</span><span class="n">CLASSIFICATION</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">datamodule</span><span class="o">.</span><span class="n">setup</span><span class="p">()</span> <span class="c1"># Split the data to train/val/test/prediction sets.</span>
<span class="n">datamodule</span><span class="o">.</span><span class="n">prepare_data</span><span class="p">()</span> <span class="c1"># Create train/val/test/predic dataloaders</span>
<span class="n">i</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="nb">enumerate</span><span class="p">(</span><span class="n">datamodule</span><span class="o">.</span><span class="n">val_dataloader</span><span class="p">()))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
</section>
<section id="training">
<h2>Training<a class="headerlink" href="#training" title="Link to this heading">#</a></h2>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1">#Deine padim model</span>
<span class="n">model</span> <span class="o">=</span> <span class="n">Padim</span><span class="p">(</span>
<span class="n">input_size</span><span class="o">=</span><span class="p">(</span><span class="mi">256</span><span class="p">,</span> <span class="mi">256</span><span class="p">),</span>
<span class="n">backbone</span><span class="o">=</span><span class="s2">"resnet18"</span><span class="p">,</span>
<span class="n">layers</span><span class="o">=</span><span class="p">[</span><span class="s2">"layer1"</span><span class="p">,</span> <span class="s2">"layer2"</span><span class="p">,</span> <span class="s2">"layer3"</span><span class="p">],</span>
<span class="p">)</span>
<span class="c1"># Create output path directory</span>
<span class="n">Path</span><span class="p">(</span><span class="s1">'weights/openvino/padim'</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">callbacks</span> <span class="o">=</span> <span class="p">[</span>
<span class="n">MetricsConfigurationCallback</span><span class="p">(</span>
<span class="n">task</span><span class="o">=</span><span class="n">TaskType</span><span class="o">.</span><span class="n">CLASSIFICATION</span><span class="p">,</span>
<span class="n">image_metrics</span><span class="o">=</span><span class="p">[</span><span class="s2">"AUROC"</span><span class="p">],</span> <span class="c1"># tried other metrics, but gave errors</span>
<span class="p">),</span>
<span class="n">ModelCheckpoint</span><span class="p">(</span>
<span class="n">mode</span><span class="o">=</span><span class="s2">"max"</span><span class="p">,</span>
<span class="n">monitor</span><span class="o">=</span><span class="s2">"image_AUROC"</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">PostProcessingConfigurationCallback</span><span class="p">(</span>
<span class="n">normalization_method</span><span class="o">=</span><span class="n">NormalizationMethod</span><span class="o">.</span><span class="n">MIN_MAX</span><span class="p">,</span>
<span class="n">threshold_method</span><span class="o">=</span><span class="n">ThresholdMethod</span><span class="o">.</span><span class="n">ADAPTIVE</span><span class="p">,</span>
<span class="p">),</span>
<span class="n">MinMaxNormalizationCallback</span><span class="p">(),</span>
<span class="n">ExportCallback</span><span class="p">(</span>
<span class="n">input_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">),</span>
<span class="n">dirpath</span><span class="o">=</span><span class="nb">str</span><span class="p">(</span><span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">())</span><span class="o">+</span><span class="s2">"/"</span><span class="o">+</span><span class="n">model_name</span><span class="p">,</span>
<span class="n">filename</span><span class="o">=</span><span class="s2">"output"</span><span class="p">,</span>
<span class="n">export_mode</span><span class="o">=</span><span class="n">ExportMode</span><span class="o">.</span><span class="n">OPENVINO</span><span class="p">,</span>
<span class="p">),</span>
<span class="p">]</span>
<span class="n">trainer</span> <span class="o">=</span> <span class="n">Trainer</span><span class="p">(</span>
<span class="n">callbacks</span><span class="o">=</span><span class="n">callbacks</span><span class="p">,</span>
<span class="n">accelerator</span><span class="o">=</span><span class="s2">"auto"</span><span class="p">,</span>
<span class="n">auto_scale_batch_size</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">check_val_every_n_epoch</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">devices</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">gpus</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
<span class="n">max_epochs</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
<span class="n">num_sanity_val_steps</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>
<span class="n">val_check_interval</span><span class="o">=</span><span class="mf">1.0</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">trainer</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">model</span><span class="p">,</span> <span class="n">datamodule</span><span class="o">=</span><span class="n">datamodule</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
</section>
<section id="test-inference">
<h2>Test inference<a class="headerlink" href="#test-inference" title="Link to this heading">#</a></h2>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">directory</span> <span class="o">=</span> <span class="n">model_name</span> <span class="o">+</span> <span class="s1">'_test_output/'</span>
<span class="c1"># Create the directory if it doesn't exist and clear all files in the directory</span>
<span class="n">Path</span><span class="p">(</span><span class="n">directory</span><span class="p">)</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">parents</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">for</span> <span class="n">filename</span> <span class="ow">in</span> <span class="n">os</span><span class="o">.</span><span class="n">listdir</span><span class="p">(</span><span class="n">directory</span><span class="p">):</span>
<span class="n">file_path</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">directory</span><span class="p">,</span> <span class="n">filename</span><span class="p">)</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isfile</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span> <span class="ow">or</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">islink</span><span class="p">(</span><span class="n">file_path</span><span class="p">):</span>
<span class="n">os</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="n">file_path</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"All files in 'test_output' have been cleared."</span><span class="p">)</span>
<span class="c1"># Load the inference engine</span>
<span class="n">openvino_model_path</span> <span class="o">=</span> <span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span> <span class="o">/</span> <span class="n">model_name</span> <span class="o">/</span> <span class="s2">"weights"</span> <span class="o">/</span> <span class="s2">"openvino"</span> <span class="o">/</span> <span class="s2">"model.bin"</span>
<span class="n">metadata_path</span> <span class="o">=</span> <span class="n">Path</span><span class="o">.</span><span class="n">cwd</span><span class="p">()</span> <span class="o">/</span> <span class="n">model_name</span> <span class="o">/</span> <span class="s2">"weights"</span> <span class="o">/</span> <span class="s2">"openvino"</span> <span class="o">/</span> <span class="s2">"metadata.json"</span>
<span class="nb">print</span><span class="p">(</span><span class="n">openvino_model_path</span><span class="o">.</span><span class="n">exists</span><span class="p">(),</span> <span class="n">metadata_path</span><span class="o">.</span><span class="n">exists</span><span class="p">())</span>
<span class="n">inferencer</span> <span class="o">=</span> <span class="n">OpenVINOInferencer</span><span class="p">(</span>
<span class="n">path</span><span class="o">=</span><span class="n">openvino_model_path</span><span class="p">,</span> <span class="c1"># Path to the OpenVINO IR model.</span>
<span class="n">metadata</span><span class="o">=</span><span class="n">metadata_path</span><span class="p">,</span> <span class="c1"># Path to the metadata file.</span>
<span class="n">device</span><span class="o">=</span><span class="s2">"CPU"</span><span class="p">,</span> <span class="c1"># NVIDIA GPU not supported</span>
<span class="p">)</span>
<span class="c1"># Initialize metrics</span>
<span class="n">total_images</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">true_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">false_positives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">false_negatives</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">correct_predictions</span> <span class="o">=</span> <span class="mi">0</span>
<span class="c1"># Process images from the dataloader</span>
<span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">data</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">datamodule</span><span class="o">.</span><span class="n">test_dataloader</span><span class="p">()):</span>
<span class="k">for</span> <span class="n">path</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[</span><span class="s1">'image_path'</span><span class="p">]:</span>
<span class="n">total_images</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">true_lbl</span> <span class="o">=</span> <span class="s2">"bg"</span>
<span class="n">pred_lbl</span> <span class="o">=</span> <span class="s2">"bg"</span>
<span class="n">confidence</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">image</span> <span class="o">=</span> <span class="n">read_image</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="n">path</span><span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">inferencer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">image</span><span class="o">=</span><span class="n">image</span><span class="p">)</span>
<span class="n">confidence</span> <span class="o">=</span> <span class="n">predictions</span><span class="o">.</span><span class="n">pred_score</span>
<span class="k">if</span> <span class="s2">"bg"</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">path</span><span class="p">:</span>
<span class="n">true_lbl</span> <span class="o">=</span> <span class="s2">"faulty"</span>
<span class="k">if</span> <span class="n">predictions</span><span class="o">.</span><span class="n">pred_label</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
<span class="n">pred_lbl</span> <span class="o">=</span> <span class="s2">"faulty"</span>
<span class="k">if</span> <span class="n">true_lbl</span> <span class="o">==</span> <span class="s2">"faulty"</span><span class="p">:</span>
<span class="n">true_positives</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">false_positives</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">if</span> <span class="n">true_lbl</span> <span class="o">==</span> <span class="s2">"faulty"</span><span class="p">:</span>
<span class="n">false_negatives</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">true_lbl</span> <span class="o">==</span> <span class="n">pred_lbl</span><span class="p">:</span>
<span class="n">correct_predictions</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c1"># Calculate and print summary metrics</span>
<span class="n">accuracy</span> <span class="o">=</span> <span class="n">correct_predictions</span> <span class="o">/</span> <span class="n">total_images</span> <span class="k">if</span> <span class="n">total_images</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">recall</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_negatives</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_negatives</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="n">precision</span> <span class="o">=</span> <span class="n">true_positives</span> <span class="o">/</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span> <span class="k">if</span> <span class="p">(</span><span class="n">true_positives</span> <span class="o">+</span> <span class="n">false_positives</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> <span class="k">else</span> <span class="mi">0</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"SUMMARY for model:"</span><span class="p">,</span> <span class="n">model_name</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Total images: </span><span class="si">{</span><span class="n">total_images</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Accuracy: </span><span class="si">{</span><span class="n">accuracy</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Recall: </span><span class="si">{</span><span class="n">recall</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Precision: </span><span class="si">{</span><span class="n">precision</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>All files in 'test_output' have been cleared.
True True
SUMMARY: padim_18
Total images: 1770
Accuracy: 0.89
Recall: 0.95
Precision: 0.92
</pre></div>
</div>
</div>
</div>
</section>
<section id="load-a-test-image">
<h2>Load a Test Image<a class="headerlink" href="#load-a-test-image" title="Link to this heading">#</a></h2>
<p>Let’s read an image from the test set and perform inference using OpenVINO inferencer.</p>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">image</span> <span class="o">=</span> <span class="n">read_image</span><span class="p">(</span><span class="n">path</span><span class="o">=</span><span class="s2">"./dataset/fault/PO22-20488_10_1_annotated_3060_5040_3284_5264.png+fault.jpg"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">image</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><matplotlib.image.AxesImage at 0x7fe5da7749a0>
</pre></div>
</div>
<img alt="_images/9118f2e404733fc1de908aae2298c00ba8a14c73dfebd3bdac60bf61ea858c64.png" src="_images/9118f2e404733fc1de908aae2298c00ba8a14c73dfebd3bdac60bf61ea858c64.png" />
</div>
</div>
<div class="cell docutils container">
<div class="cell_input docutils container">
<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">inferencer</span> <span class="o">=</span> <span class="n">OpenVINOInferencer</span><span class="p">(</span>
<span class="n">path</span><span class="o">=</span><span class="n">openvino_model_path</span><span class="p">,</span>
<span class="n">metadata</span><span class="o">=</span><span class="n">metadata_path</span><span class="p">,</span>
<span class="n">device</span><span class="o">=</span><span class="s2">"CPU"</span><span class="p">,</span>
<span class="p">)</span>
<span class="n">predictions</span> <span class="o">=</span> <span class="n">inferencer</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">image</span><span class="o">=</span><span class="n">image</span><span class="p">)</span>
<span class="n">visualizer</span> <span class="o">=</span> <span class="n">Visualizer</span><span class="p">(</span><span class="n">mode</span><span class="o">=</span><span class="n">VisualizationMode</span><span class="o">.</span><span class="n">FULL</span><span class="p">,</span> <span class="n">task</span><span class="o">=</span><span class="n">TaskType</span><span class="o">.</span><span class="n">CLASSIFICATION</span><span class="p">)</span>
<span class="n">output_image</span> <span class="o">=</span> <span class="n">visualizer</span><span class="o">.</span><span class="n">visualize_image</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span>
<span class="n">Image</span><span class="o">.</span><span class="n">fromarray</span><span class="p">(</span><span class="n">output_image</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="cell_output docutils container">
<img alt="_images/dcaaf2f3f1dacffef56cf27d6a0f4822f81b7f711274246c9fddb936ed294aa3.png" src="_images/dcaaf2f3f1dacffef56cf27d6a0f4822f81b7f711274246c9fddb936ed294aa3.png" />
</div>
</div>
</section>
</section>
<script type="text/x-thebe-config">
{
requestKernel: true,
binderOptions: {
repo: "binder-examples/jupyter-stacks-datascience",
ref: "master",
},
codeMirrorConfig: {
theme: "abcdef",
mode: "python"
},
kernelOptions: {
name: "python3",
path: "./."
},
predefinedOutput: true
}
</script>
<script>kernelName = 'python3'</script>
</article>
<footer class="prev-next-footer d-print-none">
<div class="prev-next-area">
<a class="left-prev"
href="dataset_creation.html"
title="previous page">
<i class="fa-solid fa-angle-left"></i>
<div class="prev-next-info">
<p class="prev-next-subtitle">previous</p>
<p class="prev-next-title">Dataset Creation</p>
</div>
</a>
<a class="right-next"
href="binary_classification.html"
title="next page">
<div class="prev-next-info">
<p class="prev-next-subtitle">next</p>
<p class="prev-next-title">Binary Classification</p>
</div>
<i class="fa-solid fa-angle-right"></i>
</a>
</div>
</footer>
</div>
<div class="bd-sidebar-secondary bd-toc"><div class="sidebar-secondary-items sidebar-secondary__inner">
<div class="sidebar-secondary-item">
<div class="page-toc tocsection onthispage">
<i class="fa-solid fa-list"></i> Contents
</div>
<nav class="bd-toc-nav page-toc">
<ul class="visible nav section-nav flex-column">
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#introduction">Introduction</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#imports">Imports</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#configurable-variables">Configurable variables</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#dataset-definition">Dataset definition</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#training">Training</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#test-inference">Test inference</a></li>
<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#load-a-test-image">Load a Test Image</a></li>
</ul>
</nav></div>
</div></div>
</div>
<footer class="bd-footer-content">
<div class="bd-footer-content__inner container">
<div class="footer-item">
<p class="component-author">
By Karel Debedts
</p>
</div>
<div class="footer-item">
<p class="copyright">
© Copyright 2023.
<br/>
</p>
</div>
<div class="footer-item">
</div>
<div class="footer-item">
</div>
</div>
</footer>
</main>
</div>
</div>
<!-- Scripts loaded after <body> so the DOM is not blocked -->
<script src="_static/scripts/bootstrap.js?digest=3ee479438cf8b5e0d341"></script>
<script src="_static/scripts/pydata-sphinx-theme.js?digest=3ee479438cf8b5e0d341"></script>
<footer class="bd-footer">
</footer>
</body>
</html>